Ali Behrangi

Interests

Teaching

Remote sensing of precipitation and water cycle , Hydrology,

Research

• Multi-sensor multi-spectral remote sensing of precipitation• High latitude/cold region/mountainous rain and snow retrievals and analysis• Weather and climate extremes (extreme precipitation, drought, heatwaves) and societal impact• Global water and energy budget analysis• Hydrologic/watershed modeling and optimization• Developing precipitation products for hydrological applications.• Using advanced satellite and in-situ data to improve quantification of hydrologic variables • Representation of precipitation in climate models• Land-atmosphere interaction • Using GRACE for hydrometeorology• Precipitation and ground water recharge

Courses

Scholarly Contributions

Chapters

• Ye, H., Yang, D., Behrangi, A., Stuefer, S. L., Pan, X., Mekis, E., Dibike, Y., & Walsh, J. E. (2021). Precipitation Characteristics and Changes. In Arctic Hydrology, Permafrost and Ecosystems(pp 25--59). Springer, Cham.
• Behrangi, A. (2020). Improving High-Latitude and Cold Region Precipitation Analysis. In Satellite Precipitation Measurement(pp 881--895). Springer, Cham.
• Behrangi, A. (2020). Improving high-latitude and cold region precipitation analysis. In Satellite precipitation measurement. Springer. doi:10.1007/978-3-030-35798-6_21
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  Accurate estimation of precipitation is important for science and application. In high latitudes precipitation estimation is difficult due to several challenges in remote sensing of precipitation and sparseness of in situ observations. Furthermore, in situ observations can also have large errors, especially for measurement of snowfall that occurs frequently in high latitudes. Here, we show how CloudSat and the Gravity Recovery and Climate Experiment (GRACE) can provide additional information to refine our quantification of high latitude precipitation. We show this through case studies over ocean, Eurasia, Tibetan Plateau, and arctic basins. The results suggest that combination of CloudSat and GRACE can provide valuable information on quantifying snowfall accumulation that can also be used to assess gauge undercatch correction methods. Together with ongoing efforts under GPM to advance Level 2 precipitation retrievals and their combination, great opportunities exist to advance precipitation retrieval in high latitudes.
• Hsu, K., Behrangi, A., Imam, B., & Sorooshian, S. (2010). Extreme precipitation estimation using satellite-based PERSIANN-CCS algorithm. In Satellite Rainfall Applications for Surface Hydrology(pp 49--67). Springer, Dordrecht.

Journals/Publications

• Dong, X., Das, A., Xi, B., Zheng, X., Behrangi, A., Marcovecchio, A. R., & Girone, D. J. (2025). Quantifying the Differences in Southern Ocean Clouds Observed by Radar and Lidar From Three Platforms. Geophysical Research Letters, 52(Issue 9). doi:10.1029/2024gl112079
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  A synergistic analysis of the radar-only and combined radar-lidar observations across the three platforms was conducted. To align with well-calibrated CloudSat cloud profiling radar (CPR) (and HCR) reflectivity measurements, a constant 4.5 dB offset was applied to all M-WACR reflectivitives during the MARCUS. This brings M-WACR data into better agreement with both HCR and CPR reflectivity measurements and facilitates a more reliable cloud fraction (CF) comparison. The total CFs (CFTs) derived from the three radars show excellent agreement. All three radars detect large drizzle drops, but M-WACR and HCR excel at detecting smaller cloud droplets that are often missed by CPR. The underestimated CFs by CPR are due to increased attenuation of CPR measurements below 3 km, and the combined effects of attenuation and surface clutter below 1 km. Combining radar and lidar observations enhanced cloud detection by 20%–60%. The results from this study provide new insights for designing future cloud radar systems.
• Dong, X., Das, A., Xi, B., Zheng, X., Behrangi, A., Marcovecchio, A. R., & Girone, D. J. (2025). Quantifying the differences in Southern Ocean clouds observed by radar and lidar from three platforms. Geophysical Research Letters, 52(9), e2024GL112079.
• Elsaesser, G. S., Lier-Walqui, M., Yang, Q., Kelley, M., Ackerman, A. S., Fridlind, A. M., Cesana, G. V., Schmidt, G. A., Wu, J., Behrangi, A., & others, . (2025). Using machine learning to generate a GISS ModelE calibrated physics ensemble (CPE). Journal of Advances in Modeling Earth Systems, 17(4), e2024MS004713.
• Elsaesser, G. S., van Lier-Walqui, M., Yang, Q., Kelley, M., Ackerman, A. S., Fridlind, A. M., Cesana, G. V., Schmidt, G. A., Wu, J., Behrangi, A., Camargo, S. J., De, B., Inoue, K., Leitmann-Niimi, N. M., & Strong, J. D. (2025). Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE). Journal of Advances in Modeling Earth Systems, 17(Issue 4). doi:10.1029/2024ms004713
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  A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.
• Farmani, M. A., Behrangi, A., Gupta, A., Tavakoly, A., Geheran, M., & Niu, G. (2025). Do land models miss key soil hydrological processes controlling soil moisture memory?. Hydrology and Earth System Sciences, 29(2), 547--566.
• Farmani, M. A., Behrangi, A., Gupta, A., Tavakoly, A., Geheran, M., & Niu, G. Y. (2025). Do land models miss key soil hydrological processes controlling soil moisture memory?. Hydrology and Earth System Sciences, 29(Issue 2). doi:10.5194/hess-29-547-2025
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  Soil moisture memory (SMM), which refers to how long a perturbation in soil moisture (SM) can last, is critical for understanding climatic, hydrological, and ecosystem interactions. Most land surface models (LSMs) tend to overestimate surface soil moisture and its persistency (or SMM), sustaining spuriously large soil surface evaporation during dry-down periods. We attempt to answer a question: do LSMs miss or misrepresent key hydrological processes controlling SMM? We use a version of Noah-MP with advanced hydrology that explicitly represents preferential flow and surface ponding and provides optional schemes of soil hydraulics. We test the effects of these processes, which are generally missed by most LSMs in SMM. We compare SMMs computed from various Noah-MP configurations against that derived from the Soil Moisture Active Passive (SMAP) L3 soil moisture and in situ measurements from the International Soil Moisture Network (ISMN) from the years 2015 to 2019 over the contiguous United States (CONUS). The results suggest that (1) soil hydraulics plays a dominant role and the Van Genuchten hydraulic scheme reduces the overestimation of the long-term surface SMM produced by the Brooks–Corey scheme, which is commonly used in LSMs; (2) explicitly representing surface ponding enhances SMM for both the surface layer and the root zone; and (3) representing preferential flow improves the overall representation of soil moisture dynamics. The combination of these missing schemes can significantly improve the long-term memory overestimation and short-term memory underestimation issues in LSMs. We suggest that LSMs for use in seasonal-to-subseasonal climate prediction should, at least, adopt the Van Genuchten hydraulic scheme.
• Farmani, M. A., Tavakoly, A., Behrangi, A., Qiu, Y., Gupta, A., Jawad, M., Sohi, H. Y., Zhang, X., Geheran, M., & Niu, G. (2025). Improving streamflow predictions in the arid Southwestern United States through understanding of baseflow generation mechanisms. Water Resources Research, 61(10), e2024WR039479.
• Farmani, M. A., Tavakoly, A., Behrangi, A., Qiu, Y., Gupta, A., Jawad, M., Sohi, H. Y., Zhang, X., Geheran, M., & Niu, G. Y. (2025). Improving Streamflow Predictions in the Arid Southwestern United States Through Understanding of Baseflow Generation Mechanisms. Water Resources Research, 61(Issue 10). doi:10.1029/2024wr039479
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  Understanding the factors controlling baseflow (groundwater discharge) is critical for improving streamflow predictions in the arid southwestern United States. We used an enhanced version of the Noah-MP land surface model with advanced hydrological process options and the Routing Application for Parallel computation of Discharge (RAPID) to examine the impacts of process representation, soil hydraulic parameters, and precipitation data sets on baseflow production and streamflow skill. Model experiments combined multiple configurations of hydrological processes, soil parameters, and three gridded precipitation products: NLDAS-2, Integrated Multi-satellite Retrievals for GPM Final, and NOAA AORC. RAPID was used to route Noah-MP-simulated runoff and generate daily streamflow at 390 U.S. Geological Survey (USGS) gauges. The modeled baseflow index (BFI) was compared with USGS-derived BFI. Results show that (a) soil water retention curve model plays a dominant role, with the Van-Genuchten hydraulic scheme reducing the overestimated BFI produced by the Brooks-Corey, (b) hydraulic parameters (Van-Genuchten parameters and hydraulic conductivity) strongly affect streamflow prediction, a machine learning-based Van-Genuchten parameters captures the USGS BFI, showing a better performance than the optimized National Water Model (NWM) by a median Kling-Gupta Efficiency of 21%, and (c) incorporating a ponding depth threshold into the land surface models that increases infiltration is preferred. Overall, models with more physically realistic hydrologic representations show a better performance in modeling BFI and thus a better skill in streamflow predictions than the optimized NWM in the dry southwestern river basins. These findings can guide future studies in selecting reliable schemes and data sets (before calibration) to achieve better streamflow predictions as well as water resource projections.
• Jawad, M., Behrangi, A., Farmani, M. A., Qiu, Y., Sohi, H. Y., Gupta, A., & Niu, G. (2025). Improved evapotranspiration estimation using the Penman-Monteith equation with a deep learning (DNN) model over the dry southwestern US: Comparison with ECOSTRESS, MODIS, and OpenET. Journal of Hydrology, 133460.
• Jawad, M., Behrangi, A., Farmani, M. A., Qiu, Y., Sohi, H. Y., Gupta, A., & Niu, G. Y. (2025). Improved evapotranspiration estimation using the Penman-Monteith equation with a deep learning (DNN) model over the dry southwestern US: Comparison with ECOSTRESS, MODIS, and OpenET. Journal of Hydrology, 660(Issue). doi:10.1016/j.jhydrol.2025.133460
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  As one of the major components of the water cycle, accurate estimation of evapotranspiration (ET) at regional scales is challenging, especially over drylands due to the strong soil water constraint. Recently developed remote sensing-based ET products, especially the widely used Priestley Taylor–Jet Propulsion Laboratory (PT-JPL) product (e.g., ECO3ETPTJPL), tend to overestimate ET in the US southwest drylands. In contrast, the Moderate Resolution Imaging Spectroradiometer (MODIS) based product (MOD16A2) underestimates ET. This study presents a hybrid approach that integrates physical modeling and machine learning to estimate daily actual ET at 500 m over the state of Arizona. We develop an efficient Penman-Monteith (PM) based model to compute three ET components including canopy interception loss, direct evaporation, and transpiration from three buckets of water including the canopy-intercepted, surface soil, and subsurface soil water, respectively, based on energy balance. The PM model generated ET is then post-processed with a sequential Deep Neural Network (DNN) to improve ET estimates. PM with the Deep Learning (PMDL) model is trained and tested using independent sets of eddy covariance measurements at 114 CONUS-wide AmeriFlux sites with various land cover types and a range of aridity index. We then applied the trained PMDL model to the state of Arizona using remotely sensed surface data from MODIS, near-surface atmospheric data from the Analysis Of Record for Calibration (AORC), and surface albedo from ERA5-Land. The model shows significantly better results than other remote sensing-based products, e.g., MOD16A2, ECO3ETPTJPL, ECO3ETALEXI, and OpenET with reference to the AmeriFlux observations. It shows reasonably improved performance metrics (KGEss > ∼0.78 and R2 > ∼0.85) at daily and monthly scales over various sites and the state of Arizona.
• Kumah, K. K., Zandi, O., & Behrangi, A. (2025). Retrospective Mapping of Global Snow and Ice Cover Beyond the Satellite Observational Era. Earth and Space Science, 12(Issue 5). doi:10.1029/2024ea004171
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  Monitoring Earth's snow and ice cover is essential for diverse applications, including climate studies, hydrological forecasting, and precipitation mapping. This study develops and evaluates methodologies to extend the Global Multisensor Automated Snow and Ice Mapping System (GMASI) records prior to its July 1987 inception, reconstructing high-resolution global snow and ice cover data. Using ERA5 reanalysis variables, three machine learning (ML) approaches—ML-E (ML with ERA5 predictors only), ML-EC (ML with ERA5 and Climatology-based predictors), and ML-ECC (ML with ERA5 predictors, Climatology-based predictors, and additional Consistency Checks)—were tested alongside climatological and fractional cover-based methods. Validation against GMASI (1988–1991) shows that ML-EC and ML-ECC achieve superior alignment, with the latter offering marginal accuracy gains. Both methods demonstrated stable daily estimates, with mean percentage biases for snow and ice cover remaining below 3% during validation. Their high accuracy is further reflected in probabilities of detection (POD) exceeding 97% across key surface types. Across all methods, there was a general tendency to underestimate snow-free areas and overestimate snow-covered regions in the Northern Hemisphere, while classification challenges in the Southern Hemisphere were more pronounced over snow-free land and Antarctic sea ice. The ML-EC approach was subsequently applied to extend the GMASI record back to 1980, capturing seasonal and interannual variability consistent with GMASI-era trends. These results underscore the potential of ML techniques to extend snow and ice cover records as far back as the beginning of the reanalysis era (1940–present), providing invaluable insights for climate analysis and operational applications.
• Kumah, K. K., Zandi, O., & Behrangi, A. (2025). Retrospective mapping of global snow and ice cover beyond the satellite observational era. Earth and Space Science, 12(5), e2024EA004171.
• Lambert, F. H., Allan, R. P., Behrangi, A., Byrne, M. P., Ceppi, P., Chadwick, R., Durack, P. J., Fosser, G., Fowler, H. J., Greve, P., & others, . (2025). Changes in the regional water cycle and their impact on societies. Wiley Interdisciplinary Reviews: Climate Change, 16(2), e70005.
• Noorbeh, M., Gharari, S., Salehi, H., Behrangi, A., & Massah Bavani, A. R. (2025). Impact of precipitation on parameter sensitivity and identifiability in the variable infiltration capacity (VIC) model. Hydrological Sciences Journal. doi:10.1080/02626667.2025.2569674
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  This study aims to explore the interaction between selected gridded precipitation products and parameters of the variable infiltration capacity (VIC) model within the Gharesu basin in Iran. The evaluation is done in two phases: (1) exploration of the performance of various precipitation products (i.e. three satellite gauges, two reanalyses, and three gauge-based datasets), and comparison to station measurements; and (2) the interaction of behavioural VIC parameter sets with these precipitation products. Evaluation of best-performing and non-dominant solutions indicates that for most VIC parameters, no unique value and range can be specified for the precipitation products. This reflects the important fact that precipitation, which is known to carry significant information, is decisive in inferred values of parameters that are perceived to have physical meaning (e.g. soil characteristics). In summary, our research contributes to a deeper understanding of the complex relationship between precipitation products and parameters of hydrological models.
• Noorbeh, M., Gharari, S., Salehi, H., Behrangi, A., & Massah, B. (2025). Impact of precipitation on parameter sensitivity and identifiability in the variable infiltration capacity (VIC) model. Hydrological Sciences Journal, 70(16), 3033--3047.
• Qiu, Y., Famiglietti, J. S., Behrangi, A., Farmani, M. A., Yousefi Sohi, H., Gupta, A., Hung, F., Abdelmohsen, K., & Niu, G. Y. (2025). The Strong Impact of Precipitation Intensity on Groundwater Recharge and Terrestrial Water Storage Change in Arizona, a Typical Dryland. Geophysical Research Letters, 52(Issue 14). doi:10.1029/2025gl114747
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  This study demonstrates the critical role of precipitation intensity in groundwater recharge generation and terrestrial water storage (TWS) change. We conducted two experiments driven by precipitation products with close annual totals but distinct intensity in Arizona, using the Noah-MP model with advanced soil hydrology. The experiment with higher precipitation intensity (EXPHI) produces an annual groundwater recharge of 6.91 mm/year in Arizona during 2001–2020, ∼15 times that of the experiment with lower precipitation intensity (EXPLI). Correspondingly, EXPLI produces a declining groundwater storage (GWS) trend of (Formula presented.) 0.51 mm/month, nearly triple that of EXPHI. GWS change dominates the TWS trend. EXPLI shows a declining TWS trend of (Formula presented.) 0.57 mm/month, nearly twice that of EXPHI. Higher precipitation intensity reduces evapotranspiration and enhances infiltration and percolation, allowing more precipitation to recharge groundwater. This study underscores the need to ensure the accuracy of precipitation intensity in hydrological modeling for reliable water resources assessment and projection.
• Qiu, Y., Famiglietti, J. S., Behrangi, A., Farmani, M. A., Yousefi, S. H., Gupta, A., Hung, F., Abdelmohsen, K., & Niu, G. (2025). The strong impact of precipitation intensity on groundwater recharge and terrestrial water storage change in Arizona, a typical dryland. Geophysical Research Letters, 52(14), e2025GL114747.
• Rahimpour, M., Rahimzadegan, M., Nosratpour, R., Homayouni, S., & Behrangi, A. (2025). A Novel Machine Learning-Based Clustering-Merging Method for Improving Extreme Precipitation Estimation. Advances in Atmospheric Sciences, 42(8), 1693--1714.
• Rahimpour, M., Rahimzadegan, M., Nosratpour, R., Homayouni, S., & Behrangi, A. (2025). A Novel Machine Learning-Based Clustering-Merging Method for Improving Extreme Precipitation Estimation. Advances in Atmospheric Sciences, 42(Issue). doi:10.1007/s00376-024-4315-3
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  Satellite Precipitation Products (SPPs) face challenges in detecting Extreme Precipitation Events (EPEs). Hence, the primary objective of this research is to introduce a novel framework termed Machine-Learning Clustering-Merging Algorithms (ML-CMAs) to evaluate EPEs using SPPs and Auxiliary Data (AD). Daily precipitation measurements were utilized for training and evaluating EPE estimates over Iran, which is comprised of arid and semi-arid regions. Statistical analysis and evaluation of five SPPs demonstrated that during EPE occurrences, all products face challenges in precipitation estimation, and using these products individually is not recommended. Among the SPPs, Multi-Source Weighted-Ensemble Precipitation (MSWEP) performed best for heavy (>20 mm d −1) and extreme (>40 mm d −1) precipitation events, followed by Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Dynamic Infrared-Rain Rate (PERSIANN-PDIR). The findings indicate that all proposed methods based on ML-CMAs could estimate precipitation rates more accurately than SPPs and improve statistical indices. The seasonal assessment and spatial analysis of statistical metrics of the overall daily precipitation results for all periods and climates revealed that all methods based on ML-CMAs performed well in all seasons and at nearly all measurement stations. Using unsupervised K-means++ classification for clustering EPEs and Deep Neural Network (DNN) and Convolutional Neural Network (CNN) methods for merging the ML-CMAs reduced the error rate of SPPs in EPE estimation by approximately 50%. Therefore, incorporating ML-CMAs along with PWV as AD can significantly improve the performance of SPPs in evaluating EPEs over the study region.
• Sohi, H. Y., Farmani, M. A., & Behrangi, A. (2025). How Do IMERG V07, IMERG V06, and ERA5 Precipitation Products Perform over Snow-Ice-Free and Snow-Ice-Covered Surfaces at a Range of Near-Surface Temperatures?. JOURNAL OF HYDROMETEOROLOGY, 26(7), 837--855.
• Sohi, H. Y., Farmani, M. A., & Behrangi, A. (2025). How Do IMERG V07, IMERG V06, and ERA5 Precipitation Products Perform over Snow–Ice-Free and Snow–Ice-Covered Surfaces at a Range of Near-Surface Temperatures?. Journal of Hydrometeorology, 26(Issue 7). doi:10.1175/jhm-d-24-0110.1
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  Accurate precipitation estimation is essential for hydrological research and applications. This study assesses the performance of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM), version 7 (IMERG V07), IMERG V06, and the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) over snow–ice-covered and snow–ice-free surfaces, using 3 years (2018–20) of Multi-Radar Multi-Sensor (MRMS) system gauge-adjusted data as a reference. Mountainous regions were largely excluded from the analysis due to known uncertainties in precipitation estimates over complex terrain. Besides the IMERG final products, the study com-pares precipitation estimates from the infrared (IR) and passive microwave (PMW) components of IMERG V07 under different surface and environmental conditions. This is particularly relevant as both calibrated IR and PMW products, which rely on the Combined Radar–Radiometer Algorithm (CORRA) for calibration, face significant limitations over cold regions, especially over snow–ice-covered surfaces, complicating the choice of products for integration in the merged products like IMERG. We found that IMERG V07 offers notable improvements over IMERG V06 and generally outperforms ERA5 over snow–ice-free regions, demonstrating enhanced accuracy in precipitation intensity and spatial coverage. Conversely, ERA5 outperforms IMERG V07 over snow–ice surfaces, highlighting remaining challenges in satellite-based precipitation products over cold regions. An evaluation of PMW precipitation products indicates that while they generally perform better than IR precipitation products in warmer conditions, IR precipitation is still invaluable in cold regions with snow–ice cover. Among the PMW products and over snow–ice surfaces, Advanced Microwave Scanning Radiome-ter, version 2 (AMSR2), underperforms other PMW precipitation products for most statistical metrics, while GPM Microwave Imager (GMI), Special Sensor Microwave Imager/Sounder (SSMIS), and Microwave Humidity Sounder (MHS) products perform relatively better than others. The results emphasize the need for improving spaceborne sensors and algorithms to improve their accuracy across diverse environmental conditions, especially over cold regions in the presence of snow or ice on the surface.
• Song, Y., & Behrangi, A. (2025). Insights on Satellite and Reanalysis Snowfall Estimates over Arctic Sea Ice Using Combined ICESat-2 and CryoSat-2 Observations. Journal of Hydrometeorology, 26(9), 1333--1349.
• Song, Y., & Behrangi, A. (2025). Insights on Satellite and Reanalysis Snowfall Estimates over Arctic Sea Ice Using Combined ICESat-2 and CryoSat-2 Observations. Journal of Hydrometeorology, 26(Issue 9). doi:10.1175/jhm-d-25-0016.1
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  Snowfall data from ERA5, Global Precipitation Climatology Project, version 3.2 (GPCP V3.2), Integrated Multi-satellitE Retrievals for GPM, version 7 (IMERG V07), and IMERG V06 were assessed over Arctic sea ice by reconstructing snow depth from these products and comparing the results to reference snow depth values derived from combined ICESat-2 and CryoSat-2 observations across three snow accumulation seasons (2018–21). Spatiotemporal analyses suggest that ERA5, GPCP V3.2, and IMERG V06 exhibit pattern agreement with the reference snow depth over the central Arctic, with correlations up to 0.84. IMERG V06 significantly underestimates snow depth and shows relatively low spatiotemporal variability. We found that IMERG V07 has lower skill in capturing the spatial distribution of snow depth compared to IMERG V06 at the expense of reducing overall bias and increasing spatial and intermonth variability. This work marks an initial yet significant step toward a more integrated assessment of snowfall and snow depth estimates over sea ice, where in situ observations are largely absent.
• Yousefi, S. H., Farmani, M. A., & Behrangi, A. (2025). How Do IMERG V07, IMERG V06, and ERA5 Precipitation Products Perform over Snow--Ice-Free and Snow--Ice-Covered Surfaces at a Range of Near-Surface Temperatures?. Journal of Hydrometeorology, 26(7), 837--855.
• Behrangi, A., Song, Y., Huffman, G. J., & Adler, R. F. (2024). Comparative analysis of the latest global oceanic precipitation estimates from GPM V07 and GPCP V3. 2 products. Journal of Hydrometeorology, 25(2), 293--309.
• Broxton, P., Ehsani, M. R., & Behrangi, A. (2024). Improving mountain snowpack estimation using machine learning with Sentinel-1, the Airborne Snow Observatory, and University of Arizona snowpack data. Earth and Space Science, 11(3), e2023EA002964.
• Ehsani, M. R., Behrangi, A., Castro, C. L., & Hoopes, C. A. (2021). Improving Mountain Snowfall Forecasts in the Southwestern US Using Machine Learning Methods. Weather Analysis and Forecasting.
• Ehsani, M. R., Behrangi, A., Rom'an-Palacios, C., Huffman, G. J., & Adler, R. F. (2024). Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica. Remote Sensing of Environment, 308, 114199.
• Ehsani, M. R., Zarei, A., Gupta, H. V., Barnard, K., & Behrangi, A. (2021). Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG. IEEE Transactions on Geoscience and Remote Sensing. doi:http://arxiv.org/abs/2108.06868
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  Ehsani MR, A Zarei, HV Gupta, K Barnard and A Behrangi (2021), Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG, (http://arxiv.org/abs/2108.06868) IEEE Transactions on Geoscience and Remote Sensing.
• Elsaesser, G., Walqui, M. v., Yang, Q., Kelley, M., Ackerman, A. S., Fridlind, A., Cesana, G., Schmidt, G. A., Wu, J., Behrangi, A., & others, . (2024). Using machine learning to generate a giss modele calibrated physics ensemble (cpe). Authorea Preprints.
• Farmani, M. A., Behrangi, A., Gupta, A., Tavakoly, A., Geheran, M., & Niu, G. (2024). What Are the Key Soil Hydrological Processes to Control Soil Moisture Memory?. EGUsphere, 2024, 1--28.
• Farmani, M. A., Tavakoly, A. A., Behrangi, A., Qiu, Y., Gupta, A., Jawad, M., Sohi, H. Y., Zhang, X., Geheran, M. P., & Niu, G. (2024). Improving Streamflow Predictions in the Arid Southwestern United States Through Understanding of Baseflow Generation Mechanisms. Authorea Preprints.
• Hilario, M., Arellano, A. F., Behrangi, A., Crosbie, E. C., DiGangi, J. P., Diskin, G. S., Shook, M. A., Ziemba, L. D., & Sorooshian, A. (2024). Assessing potential indicators of aerosol wet scavenging during long-range transport. Atmospheric Measurement Techniques, 17(1), 37--55.
• Hilario, M., Arellano, A., Behrangi, A., Crosbie, E., Digangi, J., Diskin, G., Shook, M., Ziemba, L., & Sorooshian, A. (2024). Assessing potential indicators of aerosol wet scavenging during long-range transport. Atmospheric Measurement Techniques, 17(1). doi:10.5194/amt-17-37-2024
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  As one of the dominant sinks of aerosol particles, wet scavenging greatly influences aerosol lifetime and interactions with clouds, precipitation, and radiation. However, wet scavenging remains highly uncertain in models, hindering accurate predictions of aerosol spatiotemporal distributions and downstream interactions. In this study, we present a flexible, computationally inexpensive method to identify meteorological variables relevant for estimating wet scavenging using a combination of aircraft, satellite, and reanalysis data augmented by trajectory modeling to account for air mass history. We assess the capabilities of an array of meteorological variables to predict the transport efficiency of black carbon (TEBC) using a combination of nonlinear regression, curve fitting, and k-fold cross-validation. We find that accumulated precipitation along trajectories (APT) - treated as a wet scavenging indicator across multiple studies - does poorly when predicting TEBC. Among different precipitation characteristics (amount, frequency, intensity), precipitation intensity was the most effective at estimating TEBC but required longer trajectories (>48h) and including only intensely precipitating grid cells. This points to the contribution of intense precipitation to aerosol scavenging and the importance of accounting for air mass history. Predictors that were most able to predict TEBC were related to the distribution of relative humidity (RH) or the frequency of humid conditions along trajectories, suggesting that RH is a more robust way to estimate TEBC than APT. We recommend the following alternatives to APT when estimating aerosol scavenging: (1) the 90th percentile of RH along trajectories, (2) the fraction of hours along trajectories with either water vapor mixing ratios>15gkg-1 or RH>95%, and (3) precipitation intensity along trajectories at least 48h along and filtered for grid cells with precipitation>0.2mmh-1. Future scavenging parameterizations should consider these meteorological variables along air mass histories. This method can be repeated for different regions to identify region-specific factors influencing wet scavenging.
• Kumah, K. K., Zandi, O., & Behrangi, A. (2024). On the Retrospective Mapping of Global Snow and Ice Cover Beyond the Satellite Observational Era. AGU24.
• Mayer, M., Kato, S., Bosilovich, M. G., Bechtold, P., Mayer, J., Schröder, M., Behrangi, A., Wild, M., Kobayashi, S., Li, Z., & L’Ecuyer, T. (2024). Assessment of Atmospheric and Surface Energy Budgets Using Observation-Based Data Products. EGUsphere. doi:10.1007/s10712-024-09827-x
• Mayer, M., Kato, S., Bosilovich, M., Bechtold, P., Mayer, J., Behrangi, A., Wild, M., Kobayashi, S., Li, Z., L’Ecuyer, T., & Schröder, M. (2024). Assessment of Atmospheric and Surface Energy Budgets Using Observation-Based Data Products. Surveys in Geophysics. doi:10.1007/s10712-024-09827-x
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  Accurate diagnosis of regional atmospheric and surface energy budgets is critical for understanding the spatial distribution of heat uptake associated with the Earth’s energy imbalance (EEI). This contribution discusses frameworks and methods for consistent evaluation of key quantities of those budgets using observationally constrained data sets. It thereby touches upon assumptions made in data products which have implications for these evaluations. We evaluate 2001–2020 average regional total (TE) and dry static energy (DSE) budgets using satellite-based and reanalysis data. For the first time, a consistent framework is applied to the ensemble of the 5th generation European Reanalysis (ERA5), version 2 of modern-era retrospective analysis for research and applications (MERRA-2), and the Japanese 55-year Reanalysis (JRA55). Uncertainties of the computed budgets are assessed through inter-product spread and evaluation of physical constraints. Furthermore, we use the TE budget to infer fields of net surface energy flux. Results indicate biases < 1 W/m2 on the global, < 5 W/m2 on the continental, and ~ 15 W/m2 on the regional scale. Inferred net surface energy fluxes exhibit reduced large-scale biases compared to surface flux data based on remote sensing and models. We use the DSE budget to infer atmospheric diabatic heating from condensational processes. Comparison to observation-based precipitation data indicates larger uncertainties (10–15 Wm−2 globally) in the DSE budget compared to the TE budget, which is reflected by increased spread in reanalysis-based fields. Continued validation efforts of atmospheric energy budgets are needed to document progress in new and upcoming observational products, and to understand their limitations when performing EEI research.
• Mayer, M., Kato, S., Bosilovich, M., Bechtold, P., Mayer, J., Schr"oder, M., Behrangi, A., Wild, M., Kobayashi, S., Li, Z., & others, . (2024). Assessment of atmospheric and surface energy budgets using observation-based data products. Surveys in Geophysics, 1--28.
• Niu, G., Fang, Y., Neto, A., Guo, B. o., Zhang, X., Farmani, M. A., Behrangi, A., & Zeng, X. (2024). Representing Preferential Flow through Variably-Saturated Soils with Surface Ponding in a Large-Scale Land Surface Model over the Conterminous US. Authorea Preprints.
• Rahimi, R., Ravirathinam, P., Ebtehaj, A., Behrangi, A., Tan, J., & Kumar, V. (2024). Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture. Journal of Hydrometeorology.
• Sohi, H. Y., Farmani, M. A., & Behrangi, A. (2024). How do IMERG V07, IMERG V06, and ERA5 Precipitation Products Perform Over Snow-ice-free and Snow-ice-covered Surfaces at a Range of Near Surface Temperatures?. Authorea Preprints.
• Song, Y., Behrangi, A., Yong, B., Wang, G., Han, D., & Zhang, Y. (2024). Intercomparisons of the Latest Precipitation Estimates from Satellite, Reanalysis, and Merged Products over Alaska. Journal of Hydrometeorology, 25(10), 1461--1479.
• Song, Y., Behrangi, A., Yong, B., Wang, G., Han, D., & Zhang, Y. (2024). Intercomparisons of the Latest Precipitation Estimates from Satellite, Reanalysis, and Merged Products over Alaska. Journal of Hydrometeorology, 25(Issue 10). doi:10.1175/jhm-d-24-0008.1
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  This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite– gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products.
• Zeraati, M., Farahmand, A., Asghari, K., & Behrangi, A. (2024). Developing a multivariate agro-meteorological index to improve capturing onset and persistence of droughts utilizing vapor pressure deficit and soil moisture. Earth and Space Science, 11(6), e2023EA003273.
• Agnihotri, J., Behrangi, A., Tavakoly, A., Geheran, M., Farmani, M. A., & Niu, G. (2023). Higher Frozen Soil Permeability Represented in a Hydrological Model Improves Spring Streamflow Prediction From River Basin to Continental Scales. Water Resources Research, 59(4), e2022WR033075.
• Behrangi, A., Song, Y., Huffman, G. J., & Adler, R. F. (2023). Comparative Analysis of the Latest Global Oceanic Precipitation Estimates from GPM V07 and GPCP V3. 2 Products. Journal of Hydrometeorology.
• Faramarzzadeh, M., Ehsani, M. R., Akbari, M., Rahimi, R., Moghaddam, M., Behrangi, A., Kl\"ove, B., Haghighi, A. T., & Oussalah, M. (2023). Application of machine learning and remote sensing for gap-filling daily precipitation data of a sparsely gauged basin in East Africa. Environmental Processes, 10(1), 8.
• Faramarzzadeh, M., Ehsani, M. R., Akbari, M., Rahimi, R., Moghaddam, M., Behrangi, A., Klöve, B., Haghighi, A. T., & Oussalah, M. (2023). Application of Machine Learning and Remote Sensing for Gap-filling Daily Precipitation Data of a Sparsely Gauged Basin in East Africa. Environmental Processes, 10(Issue 1). doi:10.1007/s40710-023-00625-y
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  Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3 mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9 mm), and highest correlation coefficient (0.53).
• Hilario, M., Arellano, A. F., Behrangi, A., Crosbie, E. C., DiGangi, J. P., Diskin, G. S., Shook, M. A., Ziemba, L. D., & Sorooshian, A. (2023). Identifying Better Indicators of Aerosol Wet Scavenging During Long-Range Transport. EGUsphere, 2023, 1--26.
• Hoopes, C. A., Castro, C. L., Behrangi, A., Ehsani, M. R., & Broxton, P. (2023). Improving prediction of mountain snowfall in the southwestern United States using machine learning methods. Meteorological Applications, 30(6), e2153.
• Huffman, G. J., Adler, R. F., Behrangi, A., Bolvin, D. T., Nelkin, E. J., Gu, G., & Ehsani, M. R. (2023). The new version 3.2 global precipitation climatology project (GPCP) monthly and daily precipitation products. Journal of Climate, 36(21), 7635--7655.
• Li, Z., Thompson, E. J., Behrangi, A., Chen, H., & Yang, J. (2023). Performance of GPCP Products Over Oceans: Evaluation Using Passive Aquatic Listeners. Authorea Preprints.
• Li, Z., Thompson, E. J., Behrangi, A., Chen, H., & Yang, J. (2023). Performance of GPCP daily products over oceans: Evaluation using Passive Aquatic Listeners. Geophysical Research Letters, 50(11), e2023GL104310.
• Marcovecchio, A. R., Xi, B., Zheng, X., Wu, P., Dong, X., & Behrangi, A. (2023). What Are the Similarities and Differences in Marine Boundary Layer Cloud and Drizzle Microphysical Properties During the ACE-ENA and MARCUS Field Campaigns?. Journal of Geophysical Research: Atmospheres, 128(18), e2022JD037109.
• Rahimi, R., Ebtehaj, A., Behrangi, A., & Tan, J. (2023). Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture. arXiv preprint arXiv:2307.10843.
• Zeraati, M., Farahmand, A., Asghari, K., & Behrangi, A. (2023). Developing a Multivariate Agro-Meteorological Index to Improve Capturing Onset and Persistence of Droughts Utilizing Vapor Pressure Deficit (VPD) and Soil Moisture. Authorea Preprints.
• Adhikari, A., & Behrangi, A. (2022). Assessment of satellite precipitation products in relation with orographic enhancement over the western United States. Earth and Space Science, 9(2), e2021EA001906.
• Behrangi, A., Barnard, K., Gupta, H. V., Zarei, A., & Ehsani, M. R. (2022). NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products using Convolutional and Recurrent Neural Networks. IEEE Transactions on Geoscience and Remote Sensing. doi:http://arxiv.org/abs/2108.06868
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  Ehsani MR, A Zarei, HV Gupta, K Barnard and A Behrangi (2022), NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products using Convolutional and Recurrent Neural Networks, (http://arxiv.org/abs/2108.06868) IEEE Transactions on Geoscience and Remote Sensing.
• Behrangi, A., Gupta, H. V., Zarei, A., Ehsani, M. R., Barnard, K., & Lyons, E. (2022). NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-21. doi:10.1109/tgrs.2022.3158888
• Dashti, H., Smith, W. K., Huo, X., Fox, A. M., Javadian, M., Devine, C. J., Behrangi, A., & Moore, D. J. (2022). Underestimation of the impact of land cover change on the biophysical environment of the Arctic and Boreal Region of North America. Environmental Research Letters.
• Ehsani, M. R., & Behrangi, A. (2022). A comparison of correction factors for the systematic gauge-measurement errors to improve the global land precipitation estimate. Journal of Hydrology, 610(Issue). doi:10.1016/j.jhydrol.2022.127884
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  Precipitation gauges are critical for assessing precipitation measurements at regional and global scales and are often used to adjust precipitation estimates from other instruments such as satellites. However, gauge-measured precipitation is affected by gauge-undercatch that is often larger for solid precipitation. In the present work, two popular gauge-undercatch correction factors (CFs) were compared: one utilizes a dynamic correction model and is used in the Global Precipitation Climatology Centre (GPCC) Monitoring product and the other one employs fixed monthly climatologies and is used in the Global Precipitation Climatology Project (GPCP) product. How the choice of CFs impacts the total precipitation estimates was quantified over land at seasonal, annual, regional, and global scales. The CFs were also compared as a function of the environmental variables used in their development such as near-surface air temperature, relative humidity, and wind speed. Results showed that the annual precipitation estimate from gauges (with no correction) can be biased by ∼ 9.61% over the global land (excluding Antarctica), although it varied depending on the season (from ∼ 6.80% in boreal summer to more than 12.33% in boreal winter), and the method used for gauge-undercatch correction. Interannual variations of CFs can be large, so the use of the fixed climatology CFs requires caution. Given their magnitudes and differences, choosing appropriate CFs has important implications in refining the water and energy budget calculations.
• Ehsani, M. R., & Behrangi, A. (2022). A comparison of correction factors for the systematic gauge-measurement errors to improve the global land precipitation estimate. Journal of Hydrology, 610, 127884.
• Ehsani, M. R., Heflin, S., Risanto, C. B., & Behrangi, A. (2022). How well do satellite and reanalysis precipitation products capture North American monsoon season in Arizona and New Mexico?. Weather and Climate Extremes, 38(Issue). doi:10.1016/j.wace.2022.100521
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  Assessment of the precipitation products with ground-based data is essential to building confidence in these datasets. Precipitation products tend to have large errors in semi-arid regions such as the Southwest United States, where accurate precipitation quantification is critical for water resource management and flood mitigation measures. Therefore, this region, with its high density of ground-based data, is important for the evaluation of the products. The Southwest United States is also interesting due to its monsoonal precipitation pattern, in which changes in circulation patterns that bring tropical moisture to the region yield roughly 50% of the region's precipitation between the months of June–September. In the present study, the performance of precipitation products was evaluated over Arizona and New Mexico for the monsoon seasons of the 2002–2021 period, with an emphasis on the recent extreme years of 2020 and 2021. Results indicate that all satellite products tend to capture interannual variations of precipitation rate but struggled to capture high-intensity events. IMERG Final notably has better performance than other products, with the lowest root mean square error and highest correlation with Stage IV, which was 3–60 percent better than other products. IMERG Final had the best detection capacity for rainy days as well. ERA5-Land performed well in capturing the average monsoon precipitation rate; however, showed limited skill in the detection of trace, light, and extreme precipitation events. IMERG Late and PDIR-Now showed difficulty detecting light precipitation events and overestimated extreme events. This study shows the importance of gauge adjustment for satellite products (e.g., IMERG) as well as the need for improvement of reanalysis products over arid regions, and better representation of orographic precipitation.
• Ehsani, M. R., Heflin, S., Risanto, C. B., & Behrangi, A. (2022). How well do satellite and reanalysis precipitation products capture North American monsoon season in Arizona and New Mexico?. Weather and Climate Extremes, 38, 100521.
• Ehsani, M. R., Zarei, A., Gupta, H. V., Barnard, K., Lyons, E., & Behrangi, A. (2022). NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 60, 1--21.
• Ghajarnia, N., Akbari, M., Saemian, P., Ehsani, M. R., Hosseini-Moghari, S., Azizian, A., Kalantari, Z., Behrangi, A., Tourian, M. J., Kl"ove, B., & others, . (2022). Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5. Earth and Space Science, 9(10), e2022EA002352.
• Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Adler, R. F., Behrangi, A., Gu, G., & Ehsani, M. R. (2022). GPCP V3. 2 Release Notes.
• Javadian, M., Smith, W. K., Lee, K., Knowles, J. F., Scott, R. L., Fisher, J. B., Moore, D. J., Leeuwen, W. J., Barron-Gafford, G., & Behrangi, A. (2022). Canopy temperature is regulated by ecosystem structural traits and captures the ecohydrologic dynamics of a semiarid mixed conifer forest site. Journal of Geophysical Research: Biogeosciences, 127(2), e2021JG006617.
• Lau, A., & Behrangi, A. (2022). Understanding Intensity--Duration--Frequency (IDF) Curves Using IMERG Sub-Hourly Precipitation against Dense Gauge Networks. Remote Sensing, 14(19), 5032.
• Marcovecchio, A., Behrangi, A., Dong, X., Xi, B., & Huang, Y. (2022). Precipitation influence on and response to early and late Arctic Sea ice melt onset during melt season. International Journal of Climatology, 42(1), 81--96.
• Marcovecchio, A., Behrangi, A., Dong, X., Xi, B., & Huang, Y. (2022). Precipitation influence on and response to early and late Arctic sea ice melt onset during melt season. International Journal of Climatology, 42(Issue 1). doi:10.1002/joc.7233
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  The region containing portions of the East Siberian Sea and Laptev Sea (73°–84°N, 90°–155°E) is the area of focus (AOF) for this study. The impacts of precipitation, latent heat (LH) and sensible heat (SH) fluxes on sea ice melt onset in the AOF are investigated. Four early melting years (1990, 2012, 2003, and 1991) and four late melting years (1982, 1983, 1984, and 1996) are compared to better identify the different responses to melt onset timing. A consistency check is performed between multiple Arctic precipitation products (including NASA MERRA-2, ECMWF ERA-Interim [ERA-I], and ECMWF ERA5 reanalyses as well as GPCP V2.3 observations) since there is not yet a high-quality ground-truth Arctic precipitation data product. MERRA-2 has the greatest monthly average precipitation, snowfall, evaporation, and net LH flux. ERA-I suggests that liquid precipitation starts earlier in the year than MERRA-2 and ERA5, while GPCP shows different seasonal precipitation variations from the reanalyses. MERRA-2 has the clearest and most amplified seasonal trends for the parameters used in this study, so the daily time series and anomalies of MERRA-2 variables before and after the first major melt event are investigated. ERA5 is used to check these results because ERA-I and ERA5 display similar seasonal trends. According to MERRA-2, during early melt years, surface SH flux loss and precipitation are above average in the days before and after the first major melt event. During late melt years, surface SH flux loss and precipitation are below average in the month leading up to the first major melt event.
• Song, Y., Broxton, P. D., Behrangi, A., & Ehsani, M. (2021).

  Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

  . Remote Sensing, 13(15), 2922.
• Zandi, O., Zahraie, B., Nasseri, M., & Behrangi, A. (2022). Stacking machine learning models versus a locally weighted linear model to generate high-resolution monthly precipitation over a topographically complex area. Atmospheric Research, 272(Issue). doi:10.1016/j.atmosres.2022.106159
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  This study applied a stacked generalization ensemble approach to generate high-resolution precipitation estimates and compared its performance with an optimized local weighted linear regression (LWLR) algorithm, a well-known local precipitation-elevation interpolation method incorporating physiographical factors. The stacked generalization ensemble consists of multilayer perceptron neural network (MLP), support vector machine (SVM), and random forest (RF) combined through a meta-learning algorithm with/without rescanning input covariates. Sixteen input covariates, including 2 topographic features, 5 cloud properties, 5 environmental variables, 3 precipitation products (PPs), and inverse distance weighted (IDW) estimates as the precipitation background field were fed into the machine learning models. Hold out approach was adopted for performance evaluation in which 50% of the 174 gauges were used for training, and the rest was used for validation. The results indicated that the overall monthly MAE, RMSE, and rBias of the proposed stacking model for the validation dataset were 3.3%, 6.8%, and 50%, respectively, less than that of the RF model, which is the best individual model. Also, the stacking model outperformed LWLR by decreasing monthly MAE, RMSE, and rBias 10.7%, 19.1%, and 28.6%, respectively. Further analysis implied that (1) the stacked model is more robust than LWLR and less dependent on the density of gauges, thus suitable for areas with scarce gauge coverage; (2) comparing the spatial distribution of mean monthly precipitation maps, generated by stacking and LWLR models, stacking algorithm can successfully screen out the bulls' eyes of background IDW precipitation field, and both patterns are consistent with the topography of the area; and (3) the stacking scheme is found to have a better extrapolation ability than LWLR over high elevations.
• Zandi, O., Zahraie, B., Nasseri, M., & Behrangi, A. (2022). Stacking machine learning models versus a locally weighted linear model to generate high-resolution monthly precipitation over a topographically complex area. Atmospheric Research, 272, 106159.
• Adhikari, A., & Behrangi, A. (2021). Assessment of Satellite Precipitation Products in Relation with Orographic Enhancement Over the Western United States. Earth and Space Science Open Archive ESSOAr.
• Arabzadeh, A., & Behrangi, A. (2021). Investigating Various Products of IMERG for Precipitation Retrieval Over Surfaces With and Without Snow and Ice Cover.
• Arabzadeh, A., & Behrangi, A. (2021). Investigating various products of IMERG for precipitation retrieval over surfaces with and without snow and ice cover. Remote Sensing, 13(Issue 14). doi:10.3390/rs13142726
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  Precipitation rate from various products of the integrated multisatellite retrievals for GPM (IMERG) and passive microwave (PMW) sensors are assessed with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the contiguous United States (CONUS) and using ground radar product as reference precipitation. IMERG products include precipitation estimates from infrared (IR), combined PMW, and combination of PMW and IR. It was found that precipitation estimates from PMW products generally have higher skills than IR over snow-and ice-free surfaces. Over snow-and ice-covered surfaces: (1) most PMW products show higher correlation coefficients than IR, (2) at cold temperatures (e.g., Tw < −10◦C), PMW products tend to underestimate and IR product shows large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation occurrence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow-and ice-surfaces) may need to be revised.
• Ayat, H., Evans, J. P., & Behrangi, A. (2021). How do different sensors impact IMERG precipitation estimates during hurricane days?. Remote Sensing of Environment, 259(Issue). doi:10.1016/j.rse.2021.112417
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  Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final product (V06B) with a ground-based radar product, Multi-Radar Multi-Sensor (MRMS), using both pixel-based and object-based approaches. This study is focused on the eastern United States (land-only) during the hurricane days that occurred in 2016–2018. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area with a bias reduction of 75% and 65%, respectively, when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS compared to temporally averaged MRMS data (MRMS-Averaged). PMW observations tend to show storms with smaller areas in the IMERG Final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1 mm/h) in the object-based approach, hurricane objects in the IMERG Final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates have smaller areas with higher average intensity during the PMW observations in the IMERG Final product compared to data estimated by Morphed or IR (morph/IR) observations, which is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. In addition, with the longer absence of PMW observations, the quality of morph/IR estimates in IMERG Final product deteriorates with a decreasing correlation coefficient, a growth in precipitation area and a downward trend in average precipitation intensity. Finally, the inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area.
• Ayat, H., Evans, J. P., & Behrangi, A. (2021). How do different sensors impact IMERG precipitation estimates during hurricane days?. Remote Sensing of Environment, 259, 112417.
• Ayat, H., Evans, J. P., Sherwood, S., & Behrangi, A. (2021). Are storm characteristics the same when viewed using merged surface radars or a merged satellite product?. Journal of Hydrometeorology, 22(1), 43--62.
• Behrangi, A., Behrangi, A., Behrangi, A., Ehret, U., Ehret, U., Behrangi, A., Sans-Fuentes, M. A., Ehret, U., Sans-Fuentes, M. A., Roy, T., Ehret, U., Roy, T., Sans-Fuentes, M. A., Ehsani, R. M., Ehsani, R. M., Sans-Fuentes, M. A., Gupta, H. V., Gupta, H. V., Roy, T., , Roy, T., et al. (2021). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. Entropy.
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  Gupta HV, RM Ehsani, T Roy, MA Sans-Fuentes, U Ehret and A Behrangi (2021), Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples, Section on Information Theory, Probability and Statistics, Entropy, 23(6), 740, doi.org/10.3390/e23060740
• Behrangi, A., Behrangi, A., Ehret, U., Ehret, U., Sans-Fuentes, M. A., Sans-Fuentes, M. A., Roy, T., Roy, T., Ehsani, R. M., Ehsani, R. M., Gupta, H. V., & Gupta, H. V. (2021). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. arXiv.
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  Gupta HV, RM Ehsani, T Roy, MA Sans-Fuentes, U Ehret and A Behrangi (2021), Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples, posted (02/24/21) to Arxiv; http://arxiv.org/abs/2102.12675
• Dou, Y., Dou, Y., Ye, L., Ye, L., Gupta, H. V., Gupta, H. V., Zhang, H., Zhang, H., Behrangi, A., Behrangi, A., Zhou, H., & Zhou, H. (2021).

  Improved Flood Forecasting in Basins with No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products

  . Water Resources Research. doi:doi: 10.1029/2021WR029682
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  Dou Y, L Ye, HV Gupta, H Zhang, A Behrangi, H Zhou (2021), Improved Flood Forecasting in Basins with No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products, submitted to Water Resources Research, doi: 10.1029/2021WR029682
• Dou, Y., Dou, Y., Ye, L., Ye, L., Gupta, H. V., Gupta, H. V., Zhang, H., Zhang, H., Behrangi, A., Behrangi, A., Zhou, H., & Zhou, H. (2021). Improved Flood Forecasting in Basins with No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products. Water Resources Research. doi:doi: 10.1029/2021WR029682
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  Dou Y, L Ye, HV Gupta, H Zhang, A Behrangi, H Zhou (2021), Improved Flood Forecasting in Basins with No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products, submitted to Water Resources Research, doi: 10.1029/2021WR029682
• Dou, Y., Ye, L., Gupta, H. V., Zhang, H., Behrangi, A., & Zhou, H. (2021). Improved Flood Forecasting in Basins with No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products. Water Resources Research, e2021WR029682.
• Ehsani, M. R., & Behrangi, A. (2021). On the Importance of Gauge-Undercatch Correction Factors and Their Impacts on the Global Precipitation Estimates.
• Ehsani, M. R., Behrangi, A., Adhikari, A., Song, Y., Huffman, G. J., Adler, R. F., Bolvin, D. T., & Nelkin, E. J. (2021). Assessment of the Advanced Very High Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning. Journal of Hydrometeorology, 22(6), 1591--1608.
• Ehsani, M. R., Behrangi, A., Adhikari, A., Song, Y., Huffman, G. J., Adler, R. F., Bolvin, D. T., & Nelkin, E. J. (2021). Assessment of the advanced very high resolution radiometer (Avhrr) for snowfall retrieval in high latitudes using cloudsat and machine learning. Journal of Hydrometeorology, 22(Issue 6). doi:10.1175/jhm-d-20-0240.1
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  Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because 1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; 2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and 3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.
• Ehsani, M. R., Zarei, A., Gupta, H. V., Barnard, K., & Behrangi, A. (2021). Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG. arXiv preprint arXiv:2108.06868.
• Ghajarnia, N., Akbari, M., Saemian, P., Ehsani, M. R., Hosseini-Moghari, S., Azizian, A., Kalantari, Z., Behrangi, A., Tourian, M. J., Kl"ove, B., & others, . (2021). Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5.
• Gupta, H. V., Ehsani, M. R., Roy, T., Sans-Fuentes, M. A., Ehret, U., & Behrangi, A. (2021). Computing accurate probabilistic estimates of one-D entropy from equiprobable random samples. Entropy, 23(6), 740.
• Gupta, H. V., Ehsani, M. R., Roy, T., Sans‐fuentes, M. A., Ehret, U., & Behrangi, A. (2021). Computing accurate probabilistic estimates of one‐d entropy from equiprobable random samples. Entropy, 23(Issue 6). doi:10.3390/e23060740
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  We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one‐dimensional entropy from equiprobable random samples, and compare it with the popular Bin‐Counting (BC) and Kernel Density (KD) methods. In contrast to BC, which uses equal‐width bins with varying probability mass, the QS method uses estimates of the quantiles that divide the support of the data generating probability density function (pdf) into equal‐probability‐mass intervals. And, whereas BC and KD each require optimal tuning of a hyper‐parameter whose value varies with sample size and shape of the pdf, QS only requires specification of the number of quantiles to be used. Results indicate, for the class of distributions tested, that the optimal number of quantiles is a fixed fraction of the sample size (empirically determined to be ~0.25– 0.35), and that this value is relatively insensitive to distributional form or sample size. This provides a clear advantage over BC and KD since hyper‐parameter tuning is not required. Further, unlike KD, there is no need to select an appropriate kernel‐type, and so QS is applicable to pdfs of arbitrary shape, including those with discontinuous slope and/or magnitude. Bootstrapping is used to approximate the sampling variability distribution of the resulting entropy estimate, and is shown to accurately reflect the true uncertainty. For the four distributional forms studied (Gaussian, Log‐Normal, Exponential and Bimodal Gaussian Mixture), expected estimation bias is less than 1% and uncertainty is low even for samples of as few as 100 data points; in contrast, for KD the small sample bias can be as large as-10% and for BC as large as -50%. We speculate that estimating quantile locations, rather than bin‐probabilities, results in more efficient use of the information in the data to approximate the underlying shape of an unknown data generating pdf.
• Gupta, H. V., Gupta, H. V., Ehsani, R. M., Ehsani, R. M., Roy, T., Roy, T., Sans-Fuentes, M. A., Sans-Fuentes, M. A., Ehret, U., Ehret, U., Behrangi, A., & Behrangi, A. (2021).

  Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples

  . Entropy.
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  Gupta HV, RM Ehsani, T Roy, MA Sans-Fuentes, U Ehret and A Behrangi (2021), Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples, Section on Information Theory, Probability and Statistics, Entropy, 23(6), 740, doi.org/10.3390/e23060740
• Hilario, M., Crosbie, E., Shook, M., Reid, J. S., Cambaliza, M., Simpas, J., Ziemba, L., DiGangi, J. P., Diskin, G. S., Nguyen, P., & others, . (2021). Measurement report: Long-range transport patterns into the tropical northwest Pacific during the CAMP 2 Ex aircraft campaign: chemical composition, size distributions, and the impact of convection. Atmospheric Chemistry and Physics, 21(5), 3777--3802.
• Li, Z., Wen, Y., Schreier, M., Behrangi, A., Hong, Y., & Lambrigtsen, B. (2021). Advancing Satellite Precipitation Retrievals With Data Driven Approaches: Is Black Box Model Explainable?. Earth and Space Science, 8(2), e2020EA001423.
• Ricardo A. Hilario, M., Crosbie, E., Shook, M., Reid, J. S., Obiminda L. Cambaliza, M., Bernard B. Simpas, J., Ziemba, L., DIgangi, J. P., DIskin, G. S., Nguyen, P., Joseph Turk, F., Winstead, E., Robinson, C. E., Wang, J., Zhang, J., Wang, Y., Yoon, S., Flynn, J., Alvarez, S. L., , Behrangi, A., et al. (2021). Measurement report: Long-range transport patterns into the tropical northwest Pacific during the CAMP2Ex aircraft campaign: Chemical composition, size distributions, and the impact of convection. Atmospheric Chemistry and Physics, 21(Issue 5). doi:10.5194/acp-21-3777-2021
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  The tropical Northwest Pacific (TNWP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosolmeteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex; 24 August to 5 October 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were used to examine transport into the TNWP from the Maritime Continent (MC), peninsular Southeast Asia (PSEA), East Asia (EA), and the West Pacific (WP). A mid-campaign monsoon shift on 20 September 2019 led to distinct transport patterns between the southwest monsoon (SWM; before 20 September) and monsoon transition (MT; after 20 September). During the SWM, long-range transport was a function of southwesterly winds and cyclones over the South China Sea. Low- (high-) altitude air generally came from MC (PSEA), implying distinct aerosol processing related to convection and perhaps wind shear. The MT saw transport from EA and WP, driven by Pacific northeasterly winds, continental anticyclones, and cyclones over the East China Sea. Composition of transported air differed by emission source and accumulated pre- cipitation along trajectories (APT). MC air was characterized by biomass burning tracers while major components of EA air pointed to Asian outflow and secondary formation. Convective scavenging of PSEA air was evidenced by considerable vertical differences between aerosol species but not trace gases, as well as notably higher APT and smaller particles than other regions. Finally, we observed a possible wet scavenging mechanism acting on MC air aloft that was not strictly linked to precipitation. These results are important for understanding the transport and processing of air masses with further implications for modeling aerosol lifecycles and guiding international policymaking to public health and climate, particularly during the SWM and MT.
• Singh, A., Reager, J. T., & Behrangi, A. (2021). Estimation of hydrological drought recovery based on precipitation and Gravity Recovery and Climate Experiment (GRACE) water storage deficit. Hydrology and Earth System Sciences, 25(2), 511--526.
• Singh, A., Reager, J. T., & Behrangi, A. (2021). Estimation of hydrological drought recovery based on precipitation and Gravity Recovery and Climate Experiment (GRACE) water storage deficit. Hydrology and Earth System Sciences, 25(Issue 2). doi:10.5194/hess-25-511-2021
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  Drought is a natural extreme climate phenomenon that presents great challenges in forecasting and monitoring for water management purposes. Previous studies have examined the use of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies to measure the amount of water missing from a drought-affected region, and other studies have attempted statistical approaches to drought recovery forecasting based on joint probabilities of precipitation and soil moisture. The goal of this study is to combine GRACE data and historical precipitation observations to quantify the amount of precipitation required to achieve normal storage conditions in order to estimate a likely drought recovery time. First, linear relationships between terrestrial water storage anomaly (TWSA) and cumulative precipitation anomaly are established across a range of conditions. Then, historical precipitation data are statistically modeled to develop simplistic precipitation forecast skill based on climatology and long-term trend. Two additional precipitation scenarios are simulated to predict the recovery period by using a standard deviation in climatology and long-term trend. Precipitation scenarios are convolved with water deficit estimates (from GRACE) to calculate the best estimate of a drought recovery period. The results show that, in the regions of strong seasonal amplitude (like a monsoon belt), drought continues even with above-normal precipitation until its wet season. The historical GRACE-observed drought recovery period is used to validate the approach. Estimated drought for an example month demonstrated an 80 % recovery period, as observed by the GRACE.
• Song, Y., Broxton, P. D., Ehsani, M. R., & Behrangi, A. (2021). Assessment of snowfall accumulation from satellite and reanalysis products using SNOTEL observations in Alaska. Remote Sensing, 13(Issue 15). doi:10.3390/rs13152922
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  The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumula-tion. We found that correction factors applied to rain gauges are effective for improving their un-dercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.
• Song, Y., Broxton, P., Ehsani, M. R., & Behrangi, A. (2021). Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska.
• Adhikari, A., Ehsani, M. R., Song, Y., & Behrangi, A. (2020). Comparative assessment of snowfall retrieval from Microwave Humidity Sounders using machine learning methods. Earth and Space Science, 7(11), e2020EA001357.
• Arabzadeh, A., Ehsani, M. R., Guan, B., Heflin, S., & Behrangi, A. (2020). Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products. Journal of Geophysical Research: Atmospheres, 125(21), e2020JD033021.
• Behrangi, A., & Song, Y. (2020). A new estimate for oceanic precipitation amount and distribution using complementary precipitation observations from space and comparison with GPCP. Environmental Research Letters, 15(12), 124042.
• Behrangi, A., Gardner, A. S., & Wiese, D. N. (2020). Comparative analysis of snowfall accumulation over Antarctica in light of Ice discharge and gravity observations from space. Environmental Research Letters, 15(Issue 10). doi:10.1088/1748-9326/ab9926
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  The remote and cold Antarctic continent presents unique challenges to quantify precipitation rates from space and in situ observations. This has resulted in large uncertainties in current estimates. In this study, we quantify annual precipitation rates over seven Antarctic basins using a novel mass budget (MB) approach, by building on the recent Landsat based estimate of ice discharge and changes in total water storage from GRACE. The MB precipitation rates are compared with those from CloudSat, GPCP, the Arthern precipitation climatology, the GPM constellation sensors, a few popular reanalysis products, and a regional climate model for two periods: 2007–2010 and 2013–2015. The new estimates are bounded by CloudSat precipitation rates with and without adjustment for the unmeasured near surface precipitation. GPM products significantly underestimate Antarctic precipitation rate, but capture spatial variability that is valuable for bias-adjustment. We find variable performance between products at basin scale, suggesting that an in-depth regional study of precipitation rates is necessary.
• Behrangi, A., Gardner, A. S., & Wiese, D. N. (2020). Comparative analysis of snowfall accumulation over Antarctica in light of ice discharge and gravity observations from space. Environmental Research Letters.
• Dadashazar, H., Crosbie, E., Majdi, M. S., Panahi, M., Moghaddam, M. A., Behrangi, A., Brunke, M., Zeng, X., Jonsson, H. H., & Sorooshian, A. (2020). Stratocumulus cloud clearings: Statistics from satellites, reanalysis models, and airborne measurements. Atmospheric Chemistry and Physics, 20(Issue 8). doi:10.5194/acp-20-4637-2020
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  This study provides a detailed characterization of stratocumulus clearings off the US West Coast using remote sensing, reanalysis, and airborne in situ data. Ten years (2009 2018) of Geostationary Operational Environmental Satellite (GOES) imagery data are used to quantify the monthly frequency, growth rate of total area (GRArea), and dimensional characteristics of 306 total clearings. While there is interannual variability, the summer (winter) months experienced the most (least) clearing events, with the lowest cloud fractions being in close proximity to coastal topographical features along the central to northern coast of California, including especially just south of Cape Mendocino and Cape Blanco. From 09:00 to 18:00 (PST), the median length, width, and area of clearings increased from 680 to 1231, 193 to 443, and ~ 67000 to ~ 250000 km2, respectively. Machine learning was applied to identify the most influential factors governing the GRArea of clearings between 09:00 and 12:00 PST, which is the time frame of most rapid clearing expansion. The results from gradient-boosted regression tree (GBRT) modeling revealed that air temperature at 850 hPa (T850), specific humidity at 950 hPa (q950), sea surface temperature (SST), and anomaly in mean sea level pressure (MSLPanom) were probably most impactful in enhancing GRArea using two scoring schemes. Clearings have distinguishing features such as an enhanced Pacific high shifted more towards northern California, offshore air that is warm and dry, stronger coastal surface winds, enhanced lower-tropospheric static stability, and increased subsidence. Although clearings are associated obviously with reduced cloud fraction where they reside, the domain-averaged cloud albedo was actually slightly higher on clearing days as compared to non-clearing days. To validate speculated processes linking environmental parameters to clearing growth rates based on satellite and reanalysis data, airborne data from three case flights were examined. Measurements were compared on both sides of the clear cloudy border of clearings at multiple altitudes in the boundary layer and free troposphere, with results helping to support links suggested by this study s model simulations. More specifically, airborne data revealed the influence of the coastal low-level jet and extensive horizontal shear at cloud-relevant altitudes that promoted mixing between clear and cloudy air. Vertical profile data provide support for warm and dry air in the free troposphere, additionally promoting expansion of clearings. Airborne data revealed greater evidence of sea salt in clouds on clearing days, pointing to a possible role for, or simply the presence of, this aerosol type in clearing areas coincident with stronger coastal winds.
• Dadashazar, H., Crosbie, E., Majdi, M. S., Panahi, M., Moghaddam, M. A., Behrangi, A., Brunke, M., Zeng, X., Jonsson, H. H., & Sorooshian, A. (2020). Stratocumulus cloud clearings: statistics from satellites, reanalysis models, and airborne measurements.. Atmospheric Chemistry & Physics, 20(8).
• Ehsani, M. R., Arevalo, J., Risanto, C. B., Javadian, M., Devine, C. J., Arabzadeh, A., Venegas-Qui~nones, H. L., Dell’Oro, A. P., & Behrangi, A. (2020). 2019--2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities. Water, 12(11), 3067.
• Ehsani, M. R., Arevalo, J., Risanto, C. B., Javadian, M., Devine, C. J., Arabzadeh, A., Venegas-Quiñones, H. L., Dell’oro, A. P., & Behrangi, A. (2020). 2019–2020 australia fire and its relationship to hydroclimatological and vegetation variabilities. Water (Switzerland), 12(Issue 11). doi:10.3390/w12113067
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  Wildfire is a major concern worldwide and particularly in Australia. The 2019–2020 wildfires in Australia became historically significant as they were widespread and extremely severe. Linking climate and vegetation settings to wildfires can provide insightful information for wildfire prediction, and help better understand wildfires behavior in the future. The goal of this research was to examine the relationship between the recent wildfires, various hydroclimatological variables, and satellite-retrieved vegetation indices. The analyses performed here show the uniqueness of the 2019–2020 wildfires. The near-surface air temperature from December 2019 to February 2020 was about 1◦C higher than the 20-year mean, which increased the evaporative demand. The lack of precipitation before the wildfires, due to an enhanced high-pressure system over southeast Australia, prevented the soil from having enough moisture to supply the demand, and set the stage for a large amount of dry fuel that highly favored the spread of the fires.
• Farahmand, A., Natasha Stavros, E., Reager, J. T., Behrangi, A., Randerson, J. T., & Quayle, B. (2020). Satellite hydrology observations as operational indicators of forecasted fire danger across the contiguous United States. Natural Hazards and Earth System Sciences, 20(Issue 4). doi:10.5194/nhess-20-1097-2020
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  Traditional methods for assessing fire danger often depend on meteorological forecasts, which have reduced reliability after &tild;1/410 d. Recent studies have demonstrated long lead-time correlations between pre-fire-season hydrological variables such as soil moisture and later fire occurrence or area burned, yet the potential value of these relationships for operational forecasting has not been studied. Here, we use soil moisture data refined by remote sensing observations of terrestrial water storage from NASA's Gravity Recovery and Climate Experiment (GRACE) mission and vapor pressure deficit from NASA's Atmospheric Infrared Sounder (AIRS) mission to generate monthly predictions of fire danger at scales commensurate with regional management. We test the viability of predictors within nine US geographic area coordination centers (GACCs) using regression models specific to each GACC. Results show that the model framework improves interannual wildfire-burned-area prediction relative to climatology for all GACCs. This demonstrates the importance of hydrological information to extend operational forecast ability into the months preceding wildfire activity.
• Farahmand, A., Stavros, E. N., Reager, J. T., & Behrangi, A. (2020). Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States. Remote Sensing, 12(8), 1252.
• Farahmand, A., Stavros, E. N., Reager, J. T., & Behrangi, A. (2020). Introducing spatially distributed fire danger from earth observations (FDEO) using satellite-based data in the contiguous United States. Remote Sensing, 12(Issue 8). doi:10.3390/rs12081252
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  Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System-NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from "normal". The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004-2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.
• Farahmand, A., Stavros, E. N., Reager, J. T., Behrangi, A., Randerson, J. T., & Quayle, B. (2020). Satellite hydrology observations as operational indicators of forecasted fire danger across the contiguous United States.. Natural Hazards and Earth System Sciences, 20(4), 1097--1106.
• Gupta, H. V., Ehsani, R. M., Roy, T., Sans-Fuentes, M. A., Ehret, U., & Behrangi, A. (2020). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. Entropy.
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  HV Gupta, MR Ehsani, T Roy, MA Sans-Fuentes, U Ehret, A BehrangiEntropy 23 (6), 740
• Hilario, M., Crosbie, E., Shook, M., Reid, J. S., Cambaliza, M., Simpas, J., Ziemba, L., DiGangi, J. P., Diskin, G. S., Nguyen, P., & others, . (2020). Long-range transport patterns into the tropical northwest Pacific during the CAMP 2 Ex aircraft campaign: chemical composition, size distributions, and the impact of convection. Atmospheric Chemistry and Physics Discussions, 1--42.
• Javadian, M., Behrangi, A., Smith, W. K., & Fisher, J. B. (2020). Global Trends in Evapotranspiration Dominated by Increases across Large Cropland Regions. Remote Sensing, 12(7), 1221.
• Javadian, M., Behrangi, A., Smith, W. K., & Fisher, J. B. (2020). Global trends in evapotranspiration dominated by increases across large cropland regions. Remote Sensing, 12(Issue 7). doi:10.3390/rs12071221
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  Irrigated croplands require large annual water inputs and are critical to global food production. Actual evapotranspiration (AET) is a main index of water use in croplands, and several remote-sensing products have been developed to quantify AET at the global scale. In this study, we estimate global trends in actual AET, potential ET (PET), and precipitation rate (PP) utilizing the MODIS Evapotranspiration product (2001-2018) within the Google Earth Engine cloud-computing environment. We then introduce a new index based on a combination of AET, PET, and PP estimates-the evapotranspiration warning index (ETWI)-which we use to evaluate the sustainability of observed AET trends.We show that while AET has not considerably changed across global natural lands, it has significantly increased across global croplands (+14% ± 5%). The average ETWI for global croplands is -0.40 ± 0.25, which is largely driven by an extreme trend in AET, exceeding both PET and PP trends. Furthermore, the trends in water and energy limited areas demonstrate, on a global scale, while AET and PET do not have significant trends in both water and energy limited areas, the increasing trend of PP in energy-limited areas is more than water-limited areas. Averaging cropland ETWI trends at the country level further revealed nonsustainable trends in cropland water consumptions in Thailand, Brazil, and China. These regions were also found to experiencing some of the largest increases in net primary production (NPP) and solar-induced fluorescence (SIF), suggesting that recent increases in food production may be dependent on unsustainable water inputs. Globally, irrigated maize was found to be associated with nonsustainable AET trends relative to other crop types. We present an online open access application designed to enable near real-time monitoring and improve the understanding of global water consumption and availability.
• Lyu, F., Tang, G., Behrangi, A., Wang, T., Tan, X., Ma, Z., & Xiong, W. (2020). Precipitation Merging Based on the Triple Collocation Method Across Mainland China. IEEE Transactions on Geoscience and Remote Sensing.
• Song, Y., Behrangi, A., & Blanchard-Wrigglesworth, E. (2020). Assessment of Satellite and Reanalysis Cold Season Snowfall Estimates Over Arctic Sea Ice. Geophysical Research Letters, 47(16), e2020GL088970.
• Adler, R. F., Gu, G., Wang, J., Huffman, G. J., & Behrangi, A. (2019). Improved Observation of Global Precipitation During the Satellite Era. AGUFM, 2019, H21E--01.
• Alexander, L. V., Fowler, H. J., Bador, M., Behrangi, A., Donat, M. G., Dunn, R., Funk, C., Goldie, J., Lewis, E., Rog'e, M., & others, . (2019). On the use of indices to study extreme precipitation on sub-daily and daily timescales. Environmental Research Letters, 14(12), 125008.
• Alexander, L. V., Fowler, H. J., Bador, M., Behrangi, A., Donat, M. G., Dunn, R., Funk, C., Goldie, J., Lewis, E., Rogé, M., Seneviratne, S. I., & Venugopal, V. (2019). On the use of indices to study extreme precipitation on sub-daily and daily timescales. Environmental Research Letters, 14(Issue 12). doi:10.1088/1748-9326/ab51b6
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  While there are obstacles to the exchange of long-term high temporal resolution precipitation data, there have been fewer barriers to the exchange of so-called 'indices'. These are derived from daily and sub-daily data and measure aspects of precipitation frequency, duration and intensity that could be used for the study of extremes. This paper outlines the history of the rationale and use of these indices, the types of indices that are frequently used and the advantages and pitfalls in analysing them. Moving forward, satellite precipitation products are now showing the potential to provide global climate indices to supplement existing products using longer-term in situ gauge records but we suggest that to advance this area differences between data products, limitations in satellite-based estimation processes, and the inherent challenges of scale need to be better understood.
• Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C., & Mechoso, C. R. (2019). A Recent Systematic Increase in Vapor Pressure Deficit over Tropical South America. Scientific Reports, 9(Issue 1). doi:10.1038/s41598-019-51857-8
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  We show a recent increasing trend in Vapor Pressure Deficit (VPD) over tropical South America in dry months with values well beyond the range of trends due to natural variability of the climate system defined in both the undisturbed Preindustrial climate and the climate over 850–1850 perturbed with natural external forcing. This trend is systematic in the southeast Amazon but driven by episodic droughts (2005, 2010, 2015) in the northwest, with the highest recoded VPD since 1979 for the 2015 drought. The univariant detection analysis shows that the observed increase in VPD cannot be explained by greenhouse-gas-induced (GHG) radiative warming alone. The bivariate attribution analysis demonstrates that forcing by elevated GHG levels and biomass burning aerosols are attributed as key causes for the observed VPD increase. We further show that There is a negative trend in evaporative fraction in the southeast Amazon, where lack of atmospheric moisture, reduced precipitation together with higher incoming solar radiation (~7% decade−1 cloud-cover reduction) influences the partitioning of surface energy fluxes towards less evapotranspiration. The VPD increase combined with the decrease in evaporative fraction are the first indications of positive climate feedback mechanisms, which we show that will continue and intensify in the course of unfolding anthropogenic climate change.
• Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C., & Mechoso, C. R. (2019). A recent systematic increase in vapor pressure deficit over tropical South America. Scientific reports, 9(1), 1--12.
• Behrangi, A., & Song, Y. (2019). A new estimate for oceanic precipitation amount and distribution using complementary precipitation observations from space and comparison with GPCP. Environmental Research Letters, 15(Issue 12). doi:10.1088/1748-9326/abc6d1
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  This study produces near global (81°S/N) spatial and seasonal maps of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference that can be used to assess current precipitation products. The Merged CloudSat, Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) (MCTG) estimate uses light rainfall and snowfall estimates from CloudSat and merges them with the combined radar-radiometer products available from the TRMM and the GPM mission. The merging process is performed at grid level and for each season, so maps of the merged products can be constructed. MCTG was then compared with the most recent Global Precipitation Climatology Project (GPCP) product (V2.3) to identify regional, seasonal, and annual differences between the two products. Several areas of major differences were highlighted, among those are regions near 5°N/S, 20°N/S, 40°N/S and 60°N/S. These regions also show seasonal variations in the magnitude and exact location of the differences. The largest differences between GPCP and MCTG occur around 40°S and 60°S, showing an under- and overestimation of MCTG, respectively. Overall, MCTG suggests that GPCP underestimates the annual oceanic precipitation rate by 9.03%, while seasonal rates are underestimated by 7.14%, 9.71%, 9.96%, and 9.73% for winter, spring, summer, and fall, respectively. Such differences in global oceanic precipitation rates need to be considered in the future updates in water and energy budget calculations and in future updates of GPCP.
• Behrangi, A., Singh, A., Song, Y., & Panahi, M. (2019). Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations. Geophysical Research Letters.
• Funk, C., Harrison, L., Alexander, L., Peterson, P., Behrangi, A., & Husak, G. (2019). Exploring trends in wet-season precipitation and drought indices in wet, humid and dry regions. Environmental Research Letters, 14(11), 115002.
• Funk, C., Harrison, L., Alexander, L., Peterson, P., Behrangi, A., & Husak, G. (2019). Exploring trends in wet-season precipitation and drought indices in wet, humid and dry regions. Environmental Research Letters, 14(Issue 11). doi:10.1088/1748-9326/ab4a6c
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  This study examines wet season droughts using eight products from the Frequent Rainfall Observations on GridS database. The study begins by evaluating wet season precipitation totals and wet day counts at seasonal and decadal time scales. While we find a high level of agreement among the products at a seasonal time scale, evaluations of 10 year variability indicate substantial non-stationary inter-product differences that make the assessment of low-frequency changes difficult, especially in data-sparse regions. Some products, however, appear more reliable than others on decadal time scales. Global time series of dry, middle, and wet region standardized precipitation index time series indicate little coherent change. There is substantial coherence in year-to-year variations in these time series for the better-performing products, likely indicative of skill for monitoring variations at large spatial scales. During the wet season, the data do not appear to indicate widespread global changes in precipitation, reference evapotranspiration (RefET) or Standardized Precipitation Evapotranspiration Index (SPEI) values. These data also do not indicate a global shift towards increasing aridity. Focusing on SPEI values for dry regions during droughts, however, we find modest increases in RefET and decreases in SPEI when wet season precipitation is below normal. Dry region SPEI values during droughts have decreased by -0.2 since the 1990s. The cause of these RefET increases is unclear, and more detailed analysis will be needed to confirm these results. For wet regions, however, the majority of products appear to indicate increases in wet season precipitation, although many products perform poorly in these regions due to limited observation networks, and estimated increases vary substantially. Synopsis: Our analysis indicates a lack of increasing aridity at global scales, issues associated with non-stationary systematic errors, and concerns associated with increases in reference evapotranspiration in global dry regions during droughts.
• Golian, S., Javadian, M., & Behrangi, A. (2019). On the use of satellite, gauge, and reanalysis precipitation products for drought studies. Environmental Research Letters, 14(Issue 7). doi:10.1088/1748-9326/ab2203
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  Precipitation is a critical variable to monitor and predict meteorological drought. The WMO recommended standardized precipitation index (SPI) is calculated from gauge (i.e. GPCC), satellite-gauge (GPCP, CHIRPS), reanalysis (i.e. ERA-Interim, and MERRA-2), and satellite-gauge-reanalysis (i.e. MSWEP) over the global domain. Measured differences among the precipitation datasets include metrics such as percent area under drought, number of drought events, spread and correlation in the number of drought events, and critical success index in capturing moderate and severe-exceptional droughts. As precipitation products are available at different lengths and spatial resolutions, sensitivity of drought metrics to record-length and spatial resolution were explored. The results suggest that precipitation-based drought metrics can vary significantly with the choice of precipitation product, its record-lengths, and spatial resolution. These relationships also vary with the severity of drought events with more severe drought events being more sensitive to the differences in resolution and record length. The quantified variation among the products has to be recognized in the interpretation of drought events when a single or a subset of products used.
• Golian, S., Javadian, M., & Behrangi, A. (2019). On the use of satellite, gauge, and reanalysis precipitation products for drought studies. Environmental Research Letters.
• Hu, J., Chen, S., Behrangi, A., & Yuan, H. (2019). Parametric uncertainty assessment in hydrological modeling using the generalized polynomial chaos expansion. Journal of Hydrology, 579(Issue). doi:10.1016/j.jhydrol.2019.124158
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  An integrated framework is proposed for parametric uncertainty analysis in hydrological modeling using a generalized polynomial chaos expansion (PCE) approach. PCE represents model output as a polynomial expression in terms of critical random variables that are determined by parameter uncertainties, thus offers an efficient way of sampling without running the original model, which is appealing to computationally expensive models. To demonstrate the applicability of generalized PCE approach, both second- and third-order PCEs (PCE-2 and PCE-3) are constructed for Xinanjiang hydrological model using three selected uncertain parameters. Uncertainties in streamflow predictions are assessed by sampling the random inputs. Results show that: (1) both PCE-2 and PCE-3 are capable of capturing the uncertainty information in hydrological predictions, generating consistent mean, variance, skewness and kurtosis estimates with the standard Monte Carlo (MC) methodology; (2) Using more collocation points and more polynomial terms, PCE-3 approximation slightly improves the model simulation and provides more matched distribution with that of MC compared to PCE-2; (3) the computational cost using the PCE approach is greatly reduced by 71% (20%) with PCE-2 (PCE-3). In general, PCE-2 is recommended to serve as a good surrogate model for Xinanjiang hydrological modelling in future with much higher computation speed, more efficient sampling, and compatible approximation results.
• Hu, J., Chen, S., Behrangi, A., & Yuan, H. (2019). Parametric uncertainty assessment in hydrological modeling using the generalized polynomial chaos expansion. Journal of Hydrology, 579, 124158.
• Javadian, M., Behrangi, A., & Sorooshian, A. (2019). Impact of drought on dust storms: Case study over Southwest Iran. Environmental Research Letters, 14(Issue 12). doi:10.1088/1748-9326/ab574e
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  Dust storms are common meteorological events in arid and semi-arid regions, particularly in Southwest Iran (SWI). Here we study the relation between drought events in Iraq and dust storms in SWI between 2003 and 2018. The HYSPLIT model showed that central and southern Iraq are the main dust sources for SWI. Mean annual aerosol optical depth (AOD) analysis demonstrated that 2008 and 2009 were the dustiest years since 2003 and there is an increased frequency of summertime extreme dust events in the years 2008 and 2009. The Standardized Precipitation Evapotranspiration Index revealed that drought in Iraq significantly affects dust storms in Iran. Similarly, dramatic desiccation of Iraq wetlands has contributed to increasing fall dust events in SWI. AOD in SWI is highly correlated (-0.76) with previous-month vapor pressure deficit (VPD) over Iraq, demonstrating the potential of VPD for dust event forecasting.
• Javadian, M., Behrangi, A., & Sorooshian, A. (2019). Impact of drought on dust storms: case study over Southwest Iran. Environmental Research Letters, 14(12), 124029.
• Javadian, M., Behrangi, A., Gholizadeh, M., & Tajrishy, M. (2019). METRIC and WaPOR estimates of evapotranspiration over the Lake Urmia Basin: comparative analysis and composite assessment. Water, 11(8), 1647.
• Javadian, M., Behrangi, A., Gholizadeh, M., & Tajrishy, M. (2019). METRIC and WaPOR estimates of evapotranspiration over the Lake Urmia basin: Comparative analysis and composite assessment. Water (Switzerland), 11(Issue 8). doi:10.3390/w11081647
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  Evapotranspiration is one of the main components of water and energy balance. In this study, we compare two ET products, suitable for regional analysis at high spatial resolution: The recent WaPOR product developed by FAO and METRIC algorithm. WaPOR is based on ETLook, which is a two-source model and relies on microwave images. WaPOR is unique as it has no limitation under cloudy days, but METRIC is limited by clouds. METRIC and WaPOR are more sensitive to land surface temperature and soil moisture, respectively. Using two years (2010 and 2014) of data over Lake Urmia basin, we show that in most areas, ET from METRIC is higher than WaPOR and the difference has an ascending trend with the elevation. The ET of lysimeter station is fairly consistent with METRIC based on a single observation. Our analysis using NDVI and land use maps suggests that the histogram of ET from WaPOR might be more realistic than METRIC, but not its amount. The fraction of ET to precipitation in rainfed agriculture areas shows thatWaPOR is more accurate than METRIC, mainly because in the absence of other water resources such as ground water annual ET cannot exceed annual precipitation. In contrast, METRIC produces a more realistic estimate than WaPOR over irrigated farms. The results suggest that the two products can complement each other.
• Marcovecchio, A., Dong, X., Behrangi, A., & Xi, B. (2019). How Uncertainties in Precipitation Estimates are Mapped on to Our Understanding of the Relationship Between Arctic Precipitation and Arctic Sea Ice. AGUFM, 2019, C23D--1585.
• Panahi, M., & Behrangi, A. (2019). Comparative Analysis of Snowfall Accumulation and Gauge Undercatch Correction Factors from Diverse Data Sets: In Situ, Satellite, and Reanalysis. Asia-Pacific Journal of Atmospheric Sciences, 1--14.
• Singh, A., Reager, J. T., & Behrangi, A. (2019). Estimation of hydrological drought recovery based on GRACE water storage deficit. Hydrology and Earth System Sciences Discussions, 1--23.
• Wang, Y. H., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., & Niu, G. Y. (2019). A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States. Geophysical Research Letters, 46(Issue 23). doi:10.1029/2019gl085722
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  Accumulation of snowfall during winter and snowmelt in the subsequent spring or earlier summer provides a dominant water source in alpine regions. Most land surface and hydrological models use near-surface air temperature (Ta) thresholds to partition precipitation into snow and rain, underestimating snowfall over drier regions. We developed a snow-rain partitioning scheme using the wet-bulb temperature (Tw), which is closer to the surface temperature of a falling hydrometeor than Ta. Tw becomes more depressed in drier environments as derived from Tw depression equation using Ta and surface air humidity, resulting in a greater fraction of snowfall. We implemented this new Tw scheme in the Noah-MP land surface model and evaluated the model against a high-quality ground-based snow product over the contiguous United States. The results suggest that the new Tw scheme substantially improves the model skill in simulating snow depth and snow water equivalent over most snow-covered grids, especially the higher and drier continental mountain ranges in the Western United States, while it retains the modeling accuracy over the more humid Eastern United States.
• Wang, Y., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., & Niu, G. (2019). A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme Improves Snowpack Prediction over the Drier Western US. Geophysical Research Letters.
• Aumann, H. H., Behrangi, A., & Wang, Y. (2018). Increased Frequency of Extreme Tropical Deep Convection: AIRS Observations and Climate Model Predictions. Geophysical Research Letters, 45(Issue 24). doi:10.1029/2018gl079423
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  Atmospheric Infrared Sounder (AIRS) data from the tropical oceans (30°N to 30°S) are used to derive the probability of the process resulting in deep convective clouds (DCCs) as function of the sea surface temperature (SST). For DCC at or below the tropopause the onset temperature of this process shifts at the same rate as the increase in the mean SST. For tropopause overshooting DCC, which are associated with extreme rain events, the shift of the onset temperature is slower, causing their frequency to increase by about 21%/K of warming of the oceans. This sensitivity is not inconsistent with the sensitivity of the increase of extreme deep convective rain in the National Center for Atmospheric Research Community Atmosphere Model version 5 model for a warmer SST. The mean of the 36 fifth Phase of the Coupled Model Intercomparison Project models predicts a 2.7 K warmer tropical SST by the end of this century, resulting in a 60% increases in the frequency of tropopause overshooting DCC.
• Aumann, H. H., Behrangi, A., & Wang, Y. (2018). Increased Frequency of Extreme Tropical Deep Convection: AIRS Observations and Climate Model Predictions. Geophysical Research Letters.
• Barkhordarian, A., Storch, H., Behrangi, A., Loikith, P. C., Mechoso, C. R., & Detzer, J. (2018). Simultaneous regional detection of land-use changes and elevated GHG levels: the case of spring precipitation in tropical South America. Geophysical Research Letters, 45(12), 6262--6271.
• Barkhordarian, A., von Storch, H., Behrangi, A., Loikith, P. C., Mechoso, C. R., & Detzer, J. (2018). Simultaneous Regional Detection of Land-Use Changes and Elevated GHG Levels: The Case of Spring Precipitation in Tropical South America. Geophysical Research Letters, 45(Issue 12). doi:10.1029/2018gl078041
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  A decline in dry season precipitation over tropical South America has a large impact on ecosystem health of the region. Results here indicate that the magnitude of negative trends in dry season precipitation in the past decades exceeds the estimated range of trends due to natural variability of the climate system defined in both the preindustrial climate and during the 850–1850 millennium. The observed drying is associated with an increase in vapor pressure deficit. The univariate detection analysis shows that greenhouse gas (GHG) forcing has a systematic influence in negative 30-year trends of precipitation ending in 1998 and later on. The bivariate attribution analysis demonstrates that forcing by elevated GHG levels and land-use change are attributed as key causes for the observed drying during 1983–2012 over the southern Amazonia and central Brazil. We further show that the effect of GS signal (GHG and sulfate aerosols) based on RCP4.5 scenario already has a detectable influence in the observed drying. Thus, we suggest that the recently observed “drier dry season” is a feature which will continue and intensify in the course of unfolding anthropogenic climate change. Such change could have profound societal and ecosystem impacts over the region.
• Behrangi, A., & Hsu, K. (2010). B. Imam, K. Hsu, S. Sorooshian, TJ Bellerby, and GJ Huffman, 2010: REFAME: Rain estimation using forwardadjusted advection of microwave estimates. J. Hydrometeorology, 11, 1305--1321.
• Behrangi, A., & Richardson, M. (2018). Observed High-Latitude Precipitation Amount and Pattern and CMIP5 Model Projections. Remote Sensing, 10(10), 1583.
• Behrangi, A., & Richardson, M. (2018). Observed high-latitude precipitation amount and pattern and CMIP5 model projections. Remote Sensing, 10(Issue 10). doi:10.3390/rs10101583
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  Utilizing reanalysis and high sensitivityW-band radar observations from CloudSat, this study assesses simulated high-latitude (55-82.5°) precipitation and its future changes under the RCP8.5 global warming scenario. A subset of models was selected based on the smallest discrepancy relative to CloudSat and ERA-I reanalysis using a combined ranking for bias and spatial root mean square error (RMSE). After accounting for uncertainties introduced by internal variability due to CloudSat's limited four year day-night observation period, RMSE provides greater discrimination between the models than a typical mean state bias criterion. Over 1976-2005 to 2071-2100, colder months experience larger fractional modelled precipitation increases than warmer months, and the observation-constrained models generally report a larger response than the full ensemble. For everywhere except the Southern Hemisphere (SH55, for 55-82.5°S) ocean, the selected models show greater warming than the model ensemble while their hydrological sensitivity (fractional precipitation change with temperature) is indistinguishable from the full ensemble relationship. This indicates that local thermodynamic effects explain much of the net high-latitude precipitation change. For the SH ocean, the models that perform best in the present climate show near-median warming but greater precipitation increase, implying a detectable contribution from processes other than local thermodynamic changes. A Taylor diagram analysis of the full CMIP5 ensemble finds that the Northern Hemisphere (NH55) and SH55 land areas follow a "wet get wetter" paradigm. The SH55 land areas show stable spatial correlations between the simulated present and future climate, indicative of small changes in the spatial pattern, but this is not true of NH55 land. This shows changes in the spatial pattern of precipitation changes through time as well as the differences in precipitation between wet and dry regions.
• Behrangi, A., Bormann, K. J., & Painter, T. H. (2018). Using the Airborne Snow Observatory to assess remotely sensed snowfall products in the California Sierra Nevada. Water Resources Research, 54(10), 7331--7346.
• Behrangi, A., Bormann, K. J., & Painter, T. H. (2018). Using the Airborne Snow Observatory to assess remotely sensed snowfall products in the California Sierra Nevada. Water Resources Research.
• Behrangi, A., Gardner, A., Reager, J. T., Fisher, J. B., Yang, D., Huffman, G. J., & Adler, R. F. (2018). Using GRACE to Estitmate Snowfall Accumulation and Assess Gauge Undercatch Corrections in High Latitudes. Journal of Climate, 31(21), 8689--8704.
• Behrangi, A., Yin, X., Rajagopal, S., Stampoulis, D., & Ye, H. (2018). On distinguishing snowfall from rainfall using near-surface atmospheric information: comparative analysis, uncertainties, and hydrologic importance. Quarterly Journal of the Royal Meteorological Society.
• Eswar, R., Das, N. N., Poulsen, C., Behrangi, A., Swigart, J., Svoboda, M., Entekhabi, D., Yueh, S., Doorn, B., & Entin, J. (2018). SMAP soil moisture change as an indicator of drought conditions. Remote Sensing, 10(Issue 5). doi:10.3390/rs10050788
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  Soil moisture is considered a key variable in drought analysis. The soil moisture dynamics given by the change in soil moisture between two time periods can provide information on the intensification or improvement of drought conditions. The aim of this work is to analyze how the soil moisture dynamics respond to changes in drought conditions over multiple time intervals. The change in soil moisture estimated from the Soil Moisture Active Passive (SMAP) satellite observations was compared with the United States Drought Monitor (USDM) and the Standardized Precipitation Index (SPI) over the contiguous United States (CONUS). The results indicated that the soil moisture change over 13-week and 26-week intervals is able to capture the changes in drought intensity levels in the USDM, and the change over a four-week interval correlated well with the one-month SPI values. This suggested that a short-term negative soil moisture change may indicate a lack of precipitation, whereas a persistent long-term negative soil moisture change may indicate severe drought conditions. The results further indicate that the inclusion of soil moisture change will add more value to the existing drought-monitoring products.
• Rajasekaran, E., Das, N. N., Poulsen, C., Behrangi, A., Swigart, J., Svoboda, M., Entekhabi, D., Yueh, S., Doorn, B., & Entin, J. (2018). SMAP Soil Moisture Change as an Indicator of Drought Conditions. Remote Sensing, 10(5), 788.
• Singh, A., Behrangi, A., Fisher, J. B., & Reager, J. T. (2018). On the Desiccation of the South Aral Sea Observed from Spaceborne Missions. Remote Sensing, 10(5), 793.
• Singh, A., Behrangi, A., Fisher, J. B., & Reager, J. T. (2018). On the desiccation of the South Aral Sea observed from spaceborne missions. Remote Sensing, 10(Issue 5). doi:10.3390/rs10050793
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  The South Aral Sea has been massively affected by the implementation of a mega-irrigation project in the region, but ground-based observations have monitored the Sea poorly. This study is a comprehensive analysis of the mass balance of the South Aral Sea and its basin, using multiple instruments from ground and space. We estimate lake volume, evaporation from the lake, and the Amu Darya streamflow into the lake using strengths offered by various remote-sensing data. We also diagnose the attribution behind the shrinking of the lake and its possible future fate. Terrestrial water storage (TWS) variations observed by the Gravity Recovery and Climate Experiment (GRACE) mission from the Aral Sea region can approximate water level of the East Aral Sea with good accuracy (1.8% normalized root mean square error (RMSE), and 0.9 correlation) against altimetry observations. Evaporation from the lake is back-calculated by integrating altimetry-based lake volume, in situ streamflow, and Global Precipitation Climatology Project (GPCP) precipitation. Different evapotranspiration (ET) products (Global Land Data Assimilation System (GLDAS), the Water Gap Hydrological Model (WGHM)), and Moderate-Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16) significantly underestimate the evaporation from the lake. However, another MODIS based Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) ET estimate shows remarkably high consistency (0.76 correlation) with our estimate (based on the water-budget equation). Further, streamflow is approximated by integrating lake volume variation, PT-JPL ET, and GPCP datasets. In another approach, the deseasonalized GRACE signal from the Amu Darya basin was also found to approximate streamflow and predict extreme flow into the lake by one or two months. They can be used for water resource management in the Amu Darya delta. The spatiotemporal pattern in the Amu Darya basin shows that terrestrial water storage (TWS) in the central region (predominantly in the primary irrigation belt other than delta) has increased. This increase can be attributed to enhanced infiltration, as ET and vegetation index (i.e., normalized difference vegetation index (NDVI)) from the area has decreased. The additional infiltration might be an indication of worsening of the canal structures and leakage in the area. The study shows how altimetry, optical images, gravimetric and other ancillary observations can collectively help to study the desiccating Aral Sea and its basin. A similar method can be used to explore other desiccating lakes.
• Tang, G., Behrangi, A., Long, D. i., Li, C., & Hong, Y. (2018). Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products. Journal of Hydrology, 559, 294--306.
• Tang, G., Behrangi, A., Long, D., Li, C., & Hong, Y. (2018). Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products. Journal of Hydrology, 559(Issue). doi:10.1016/j.jhydrol.2018.02.057
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  Rain gauge observations are commonly used to evaluate the quality of satellite precipitation products. However, the inherent difference between point-scale gauge measurements and areal satellite precipitation, i.e. a point of space in time accumulation v.s. a snapshot of time in space aggregation, has an important effect on the accuracy and precision of qualitative and quantitative evaluation results. This study aims to quantify the uncertainty caused by various combinations of spatiotemporal scales (0.1°–0.8° and 1–24 h) of gauge network designs in the densely gauged and relatively flat Ganjiang River basin, South China, in order to evaluate the state-of-the-art satellite precipitation, the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). For comparison with the dense gauge network serving as “ground truth”, 500 sparse gauge networks are generated through random combinations of gauge numbers at each set of spatiotemporal scales. Results show that all sparse gauge networks persistently underestimate the performance of IMERG according to most metrics. However, the probability of detection is overestimated because hit and miss events are more likely fewer than the reference numbers derived from dense gauge networks. A nonlinear error function of spatiotemporal scales and the number of gauges in each grid pixel is developed to estimate the errors of using gauges to evaluate satellite precipitation. Coefficients of determination of the fitting are above 0.9 for most metrics. The error function can also be used to estimate the required minimum number of gauges in each grid pixel to meet a predefined error level. This study suggests that the actual quality of satellite precipitation products could be better than conventionally evaluated or expected, and hopefully enables non-subject-matter-expert researchers to have better understanding of the explicit uncertainties when using point-scale gauge observations to evaluate areal products.
• Tang, G., Behrangi, A., Ma, Z., Long, D. i., & Hong, Y. (2018). Downscaling of ERA-Interim Temperature in the Contiguous United States and Its Implications for Rain--Snow Partitioning. Journal of Hydrometeorology, 19(7), 1215--1233.
• Tang, G., Long, D. i., Behrangi, A., Wang, C., & Hong, Y. (2018). Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data. Water Resources Research, 54(10), 8253--8278.
• Tang, G., Long, D., Behrangi, A., Wang, C., & Hong, Y. (2018). Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data. Water Resources Research, 54(Issue 10). doi:10.1029/2018wr023830
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  Satellite remote sensing is able to provide information on global rain and snow, but challenges remain in accurate estimation of precipitation rates, particularly in snow retrieval. In this work, the deep neural network (DNN) is applied to estimate rain and snow rates in high latitudes. The reference data for DNN training are provided by two spaceborne radars onboard the Global Precipitation Measurement (GPM) Core Observatory and CloudSat. Passive microwave data from the GPM Microwave Imager (GMI), infrared data from MODerate resolution Imaging Spectroradiometer and environmental data from European Centre for Medium-Range Weather Forecasts are trained to the spaceborne radar-based reference precipitation. The DNN estimates are compared to data from the Goddard Profiling Algorithm (GPROF), which is used to retrieve passive microwave precipitation for the GPM mission. First, the DNN-based retrieval method performs well in both training and testing periods. Second, the DNN can reveal the advantages and disadvantages of different channels of GMI and MODerate resolution Imaging Spectroradiometer. Additionally, infrared and environmental data can improve precipitation estimation of the DNN, particularly for snowfall. Finally, based on the optimized DNN, rain and snow are estimated in 2017 from orbital GMI brightness temperatures and compared to ERA-Interim and Modern-Era Retrospective analysis for Research and Applications Version 2 reanalysis data. Evaluation results show that (1) the DNN can largely mitigate the underestimation of precipitation rates in high latitudes by GPROF; (2) the DNN-based snowfall estimates largely outperform those of GPROF; and (3) the spatial distributions of DNN-based precipitation are closer to reanalysis data. The method and assessment presented in this study could potentially contribute to the substantial improvement of satellite precipitation products in high latitudes.
• Wen, Y., Behrangi, A., Chen, H., & Lambrigtsen, B. (2018). How well were the early 2017 California Atmospheric River precipitation events captured by satellite products and ground-based radars?. Quarterly Journal of the Royal Meteorological Society, 144(Issue). doi:10.1002/qj.3253
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  In January and February of 2017, California experienced multiple heavy storms that caused serious destruction of facilities and economic loss, although it also helped to reduce water storage deficit due to prolonged drought in previous years. These extreme precipitation events were mainly associated with Atmospheric Rivers (ARs) and brought about 174 km3 of water to California according to ground observations. This article evaluates the performance of six commonly used satellite-based precipitation products (IMERG, 3B42RT, PERSIANN, CCS, CMORPH and GSMaP), as well as ground-based radar products (Radar-only and Radar-lgc) in capturing the ARs precipitation rate and distribution. It is found that precipitation maps from all products present heavy precipitation in January and February, with more consistent observations over ocean than land. Though large uncertainties exist in quantitative precipitation estimation (QPE) over land, the ensemble mean of different remote-sensing precipitation products over California is consistent with gauge measurements. Among the six satellite-based products, IMERG correlates the best with gauge observations both in the detection and quantification of precipitation, but it is not the best product in terms of root-mean-square error or bias. Compared to satellite products, ground weather radar shows better precipitation detectability and estimation skill. However, neither radar nor satellite QPE products have good performances in quantifying the peak precipitation intensity during the extreme events, suggesting that further advancement in quantification of extremely intense precipitation associated with ARs in the western United States is needed.
• Wong, S., & Behrangi, A. (2018). Regime-Dependent Differences in Surface Freshwater Exchange Estimates Over the Ocean. Geophysical Research Letters, 45(2), 955--963.
• Andreadis, K. M., Das, N., Stampoulis, D., Ines, A., Fisher, J. B., Granger, S., Kawata, J., Han, E., & Behrangi, A. (2017). The Regional Hydrologic Extremes Assessment System: A software framework for hydrologic modeling and data assimilation. PloS one, 12(5), e0176506.
• Andreadis, K. M., Das, N., Stampoulis, D., Ines, A., Fisher, J. B., Granger, S., Kawata, J., Han, E., & Behrangi, A. (2017). The regional hydrologic extremes assessment system: A software framework for hydrologic modeling and data assimilation. PLoS ONE, 12(Issue 5). doi:10.1371/journal.pone.0176506
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  The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications.
• Behrangi, A., & Wen, Y. (2017). On the Spatial and Temporal Sampling Errors of Remotely Sensed Precipitation Products. Remote Sensing, 9(11), 1127.
• Behrangi, A., Gardner, A. S., Reager, J. T., & Fisher, J. B. (2017). Using GRACE to constrain precipitation amount over cold mountainous basins. Geophysical Research Letters, 44(1), 219--227.
• Behrangi, A., Gardner, A. S., Reager, J. T., & Fisher, J. B. (2017). Using GRACE to constrain precipitation amount over cold mountainous basins. Geophysical Research Letters, 44(Issue 1). doi:10.1002/2016gl071832
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  Despite the importance for hydrology and climate-change studies, current quantitative knowledge on the amount and distribution of precipitation in mountainous and high-elevation regions is limited due to instrumental and retrieval shortcomings. Here by focusing on two large endorheic basins in High Mountain Asia, we show that satellite gravimetry (Gravity Recovery and Climate Experiment (GRACE)) can be used to provide an independent estimate of monthly accumulated precipitation using mass balance equation. Results showed that the GRACE-based precipitation estimate has the highest agreement with most of the commonly used precipitation products in summer, but it deviates from them in cold months, when the other products are expected to have larger errors. It was found that most of the products capture about or less than 50% of the total precipitation estimated using GRACE in winter. Overall, Global Precipitation Climatology Project (GPCP) showed better agreement with GRACE estimate than other products. Yet on average GRACE showed ~30% more annual precipitation than GPCP in the study basins. In basins of appropriate size with an absence of dense ground measurements, as is a typical case in cold mountainous regions, we find GRACE can be a viable alternative to constrain monthly and seasonal precipitation estimates from other remotely sensed precipitation products that show large bias.
• Herold, N., Behrangi, A., & Alexander, L. V. (2017). Large uncertainties in observed daily precipitation extremes over land. Journal of Geophysical Research: Atmospheres, 122(2), 668--681.
• Herold, N., Behrangi, A., & Alexander, L. V. (2017). Large uncertainties in observed daily precipitation extremes over land. Journal of Geophysical Research, 122(Issue 2). doi:10.1002/2016jd025842
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  We explore uncertainties in observed daily precipitation extremes over the terrestrial tropics and subtropics (50°S–50°N) based on five commonly used products: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset, the Global Precipitation Climatology Centre-Full Data Daily (GPCC-FDD) dataset, the Tropical Rainfall Measuring Mission (TRMM) multi-satellite research product (T3B42 v7), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and the Global Precipitation Climatology Project’s One-Degree Daily (GPCP-1DD) dataset. We use the precipitation indices R10mm and Rx1day, developed by the Expert Team on Climate Change Detection and Indices, to explore the behavior of “moderate” and “extreme” extremes, respectively. In order to assess the sensitivity of extreme precipitation to different grid sizes we perform our calculations on four common spatial resolutions (0.25° × 0.25°, 1° × 1°, 2.5° × 2.5°, and 3.75° × 2.5°). The impact of the chosen “order of operation” in calculating these indices is also determined. Our results show thatmoderate extremes are relatively insensitive to product and resolution choice, while extreme extremes can be very sensitive. For example, at 0.25° × 0.25° quasi-global mean Rx1day values vary from 37mmin PERSIANN-CDR to 62mm in T3B42. We find that the interproduct spread becomes prominent at resolutions of 1° × 1° and finer, thus establishing a minimum effective resolution at which observational products agree. Without improvements in interproduct spread, these exceedingly large observational uncertainties at high spatial resolution may limit the usefulness of model evaluations. As has been found previously, resolution sensitivity can be largely eliminated by applying an order of operation where indices are calculated prior to regridding. However, this approach is not appropriate when true area averages are desired (e.g., for model evaluations).
• Wen, Y., Behrangi, A., Chen, H., & Lambrigtsen, B. (2017). How Well the Early 2017 California Atmospheric River Precipitation Events Were Captured by Satellite Products and Ground-based Radars?. Quarterly Journal of the Royal Meteorological Society.
• Wen, Y., Kirstetter, P., Gourley, J. J., Hong, Y., Behrangi, A., & Flamig, Z. (2017). Evaluation of MRMS Snowfall Products over the Western United States. Journal of Hydrometeorology, 18(6), 1707--1713.
• Wen, Y., Kirstetter, P., Gourley, J. J., Hong, Y., Behrangi, A., & Flamig, Z. (2017). Evaluation of MRMS snowfall products over the Western United States. Journal of Hydrometeorology, 18(Issue 6). doi:10.1175/jhm-d-16-0266.1
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  Snow is important to water resources and is of critical importance to society. Ground-weather-radar-based snowfall observations have been highly desirable for large-scale weather monitoring and water resources applications. This study conducts an evaluation of the Multi-Radar Multi-Sensor (MRMS) quantitative estimates of snow rate using the Snowpack Telemetry (SNOTEL) daily snow water equivalent (SWE) datasets. A detectability evaluation shows that MRMS is limited in detecting very light snow (daily snow accumulation < 5 mm) because of the quality control module in MRMS filtering out weak signals (< 5 dBZ). For daily snow accumulation greater than 10 mm, MRMS has good detectability. The quantitative comparisons reveal a bias of -77.37% between MRMS and SNOTEL. A majority of the underestimation bias occurs in relatively warm conditions with surface temperatures ranging from -10° to 0°C. A constant reflectivity-SWE intensity relationship does not capture the snow mass flux increase associated with denser snow particles at these relatively warm temperatures. There is no clear dependence of the bias on radar beam height. The findings in this study indicate that further improvement in radar snowfall products might occur by deriving appropriate reflectivity-SWE relationships considering the degree of riming and snowflake size.
• Yanovsky, I., Behrangi, A., Wen, Y., Schreier, M., Dang, V., & Lambrigtsen, B. (2017). Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data. Remote Sensing, 9(11), 1097.
• Yanovsky, I., Behrangi, A., Wen, Y., Schreier, M., Dang, V., & Lambrigtsen, B. (2017). Enhanced resolution of microwave sounder imagery through fusion with infrared sensor data. Remote Sensing, 9(Issue 11). doi:10.3390/rs9111097
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  The images acquired by microwave sensors are blurry and have low resolution. On the other hand, the images obtained using infrared/visible sensors are often of higher resolution. In this paper, we develop a data fusion methodology and apply it to enhance the resolution of a microwave image using the data from a collocated infrared/visible sensor. Such an approach takes advantage of the spatial resolution of the infrared instrument and the sensing accuracy of the microwave instrument. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. We tested our method using a precipitation scene captured with the Advanced Microwave Sounding Unit (AMSU-B) microwave instrument and the Advanced Very High Resolution Radiometer (AVHRR) infrared instrument and compared the results to simultaneous radar observations. We show that the data fusion product is better than the original AMSU-B and AVHRR observations across all statistical indicators.
• Zhang, G., Yao, T., Shum, C. K., Yi, S., Yang, K., Xie, H., Feng, W., Bolch, T., Wang, L., Behrangi, A., & others, . (2017). Lake volume and groundwater storage variations in Tibetan Plateau's endorheic basin. Geophysical Research Letters, 44(11), 5550--5560.
• Zhang, G., Yao, T., Shum, C. K., Yi, S., Yang, K., Xie, H., Feng, W., Bolch, T., Wang, L., Behrangi, A., Zhang, H., Wang, W., Xiang, Y., & Yu, J. (2017). Lake volume and groundwater storage variations in Tibetan Plateau's endorheic basin. Geophysical Research Letters, 44(Issue 11). doi:10.1002/2017gl073773
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  The Tibetan Plateau (TP), the highest and largest plateau in the world, with complex and competing cryospheric-hydrologic-geodynamic processes, is particularly sensitive to anthropogenic warming. The quantitative water mass budget in the TP is poorly known. Here we examine annual changes in lake area, level, and volume during 1970s–2015. We find that a complex pattern of lake volume changes during 1970s–2015: a slight decrease of −2.78 Gt yr−1 during 1970s–1995, followed by a rapid increase of 12.53 Gt yr−1 during 1996–2010, and then a recent deceleration (1.46 Gt yr−1) during 2011–2015. We then estimated the recent water mass budget for the Inner TP, 2003–2009, including changes in terrestrial water storage, lake volume, glacier mass, snow water equivalent (SWE), soil moisture, and permafrost. The dominant components of water mass budget, namely, changes in lake volume (7.72 ± 0.63 Gt yr−1) and groundwater storage (5.01 ± 1.59 Gt yr−1), increased at similar rates. We find that increased net precipitation contributes the majority of water supply (74%) for the lake volume increase, followed by glacier mass loss (13%), and ground ice melt due to permafrost degradation (12%). Other term such as SWE (1%) makes a relatively small contribution. These results suggest that the hydrologic cycle in the TP has intensified remarkably during recent decades.
• Alexander, L. V., Zhang, X., Hegerl, G., Seneviratne, S. I., Behrangi, A., Fischer, E., Martius, O., Otto, F., Sillmann, J., & Vautard, R. (2016). Implementation Plan for WCRP Grand Challenge on Understanding and Predicting Weather and Climate Extremes--the “Extremes Grand Challenge”. Version, June.
• Behrangi, A., Christensen, M., Richardson, M., Lebsock, M., Stephens, G., Huffman, G. J., Bolvin, D., Adler, R. F., Gardner, A., Lambrigtsen, B., & Fetzer, E. (2016). Status of high-latitude precipitation estimates from observations and reanalyses. Journal of Geophysical Research, 121(Issue 9). doi:10.1002/2015jd024546
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  An intercomparison of high-latitude precipitation characteristics from observation-based and reanalysis products is performed. In particular, the precipitation products from CloudSat provide an independent assessment to other widely used products, these being the observationally based Global Precipitation Climatology Project (GPCP), Global Precipitation Climatology Centre, and Climate Prediction Center Merged Analysis of Precipitation (CMAP) products and the ERA-Interim, Modern-Era Retrospective Analysis for Research and Applications (MERRA), and National Centers for Environmental Prediction-Department of Energy Reanalysis 2 (NCEP-DOE R2) reanalyses. Seasonal and annual total precipitation in both hemispheres poleward of 55° latitude are considered in all products, and CloudSat is used to assess intensity and frequency of precipitation occurrence by phase, defined as rain, snow, or mixed phase. Furthermore, an independent estimate of snow accumulation during the cold season was calculated from the Gravity Recovery and Climate Experiment. The intercomparison is performed for the 2007-2010 period when CloudSat was fully operational. It is found that ERA-Interim and MERRA are broadly similar, agreeing more closely with CloudSat over oceans. ERA-Interim also agrees well with CloudSat estimates of snowfall over Antarctica where total snowfall from GPCP and CloudSat is almost identical. A number of disagreements on regional or seasonal scales are identified: CMAP reports much lower ocean precipitation relative to other products, NCEP-DOE R2 reports much higher summer precipitation over Northern Hemisphere land, GPCP reports much higher snowfall over Eurasia, and CloudSat overestimates precipitation over Greenland, likely due to mischaracterization of rain and mixed-phase precipitation. These outliers are likely unrealistic for these specific regions and time periods. These estimates from observations and reanalyses provide useful insights for diagnostic assessment of precipitation products in high latitudes, quantifying the current uncertainties, improving the products, and establishing a benchmark for assessment of climate models.
• Behrangi, A., Christensen, M., Richardson, M., Lebsock, M., Stephens, G., Huffman, G. J., Bolvin, D., Adler, R. F., Gardner, A., Lambrigtsen, B., & others, . (2016). Status of high-latitude precipitation estimates from observations and reanalyses. Journal of Geophysical Research: Atmospheres, 121(9), 4468--4486.
• Behrangi, A., Fetzer, E. J., & Granger, S. L. (2016). Early detection of drought onset using near surface temperature and humidity observed from space. International Journal of Remote Sensing, 37(16), 3911--3923.
• Behrangi, A., Fetzer, E. J., & Granger, S. L. (2016). Early detection of drought onset using near surface temperature and humidity observed from space. International Journal of Remote Sensing, 37(Issue 16). doi:10.1080/01431161.2016.1204478
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  Drought is associated with severe societal impacts ranging from shortages of water for human consumption to agricultural failure and famine. An important aspect of drought forecast is determining the onset, which is critical for early warning efforts and water resources and agriculture planning. Indices of precipitation shortage have been widely used to detect the onset of drought because precipitation deficits often lead to shortages in other hydrologic variables such as soil moisture and runoff. The present work demonstrates that atmospheric temperature and humidity observations from the Atmospheric Infrared Sounder (AIRS) contain information that can be used to detect drought onset earlier than that obtained from precipitation deficit. By calculating the standardized indices for precipitation, near-surface temperature, vapour pressure deficit, and relative humidity, we show that in many regions of the world signals of drought onset can be detected from near-surface temperature and humidity data a few months earlier than those obtained from precipitation deficit. In particular, vapour pressure deficit showed higher effectiveness than relative humidity or temperature only. The outcome was generally consistent for the three- and six-month accumulations studied here. Further analysis using 65 years (1960–2014) of monthly temperature and humidity data derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) data set over the continental United States suggests that there is a good agreement between drought early detection signals obtained from AIRS and that from ground stations during the overlapped (2003–2014) period. Analysis using longer record suggests that the frequency of successful early detection of drought onset using temperature and humidity data shows regional shift towards eastern United States in the recent years.
• Behrangi, A., Guan, B., Neiman, P. J., Schreier, M., & Lambrigtsen, B. (2016). On the quantification of atmospheric rivers precipitation from space: Composite assessments and case studies over the eastern North Pacific Ocean and the western united states. Journal of Hydrometeorology, 17(1), 369--382.
• Behrangi, A., Guan, B., Neiman, P. J., Schreier, M., & Lambrigtsen, B. (2016). On the quantification of atmospheric rivers precipitation from space: Composite assessments and case studies over the eastern north pacific ocean and the Western United States. Journal of Hydrometeorology, 17(Issue 1). doi:10.1175/jhm-d-15-0061.1
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  Atmospheric rivers (ARs) are often associated with extreme precipitation, which can lead to flooding or alleviate droughts. A decade (2003-12) of landfalling ARs impacting the North American west coast (between 32.5° and 52.5°N) is collected to assess the skill of five commonly used satellite-based precipitation products [T3B42, T3B42 real-time (T3B42RT), CPC morphing technique (CMORPH), PERSIANN, and PERSIANN-Cloud Classification System (CCS)] in capturing ARs' precipitation rate and pattern. AR detection was carried out using a database containing twice-daily satellite-based integrated water vapor composite observations. It was found that satellite products are more consistent over ocean than land and often significantly underestimate precipitation rate over land compared to ground observations. Incorrect detection of precipitation from IR-based methods is prevalent over snow and ice surfaces where microwave estimates often show underestimation or missing data. Bias adjustment using ground observation is found very effective to improve satellite products, but it also raises concern regarding near-real-time applicability of satellite products for ARs. The analysis using individual case studies (6-8 January and 13-14 October 2009) and an ensemble of AR events suggests that further advancement in capturing orographic precipitation and precipitation over cold and frozen surfaces is needed to more reliably quantify AR precipitation from space.
• Chen, S., Behrangi, A., Tian, Y., Hu, J., Hong, Y., Tang, Q., Hu, X., Stepanian, P. M., Hu, B., & Zhang, X. (2016). Precipitation spectra analysis over China with high-resolution measurements from optimally-merged satellite/gauge observations—Part II: Diurnal variability analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7), 2979--2988.
• Chen, S., Hong, Y., Kulie, M., Behrangi, A., Stepanian, P. M., Cao, Q., You, Y., Zhang, J., Hu, J., & Zhang, X. (2016). Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL Multi-Radar Multi-Sensor System. Journal of Hydrology, 541(Issue). doi:10.1016/j.jhydrol.2016.07.047
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  The latest global snowfall product derived from the CloudSat Cloud Profiling Radar (2C-SNOW-PROFILE) is compared with NOAA/National Severe Storms Laboratory's Multi-Radar Multi-Sensor (MRMS/Q3) system precipitation products from 2009 through 2010. The results show that: (1) Compared to Q3, CloudSat tends to observe more extremely light snowfall events (
• Chen, S., Hong, Y., Kulie, M., Behrangi, A., Stepanian, P. M., Cao, Q., You, Y., Zhang, J., Hu, J., & Zhang, X. (2016). Comparison of snowfall estimates from the NASA CloudSat cloud profiling radar and NOAA/NSSL multi-radar multi-sensor system. Journal of Hydrology, 541, 862--872.
• Chen, S., Tian, Y., Behrangi, A., Hu, J., Hong, Y., Zhang, Z., Stepanian, P. M., Hu, B., & Zhang, X. (2016). Precipitation Spectra Analysis over China with High-Resolution Measurements from Optimally Merged Satellite/Gauge Observations - Part I: Spatial and Seasonal Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(Issue 7). doi:10.1109/jstars.2016.2529003
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  Precipitation amount (PA), frequency (PF), and intensity (PI) over China are characterized and quantified using a high-resolution merged satellite-gauge precipitation product for 6 years (January 2008 through December 2013). The precipitation product synthesizes both state-of-the-art multisatellite precipitation algorithms and the latest, densest gauge observations to provide high-quality precipitation information at a very fine temporal and spatial resolution (0.1°/hourly) that encompasses all of China. The geographical and seasonal variations in precipitation are systematically documented over seven subregions, each corresponding to a unique climate regime. PA, PF, and PI have large seasonal and geographical variations across China. It is found that 1) although heavy precipitation events (>10 mm/h) represent only 0.8% of total precipitation occurrence over China, they contribute 12.1% of the total precipitation volume. Light precipitation events (
• Christensen, M. W., Behrangi, A., L’ecuyer, T. S., Wood, N. B., Lebsock, M. D., & Stephens, G. L. (2016). Arctic observation and reanalysis integrated system: A new data product for validation and climate study. Bulletin of the American Meteorological Society, 97(6), 907--916.
• Guo, H., Chen, S., Bao, A., Behrangi, A., Hong, Y., Ndayisaba, F., Hu, J., & Stepanian, P. M. (2016). Early assessment of Integrated Multi-satellite Retrievals for Global Precipitation Measurement over China. Atmospheric Research, 176-177(Issue). doi:10.1016/j.atmosres.2016.02.020
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  Two post-real time precipitation products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) are systematically evaluated over China with China daily Precipitation Analysis Product (CPAP) as reference. The IMERG products include the gauge-corrected IMERG product (IMERG_Cal) and the version of IMERG without direct gauge correction (IMERG_Uncal). The post-research TRMM Multisatellite Precipitation Analysis version 7 (TMPA-3B42V7) is also evaluated concurrently with IMERG for better perspective. In order to be consistent with CPAP, the evaluation and comparison of selected products are performed at 0.25° and daily resolutions from 12 March 2014 through 28 February 2015.The results show that: Both IMERG and 3B42V7 show similar performances. Compared to IMERG_Uncal, IMERG_Cal shows significant improvement in overall and conditional bias and in the correlation coefficient. Both IMERG_Cal and IMERG_Uncal perform relatively poor in winter and over-detect slight precipitation events in northwestern China. As an early validation of the GPM-era IMERG products that inherit the TRMM-era global satellite precipitation products, these findings will provide useful feedbacks and insights for algorithm developers and data users over China and beyond.
• Guo, H., Chen, S., Bao, A., Behrangi, A., Hong, Y., Ndayisaba, F., Hu, J., & Stepanian, P. M. (2016). Early assessment of integrated multi-satellite retrievals for global precipitation measurement over China. Atmospheric Research, 176, 121--133.
• Stampoulis, D., Andreadis, K. M., Granger, S. L., Fisher, J. B., Turk, F. J., Behrangi, A., Ines, A. V., & Das, N. N. (2016). Assessing hydro-ecological vulnerability using microwave radiometric measurements from WindSat. Remote Sensing of Environment, 184(Issue). doi:10.1016/j.rse.2016.06.007
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  The spatial distribution, magnitude and timing of precipitation events are being altered globally, often leading to extreme hydrologic conditions with serious implications to the environment and society. Motivated by the pressing need to understand, from a hydro-ecological perspective, the impact of the dynamic nature of the hydrologic cycle on the environment in water-stressed regions, we investigated how different habitats in East Africa behave under extreme hydrologic conditions. We assessed the hydro-ecological vulnerability of the region by studying the response of soil moisture and vegetation water content to precipitation deficiency. The spatial patterns and characteristics of the inter-relations among the three aforementioned hydrologic variables, as well as the sensitivity and resilience of vegetation water content and soil moisture, derived from WindSat, were investigated for different vegetation types during dry spells of varying duration, identified using the Tropical Rainfall Measuring Mission (TRMM), in 2003-2011. Forest/Woody Savanna (FWS) and Savanna/Grasslands (SG) are more sensitive to local hydrologic extremes, while Shrublands (SHR) and the soils that support it are the least impacted by these conditions. SG and FWS exhibit the highest vegetation water content resilience, whereas soil moisture persistence during dry spells is at its highest in SHR/SG. The environmental variability, illustrated by the spatial patterns of the aforementioned hydrologic properties, can potentially play a role in the enhancement of resilience. This study provides critical insight into the hydro-ecological vulnerability of East Africa using microwave remote sensing, and this information can be used towards advancing management and decision support systems that would improve societal well-being and economic development.
• Stampoulis, D., Andreadis, K. M., Granger, S. L., Fisher, J. B., Turk, F. J., Behrangi, A., Ines, A. V., & Das, N. N. (2016). Assessing hydro-ecological vulnerability using microwave radiometric measurements from WindSat. Remote Sensing of Environment, 184, 58--72.
• Stephens, G. L., Hakuba, M. Z., Hawcroft, M., Haywood, J. M., Behrangi, A., Kay, J. E., & Webster, P. J. (2016). The curious nature of the hemispheric symmetry of the Earth’s water and energy balances. Current Climate Change Reports, 2(4), 135--147.
• Tapiador, F. J., Behrangi, A., Haddad, Z. S., Katsanos, D., & Castro, M. (2016). Disruptions in precipitation cycles: Attribution to anthropogenic forcing. Journal of Geophysical Research: Atmospheres, 121(5), 2161--2177.
• Tapiador, F. J., Behrangi, A., Haddad, Z. S., Katsanos, D., & de Castro, M. (2016). Disruptions in precipitation cycles: Attribution to anthropogenic forcing. Journal of Geophysical Research, 121(Issue 5). doi:10.1002/2015jd023406
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  Disruptions of the spatiotemporal distribution of surface precipitation that are induced by global warming may affect Earth’s climate more significantly than changes in the total precipitation amount. Identifying such disruptions at global scales is not straightforward, as it requires disentangling a weak signal from comprehensive, gapless data in a 5-D configuration space whose dimensions are latitude, longitude, time, power, and period. Drawing on reliable, state-of-the-art climate model simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) experiments and using well-tested analytical methods, clear changes in the global precipitation cycles have been found for the simulated period 1862–2003. It has also been found that the disruptions may be attributable to anthropogenic forcing. The disruptions are relevant enough to envision significant changes in precipitation timing if human greenhouse gas emissions continue to accumulate in the future. It is noteworthy that the effects of anthropogenic forcings have been found not predominantly in the intra-annual cycles, i.e., in the short-term weather patterns that would be indicative of local effects, but rather in the interannual planetary long-term variability of the atmosphere. This suggests a global, distributed effect of the anthropogenic forcings on precipitation, which in turn is indicative of changes in the precipitation patterns linked with changes in the thermodynamics of the precipitation microphysics and to a lesser extent with the dynamical aspects of the precipitation processes.
• Thomas, B. F., Behrangi, A., & Famiglietti, J. S. (2016). Precipitation intensity effects on groundwater recharge in the southwestern United States. Water (Switzerland), 8(Issue 3). doi:10.3390/w8030090
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  Episodic recharge as a result of infrequent, high intensity precipitation events comprises the bulk of groundwater recharge in arid environments. Climate change and shifts in precipitation intensity will affect groundwater continuity, thus altering groundwater recharge. This study aims to identify changes in the ratio of groundwater recharge and precipitation, the R:P ratio, in the arid southwestern United States to characterize observed changes in groundwater recharge attributed to variations in precipitation intensity. Our precipitation metric, precipitation intensity magnification, was used to investigate the relationship between the R:P ratio and precipitation intensity. Our analysis identified significant changes in the R:P ratio concurrent with decreases in precipitation intensity. The results illustrate the importance of precipitation intensity in relation to groundwater recharge in arid regions and provide further insights for groundwater management in nonrenewable groundwater systems and in a changing climate.
• Thomas, B. F., Behrangi, A., & Famiglietti, J. S. (2016). Precipitation intensity effects on groundwater recharge in the southwestern United States. Water, 8(3), 90.
• Wen, Y., Behrangi, A., Lambrigtsen, B., & Kirstetter, P. (2016). Evaluation and uncertainty estimation of the latest radar and satellite snowfall products using SNOTEL measurements over mountainous regions in western United States. Remote Sensing, 8(11), 904.
• Wen, Y., Behrangi, A., Lambrigtsen, B., & Kirstetter, P. E. (2016). Evaluation and uncertainty estimation of the latest radar and satellite snowfall products using SNOTEL measurements over mountainous regions in western United States. Remote Sensing, 8(Issue 11). doi:10.3390/rs8110904
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  Snow contributes to regional and global water budgets, and is of critical importance to water resources management and our society. Along with advancement in remote sensing tools and techniques to retrieve snowfall, verification and refinement of these estimates need to be performed using ground-validation datasets. A comprehensive evaluation of the Multi-Radar/Multi-Sensor (MRMS) snowfall products and Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) precipitation products is conducted using the Snow Telemetry (SNOTEL) daily precipitation and Snow Water Equivalent (SWE) datasets. Severe underestimations are found in both radar and satellite products. Comparisons are conducted as functions of air temperature, snowfall intensity, and radar beam height, in hopes of resolving the discrepancies between measurements by remote sensing and gauge, and finally developing better snowfall retrieval algorithms in the future.
• Ye, H., Fetzer, E. J., Behrangi, A., Wong, S., Lambrigtsen, B. H., Wang, C. Y., Cohen, J., & Gamelin, B. L. (2016). Increasing daily precipitation intensity associated with warmer air temperatures over Northern Eurasia. Journal of Climate, 29(2), 623--636.
• Ye, H., Fetzer, E. J., Behrangi, A., Wong, S., Lambrigtsen, B. H., Wang, C. Y., Cohen, J., & Gamelin, B. L. (2016). Increasing daily precipitation intensity associated with warmer air temperatures over northern Eurasia. Journal of Climate, 29(Issue 2). doi:10.1175/jcli-d-14-00771.1
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  This study uses 45 years of observational records from 517 historical surface weather stations over northern Eurasia to examine changing precipitation characteristics associated with increasing air temperatures. Results suggest that warming air temperatures over northern Eurasia have been accompanied by higher precipitation intensity but lower frequency and little change in annual precipitation total. An increase in daily precipitation intensity of around 1%-3% per each degree of air temperature increase is found for all seasons as long as a station's seasonal mean air temperature is below about 15°-16°C. This threshold temperature may be location dependent. At temperatures above this threshold, precipitation intensity switches to decreasing with increasing air temperature, possibly related to decreasing water vapor associated with extreme high temperatures. Furthermore, the major atmospheric circulation of the Arctic Oscillation, Scandinavian pattern, east Atlantic-western Eurasian pattern, and polar-Eurasian pattern also have significant influences on precipitation intensity in winter, spring, and summer over certain areas of northern Eurasia.
• Behrangi, A., Loikith, P. C., Fetzer, E. J., Nguyen, H. M., & Granger, S. L. (2015). Utilizing humidity and temperature data to advance monitoring and prediction of meteorological drought. Climate, 3(4), 999--1017.
• Behrangi, A., Loikith, P. C., Fetzer, E. J., Nguyen, H. M., & Granger, S. L. (2015). Utilizing humidity and temperature data to advance monitoring and prediction of meteorological drought. Climate, 3(Issue 4). doi:10.3390/cli3040999
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  The fraction of land area over the Continental United States experiencing extreme hot and dry conditions has been increasing over the past several decades, consistent with expectation from anthropogenic climate change. A clear concurrent change in precipitation, however, has not been confirmed. Vapor pressure deficit (VPD), combining temperature and humidity, is utilized here as an indicator of the background atmospheric conditions associated with meteorological drought. Furthermore, atmospheric conditions associated with warm season drought events are assessed by partitioning associated VPD anomalies into the temperature and humidity components. This approach suggests that the concurrence of anomalously high temperature and low humidity was an important driver of the rapid development and evolution of the exceptionally severe 2011 Texas and the 2012 Great Plains droughts. By classification of a decade of extreme drought events and tracking them back in time, it was found that near surface atmospheric temperature and humidity add essential information to the commonly used precipitation-based drought indicators and can advance efforts to determine the timing of drought onset and its severity.
• Behrangi, A., Nguyen, H., & Granger, S. (2015). Probabilistic seasonal prediction of meteorological drought using the bootstrap and multivariate information. Journal of Applied Meteorology and Climatology, 54(7), 1510--1522.
• Behrangi, A., Nguyen, H., & Granger, S. (2015). Probabilistic seasonal prediction of meteorological drought using the bootstrap and multivariate information. Journal of Applied Meteorology and Climatology, 54(Issue 7). doi:10.1175/jamc-d-14-0162.1
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  In the present work, a probabilistic ensemble method using the bootstrap is developed to predict the future state of the standard precipitation index (SPI) commonly used for drought monitoring. The methodology is data driven and has the advantage of being easily extended to use more than one variable as predictors. Using 110 years of monthly observations of precipitaton, surface air temperature, and the Niño-3.4 index, the method was employed to assess the impact of the different variables in enhancing the prediction skill. A predictive probability density function (PDF) is produced for future 6-month SPI, and a log-likelihood skill score is used to cross compare various combination scenarios using the entire predictive PDF and with reference to the observed values set aside for validation. The results suggest that the multivariate prediction using complementary information from 3- and 6-month SPI and initial surface air temperature significantly improves seasonal prediction skills for capturing drought severity and delineation of drought areas based on observed 6-month SPI. The improvement is observed across all seasons and regions over the continental United States relative to other prediction scenarios that ignore the surface air temperature information.
• Behrangi, A., Nguyen, H., Lambrigtsen, B., Schreier, M., & Dang, V. (2015). Investigating the role of multi-spectral and near surface temperature and humidity data to improve precipitation detection at high latitudes. Atmospheric Research, 163(Issue). doi:10.1016/j.atmosres.2014.10.019
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  Accurate estimation of global precipitation is critical for the study of the earth in a changing climate. It is generally understood that instantaneous retrieval of precipitation using microwave sensors is more accurate in the tropics and mid latitudes, but the retrievals become difficult and uncertain at higher latitude and over frozen land. In the lack of reliable microwave-based precipitation estimates at high latitudes, retrievals from a single infrared band are commonly used as an alternative to fill the missing gaps. The present study shows that multi-spectral infrared, near-surface air temperature, and near-surface humidity data can add useful information to that obtained from a single infrared band and can significantly improve delineating precipitating from non-precipitating scenes, especially at higher latitudes over land. The role of surface air temperature and humidity is found to be more effective at higher latitudes, but multispectral data is effective across all latitudes. The study is performed using 4. years (2007-2010) of collocated multi-spectral data from the Moderate Resolution Imaging Spectroradiometer (MODIS), surface temperature and humidity data from the European Center for Medium Range Weather Forecast (ECMWF) analysis, and reference precipitation data from CloudSat, which can detect even very light precipitation within 80°S-80°N.
• Behrangi, A., Nguyen, H., Lambrigtsen, B., Schreier, M., & Dang, V. (2015). Investigating the role of multi-spectral and near surface temperature and humidity data to improve precipitation detection at high latitudes. Atmospheric Research, 163, 2--12.
• Chen, S., Hu, J., Zhang, Z., Behrangi, A., Hong, Y., Gebregiorgis, A. S., Cao, J., Hu, B., Xue, X., & Zhang, X. (2015). Hydrologic evaluation of the TRMM multisatellite precipitation analysis over Ganjiang Basin in humid southeastern China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(9), 4568--4580.
• Chen, S., Zhang, J., Mullens, E., Hong, Y., Behrangi, A., Tian, Y., Hu, X. M., Hu, J., Zhang, Z., & Zhang, X. (2015). Mapping the precipitation type distribution over the contiguous United States using NOAA/NSSL national multi-sensor mosaic QPE. IEEE Transactions on Geoscience and Remote Sensing, 53(Issue 8). doi:10.1109/tgrs.2015.2399015
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  Understanding the Earth's energy cycle and water balance requires an understanding of the distribution of precipitation types and their total equivalent water budget estimation. The fine distribution of precipitation types over the contiguous United States (CONUS) is not yet well understood due to either unavailability or coarse resolution of previous satellite- and ground radar-based precipitation products that have difficulty in classifying precipitation. The newly available NOAA/National Severe Storms Laboratory ground radar network-based National Multi-Sensor Mosaic QPE (NMQ/Q2) System has provided precipitation rates and types at unprecedented high spatiotemporal resolution. Here, four years of 1 km/5 min observations derived from the NMQ are used to probe spatiotemporal distribution and characteristics of precipitation types (stratiform, convective, snow, tropical/warm (T/W), and hail) over CONUS, resulting in assessment of occurrence and volume contribution for these precipitation types through the four-year period, including seasonal distributions, with some radar coverage artifacts. These maps in general highlight the snow distribution over northwestern and northern CONUS, convective distribution over southwestern and central CONUS, hail distribution over central CONUS, and T/W distribution over southeastern CONUS. The total occurrences (contribution of total rain amount/volume) of these types are 72.88% (53.91%) for stratiform, 21.15% (7.64%) for snow, 2.95% (19.31%) for T/W, 2.77% (14.03%) for convective, and 0.24% (5.11%) for hail. This paper makes it possible to prototype a near seamless high-resolution reference for evaluating satellite swath-based precipitation type retrievals and also a potentially useful forcing database for energy-water balance budgeting and hydrological prediction for the United States.
• Chen, S., Zhang, J., Mullens, E., Hong, Y., Behrangi, A., Tian, Y., Hu, X., Hu, J., Zhang, Z., & Zhang, X. (2015). Mapping the precipitation type distribution over the contiguous United States using NOAA/NSSL national multi-sensor mosaic QPE. IEEE Transactions on Geoscience and Remote Sensing, 53(8), 4434--4443.
• Ye, H., Fetzer, E. J., Wong, S., Behrangi, A., Yang, D., & Lambrigtson, B. H. (2015). Increasing atmospheric water vapor and higher daily precipitation intensity over northern Eurasia. Geophysical Research Letters, 42(21), 9404--9410.
• Ye, H., Fetzer, E. J., Wong, S., Behrangi, A., Yang, D., & Lambrigtson, B. H. (2015). Increasing atmospheric water vapor and higher daily precipitation intensity over northern Eurasia. Geophysical Research Letters, 42(Issue 21). doi:10.1002/2015gl066104
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  Increasing daily precipitation intensity is strongly associated with increasing water vapor in the atmosphere over northern Eurasia based on this study of 35 years of daily precipitation, specific humidity, and air temperature observations at 152 stations. The apparently linear relationship is consistent across all four seasons at interannual and longer time scales, and holds after temperature variation have been controlled. The study further reveals that this relationship is accompanied by increases in precipitation totals from heavy events (above the 70th percentile) and decreases in light ones (below the 30th percentile). Results suggest that increased atmospheric water vapor is the direct link to more frequent intense events of precipitation and increased risk of flooding under a warming climate via increasing precipitation intensity.
• Behrangi, A., Andreadis, K., Fisher, J. B., Joseph Turk, F., Granger, S., Painter, T., & Das, N. (2014). Satellite-based precipitation estimation and its application for streamflow prediction over mountainous western U.S. basins. Journal of Applied Meteorology and Climatology, 53(Issue 12). doi:10.1175/jamc-d-14-0056.1
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  Recognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period (2003-09). Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available.
• Behrangi, A., Andreadis, K., Fisher, J. B., Turk, F. J., Granger, S., Painter, T., & Das, N. (2014). Satellite-based precipitation estimation and its application for streamflow prediction over mountainous western US basins. Journal of Applied Meteorology and Climatology, 53(12), 2823--2842.
• Behrangi, A., Stephens, G., Adler, R. F., Huffman, G. J., Lambrigtsen, B., & Lebsock, M. (2014). An update on the oceanic precipitation rate and its zonal distribution in light of advanced observations from space. Journal of Climate, 27(11), 3957--3965.
• Behrangi, A., Stephens, G., Adler, R. F., Huffman, G. J., Lambrigtsen, B., & Lebsock, M. (2014). An update on the oceanic precipitation rate and its zonal distribution in light of advanced observations from space. Journal of Climate, 27(Issue 11). doi:10.1175/jcli-d-13-00679.1
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  This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua's Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors' estimate for 3-yr (2007-09) nearglobal (80°S-80°N) oceanic mean precipitation rate is ̃2.94 mm day-1. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68 mm day-1) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82 mm day-1). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCPand CMAP, especially in the Southern Hemisphere. © 2014 American Meteorological Society.
• Behrangi, A., Tian, Y., Lambrigtsen, B. H., & Stephens, G. L. (2014). What does CloudSat reveal about global land precipitation detection by other spaceborne sensors?. Water Resources Research, 50(6), 4893--4905.
• Behrangi, A., Tian, Y., Lambrigtsen, B. H., & Stephens, G. L. (2014). What does CloudSat reveal about global land precipitation detection by other spaceborne sensors?. Water Resources Research, 50(Issue 6). doi:10.1002/2013wr014566
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  Current orbital land precipitation products have serious shortcomings in detecting light rain and snowfall, the most frequent types of global precipitation. The missed precipitation is then propagated into the merged precipitation products that are widely used. Precipitation characteristics such as frequency and intensity and their regional distribution are expected to change in a warming climate. It is important to accurately capture those characteristics to understand and model the current state of the Earth's climate and predict future changes. In this work, the precipitation detection performance of a suite of precipitation sensors, commonly used in generating the merged precipitation products, are investigated. The high sensitivity of CloudSat Cloud Profiling Radar (CPR) to liquid and frozen hydrometeors enables superior estimates of light rainfall and snowfall within 80S-80N. Three years (2007-2009) of CloudSat precipitation data were collected to construct a climatology reference for guiding our analysis. In addition, auxiliary data such as infrared brightness temperature, surface air temperature, and cloud types were used for a more detailed assessment. The analysis shows that no more than 50% of the tropical (40S-40N) precipitation occurrence is captured by the current suite of precipitation measuring sensors. Poleward of 50 latitude, a combination of various factors such as an abundance of light rainfall, snowfall, shallow precipitation-bearing clouds, and frozen surfaces reduces the space-based precipitation detection rate to less than 20%. This shows that for a better understanding of precipitation from space, especially at higher latitudes, there is a critical need to improve current precipitation retrieval techniques and sensors. © 2014. American Geophysical Union. All Rights Reserved.
• Behrangi, A., Wong, S., Mallick, K., & Fisher, J. B. (2014). On the net surface water exchange rate estimated from remote-sensing observation and reanalysis. International Journal of Remote Sensing, 35(Issue 6). doi:10.1080/01431161.2014.889866
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  This study compares the net surface water exchange rates, or surface precipitation (P) minus evapotranspiration (ET), and atmospheric water vapour sinks calculated from various observations and reanalyses, and investigates whether they are physically consistent. We use the observed precipitation from the Global Precipitation Climatology Project (GPCP) and the Tropical Rainfall Measuring Mission (TRMM) 3B43, ocean evaporation from Goddard Satellite-based Surface Turbulent Fluxes Version 2c (GSSTF2c), and land ET from the Moderate Resolution Imaging Spectroradiometer (MODIS) global ET project (MOD16) and PT-JPL products to calculate observed P minus observed ET. P-ET is also obtained from atmospheric water vapour sink calculated using Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit observation specific humidity observation and wind fields from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and ERA-interim, denoted as AIRSM and AIRSE, respectively. MERRA and ERA-interim water vapour budgets are also calculated for cross-comparison and consistency check. The period of study is between 2003 and 2006 based on the availability of all of the data sets. Averaged water vapour sinks from AIRS and reanalysis are consistent over the global ocean and are close to zero (range: 0.02-0.06 mm day-1), but range between 0.14 and 0.23 mm day-1 when land is included. Over ocean within 50oS--50oN, averaged observed P minus observed evaporation shows a much larger negative number than that obtained from AIRS and reanalysis. The differences mainly occur over subtropical oceans, especially in the southern hemisphere in summer and the northern hemisphere in winter. Over land, generally higher agreement between observed P minus observed ET and atmospheric water vapour sinks (calculated from AIRS and reanalysis) is found. However, large regional differences, often with strong seasonal dependence, are also observed over land. Estimates of atmospheric water vapour sinks are influenced by both winds and biases in water vapour data, especially over tropics and subtropical oceans, thereby calling for the need for further investigations and consistency checks of satellite-based and reanalysis water vapour, reanalysis winds, P observations, and surface evaporation estimates. In higher latitudes, atmospheric water vapour sinks calculated from AIRSM, AIRSE, MERRA, and ERA-interim are more consistent with each other. © 2014 © 2014 Taylor & Francis.
• Behrangi, A., Wong, S., Mallick, K., & Fisher, J. B. (2014). On the net surface water exchange rate estimated from remote-sensing observation and reanalysis. International journal of remote sensing, 35(6), 2170--2185.
• Tian, Y., Liu, Y., Arsenault, K. R., & Behrangi, A. (2014). A new approach to satellite-based estimation of precipitation over snow cover. International Journal of Remote Sensing, 35(13), 4940--4951.
• Tian, Y., Liu, Y., Arsenault, K. R., & Behrangi, A. (2014). A new approach to satellite-based estimation of precipitation over snow cover. International Journal of Remote Sensing, 35(Issue 13). doi:10.1080/01431161.2014.930208
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  Current satellite-based remote-sensing approaches are largely incapable of estimating precipitation over snow cover. This note reports a proof-of-concept study of a new satellite-based approach to the estimation of precipitation over snow-covered surfaces. The method is based on the principle that precipitation can be inferred from the changes in the snow water equivalent of the snowpack. Using satellite-based snow water equivalent measurements, we derived daily precipitation amounts for the northern hemisphere for three snow-accumulation seasons, and evaluated these against independent reference datasets. The new precipitation estimates captured realistic-looking storm events over largely un-instrumented regions. However, the data are noisy and, on a seasonal scale, the amount of precipitation is believed to be underestimated. Nevertheless, current uncertainty in snow measurements, albeit large (50-100%), is still lower than direct precipitation measurements over snow (100-140%) and therefore this approach is still useful. The method will become more feasible as the quality of remotely sensed snow measurements improves. © 2014 © 2014 Taylor & Francis.
• Ye, H., Fetzer, E. J., Wong, S., Behrangi, A., Olsen, E. T., Cohen, J., Lambrigtsen, B. H., & Chen, L. (2014). Impact of increased water vapor on precipitation efficiency over northern Eurasia. Geophysical Research Letters, 41(8), 2941--2947.
• Ye, H., Fetzer, E. J., Wong, S., Behrangi, A., Olsen, E. T., Cohen, J., Lambrigtsen, B. H., & Chen, L. (2014). Impact of increased water vapor on precipitation efficiency over northern Eurasia. Geophysical Research Letters, 41(Issue 8). doi:10.1002/2014gl059830
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  This study investigates the relationships among water vapor, precipitation efficiency, precipitation amount, and air temperature anomalies on monthly time scales over northern Eurasia for winter and summer 2003-2010. Daily precipitation and temperature records at 505 historical stations, and atmospheric total precipitable water vapor and relative humidity data from Atmospheric Infrared Sounders, are used for analysis. Results show that higher atmospheric precipitable water associated with warmer temperature directly contributes to winter precipitation amount but has little impact on winter precipitation efficiency. However, accelerated decreasing relative humidity associated with higher temperature is the primary factor in the reduction of precipitation efficiency and precipitation amount regardless of higher precipitable water in summer. This study suggests that there are evident seasonal differences in precipitation trend associated with air temperature changes over the study region. Air temperature modifies a key atmospheric water variable that directly controls precipitation for that particular season. Key Points Increasing water vapor directly contributes to winter precipitation Reduced summer precipitation is related to accelerated decreasing RH ©2014. The Authors.
• Mahrooghy, M., Anantharaj, V. G., Younan, N. H., Petersen, W. A., Hsu, K. L., Behrangi, A., & Aanstoos, J. (2013). Augmenting satellite precipitation estimation with lightning information. International Journal of Remote Sensing, 34(Issue 16). doi:10.1080/01431161.2013.796100
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  We have used lightning information to augment the precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from Geostationary Operational Environmental Satellite (GOES)-12 infrared (IR) data, into either electrified patches (ECPs) or nonelectrified patches (NECPs). A set of features is extracted separately for the ECPs and NECPs. Features for the ECPs include a new feature corresponding to the number of flashes that occur within a 15 minute window around the time of the nominal scan of the satellite IR images of the cloud patches. The cloud patches are classified and clustered using a self-organizing maps (SOM) neural network. Then, brightness temperature and rain rate (T-R) relationships are derived for different clusters. Rain rates are estimated for the cloud patches based on their representative (T-R) relationship. The equitable threat scores (ETS) of the daily and hourly precipitation estimates at a range of rain rate thresholds show that incorporating lightning information can improve categorical precipitation estimation in the winter and fall seasons. In the winter, the ETS improvement is almost 15% for the daily and 12% for the hourly rainfall estimates (at thresholds below 15 mm hour-1). During the same period, there is also a drop in the false alarm ratio (FAR) and a corresponding increase in the probability of detection (POD) at most threshold levels. During the summer and spring seasons, no categorical significant improvements have been noted, except for the BIAS scores for the hourly rainfall estimates at higher thresholds (above 5 mm hour-1) in the summer months. A quantitative evaluation in terms of the root mean squared error (RMSE) and correlation coefficient (CORR) shows that the incorporation of lightning data does improve rainfall estimation over all seasons with the most improvement (around 11-13% CORR improvement) occurring during the winter. We speculate that during the winter, more of the ice processes are packed into a thinner stratiform layer with lower cloud tops and freezing levels. Hence, more of the ice contributes to precipitation on the ground. We also expect that information from lightning, related to the ice microphysics processes, provides surrogate information about the rain rate. © 2013 Copyright Taylor and Francis Group, LLC.
• Mahrooghy, M., Anantharaj, V. G., Younan, N. H., Petersen, W. A., Hsu, K., Behrangi, A., & Aanstoos, J. (2013). Augmenting satellite precipitation estimation with lightning information. International journal of remote sensing, 34(16), 5796--5811.
• Smith, M., Koren, V., Zhang, Z., Moreda, F., Cui, Z., Cosgrove, B., Mizukami, N., Kitzmiller, D., Ding, F., Reed, S., & others, . (2013). The distributed model intercomparison project--Phase 2: Experiment design and summary results of the western basin experiments. Journal of Hydrology, 507, 300--329.
• Smith, M., Koren, V., Zhang, Z., Moreda, F., Cui, Z., Cosgrove, B., Mizukami, N., Kitzmiller, D., Ding, F., Reed, S., Anderson, E., Schaake, J., Zhang, Y., Andréassian, V., Perrin, C., Coron, L., Valéry, A., Khakbaz, B., Sorooshian, S., , Behrangi, A., et al. (2013). The distributed model intercomparison project - Phase 2: Experiment design and summary results of the western basin experiments. Journal of Hydrology, 507(Issue). doi:10.1016/j.jhydrol.2013.08.040
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  The Office of Hydrologic Development (OHD) of the U.S. National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) conducted the two phases of the Distributed Model Intercomparison Project (DMIP) as cost-effective studies to guide the transition to spatially distributed hydrologic modeling for operational forecasting at NWS River Forecast Centers (RFCs). Phase 2 of the Distributed Model Intercomparison Project (DMIP 2) was formulated primarily as a mechanism to help guide the U.S. NWS as it expands its use of spatially distributed watershed models for operational river, flash flood, and water resources forecasting. The overall purpose of DMIP 2 was to test many distributed models forced by high quality operational data with a view towards meeting NWS operational forecasting needs. At the same time, DMIP 2 was formulated as an experiment that could be leveraged by the broader scientific community as a platform for the testing, evaluation, and improvement of distributed models.DMIP 2 contained experiments in two regions: in the DMIP 1 Oklahoma basins, and second, in two basins in the Sierra Nevada Mountains in the western USA. This paper presents the overview and results of the DMIP 2 experiments conducted for the two Sierra Nevada basins. Simulations from five independent groups from France, Italy, Spain and the USA were analyzed. Experiments included comparison of lumped and distributed model streamflow simulations generated with uncalibrated and calibrated parameters, and simulations of snow water equivalent (SWE) at interior locations. As in other phases of DMIP, the participant simulations were evaluated against observed hourly streamflow and SWE data and compared with simulations provided by the NWS operational lumped model. A wide range of statistical measures are used to evaluate model performance on a run-period and event basis. Differences between uncalibrated and calibrated model simulations are assessed.Results indicate that in the two study basins, no single model performed best in all cases. In addition, no distributed model was able to consistently outperform the lumped model benchmark. However, one or more distributed models were able to outperform the lumped model benchmark in many of the analyses. Several calibrated distributed models achieved higher correlation and lower bias than the calibrated lumped benchmark in the calibration, validation, and combined periods. Evaluating a number of specific precipitation-runoff events, one calibrated distributed model was able to perform at a level equal to or better than the calibrated lumped model benchmark in terms of event-averaged peak and runoff volume error. However, three distributed models were able to provide improved peak timing compared to the lumped benchmark. Taken together, calibrated distributed models provided specific improvements over the lumped benchmark in 24% of the model-basin pairs for peak flow, 12% of the model-basin pairs for event runoff volume, and 41% of the model-basin pairs for peak timing. Model calibration improved the performance statistics of nearly all models (lumped and distributed). Analysis of several precipitation/runoff events indicates that distributed models may more accurately model the dynamics of the rain/snow line (and resulting hydrologic conditions) compared to the lumped benchmark model. Analysis of SWE simulations shows that better results were achieved at higher elevation observation sites.Although the performance of distributed models was mixed compared to the lumped benchmark, all calibrated models performed well compared to results in the DMIP 2 Oklahoma basins in terms of run period correlation and %Bias, and event-averaged peak and runoff error. This finding is noteworthy considering that these Sierra Nevada basins have complications such as orographically-enhanced precipitation, snow accumulation and melt, rain on snow events, and highly variable topography. Looking at these findings and those from the previous DMIP experiments, it is clear that at this point in their evolution, distributed models have the potential to provide valuable information on specific flood events that could complement lumped model simulations. © 2013.
• Zahraei, A., Hsu, K. l., Sorooshian, S., Gourley, J. J., Hong, Y., & Behrangi, A. (2013). Short-term quantitative precipitation forecasting using an object-based approach. Journal of Hydrology, 483(Issue). doi:10.1016/j.jhydrol.2012.09.052
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  Short-term Quantitative Precipitation Forecasting (SQPF) is critical for flash-flood warning, navigation safety, and many other applications. The current study proposes a new object-based method, named PERCAST (PERsiann-ForeCAST), to identify, track, and nowcast storms. PERCAST predicts the location and rate of rainfall up to 4. h using the most recent storm images to extract storm features, such as advection field and changes in storm intensity and size. PERCAST is coupled with a previously developed precipitation retrieval algorithm called PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System) to forecast rainfall rates. Four case studies have been presented to evaluate the performance of the models. While the first two case studies justify the model capabilities in nowcasting single storms, the third and fourth case studies evaluate the proposed model over the contiguous US during the summer of 2010. The results show that, by considering storm Growth and Decay (GD) trends for the prediction, the PERCAST-GD further improves the predictability of convection in terms of verification parameters such as Probability of Detection (POD) and False Alarm Ratio (FAR) up to 15-20%, compared to the comparison algorithms such as PERCAST. © 2012 Elsevier B.V.
• Zahraei, A., Hsu, K., Sorooshian, S., Gourley, J. J., Hong, Y., & Behrangi, A. (2013). Short-term quantitative precipitation forecasting using an object-based approach. Journal of hydrology, 483, 1--15.
• Aghakouchak, A., AghaKouchak, A., Mehran, A., Mehran, A., Norouzi, H., Norouzi, H., Behrangi, A., & Behrangi, A. (2012). Systematic and random error components in satellite precipitation data sets. Geophysical Research Letters, 39(9).
• Aumann, H. H., Ruzmaikin, A., & Behrangi, A. (2012). On the surface temperature sensitivity of the reflected shortwave, outgoing longwave, and net incident radiation. Journal of Climate, 25(19), 6585--6593.
• Aumann, H. H., Ruzmaikin, A., & Behrangi, A. (2012). On the surface temperature sensitivity of the reflected shortwave, outgoing longwave, and net incident radiation. Journal of Climate, 25(Issue 19). doi:10.1175/jcli-d-11-00607.1
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  The global-mean top-of-atmosphere incident solar radiation (ISR) minus the outgoing longwave radiation (OLR) and the reflected shortwave radiation (RSW) is the net incident radiation (NET). This study analyzes the global-mean NET sensitivity to a change in the global-mean surface temperature by applying the interannual anomaly correlation technique to 9yr of Atmospheric Infrared Sounder (AIRS) global measurements of RSW and OLR under cloudy and clear conditions. The study finds the observed sensitivity of NET that includes the effects of clouds to be -1.5±0.25 (1σ)W m -2 K -1 and the clear NET sensitivity to be -2.0 ± 0.2 (1σ) W m -2 K -1, consistent with previous work using Earth Radiation Budget Experiment and Clouds and the Earth's Radiant Energy System data. The cloud effect, +0.5 ± 0.2 (1σ) W mm -2 K -1, is a positive component of the NET sensitivity. The similarity of the NET sensitivities derived from forced and unforced models invites a comparison between the observed sensitivities and the effective sensitivities calculated for the Fourth Assessment Report models, although this requires some caution: The effective model sensitivities with clouds range from -0.88 to -1.64 W m -2 K -1, the clear NET sensitivity in the models ranges from -2.32 to -1.73 W m -2 K -1, and the cloud forcing sensitivities range from +0.14 to +1.18 W m -2 K -1. The effective NET and clear NET sensitivities derived from the models are statistically consistent with those derived from the AIRS data, considering the observational and model derivation uncertainties. © 2012 American Meteorological Society.
• Behrangi, A., Casey, S. P., & Lambrigtsen, B. H. (2012). Three-dimensional distribution of cloud types over the USA and surrounding areas observed by CloudSat. International Journal of Remote Sensing, 33(Issue 16). doi:10.1080/01431161.2011.639404
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  The vertical and horizontal distributions of the cloud types across different seasons and over the contiguous USA and surrounding areas are studied. The study is performed by collecting two years (2007 and 2008) of data from the CloudSat 2B-CLDCLASS product that uses effective radar reflectivity factor Ze, the presence of precipitation and ancillary data such as surface topography and the model-predicted temperature profile to classify clouds into seven distinct types. Considerable seasonal variations of the horizontal distribution of the cloud-type fractions are observed in the study area among different seasons and for both daytime and night-time CloudSat observations. It was found that during spring and summer, deep convective (Dc) clouds are observed much more frequently during night-time than during daytime over both the land and ocean. For the studied area and during daytime, low clouds were more frequent (up to ~50%) over the land and less frequent over the ocean compared with night-time observations. Analysis of the vertical distribution of cloud layers reveals that the fraction of cloudy scenes with two or more distinct cloud layers is the highest (up to 30%) over the northwest corner of the USA and the southwest corner of Canada and the nearby oceans. The southwest corner of the USA and the nearby east Pacific Ocean appeared to have the lowest fraction (
• Behrangi, A., Casey, S. P., & Lambrigtsen, B. H. (2012). Three-dimensional distribution of cloud types over the USA and surrounding areas observed by CloudSat. International journal of remote sensing, 33(16), 4856--4870.
• Behrangi, A., Kubar, T., & Lambrigtsen, B. (2012). Phenomenological description of tropical clouds using CloudSat cloud classification. Monthly Weather Review, 140(10), 3235--3249.
• Behrangi, A., Kubar, T., & Lambrigtsen, B. (2012). Phenomenological description of tropical clouds using cloudsat cloud classification. Monthly Weather Review, 140(Issue 10). doi:10.1175/mwr-d-11-00247.1
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  Two years of tropical oceanic cloud observations are analyzed using the operational CloudSat cloud classification product and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Relationships are examined between cloud types, sea surface temperature (SST), and location during the CloudSat early morning and afternoon overpasses. Based on CloudSat and combined lidar-radar products, the maximum and minimum cloud fractions occur at SSTs near 303 and 299 K, respectively, corresponding to deep convective/detrained cloud populations and the transition from shallow to deep convection. For SSTs below approximately 301 K, low clouds (stratiform and stratocumulus) are dominant (cloud fraction between 0.15 and 0.37) whereas high clouds are dominant for SSTs greater than about 301 K (cloud fraction between 0.18 and 0.28). Consistent with previous studies, most tropical low clouds are associated with lower SSTs, with a strong inverse linear relationship between low cloud frequency and SST. For all cloud types except nimbostratus, stratus, and stratocumulus, a sharp increase in frequency of occurrence is observed for SSTs between 299 and 300.5 K, deduced as the onset of deeper convection. Peak fractions of high, deep convective, altostratus, and altocumulus clouds occur at SSTs close to 303 K, while cumulus clouds, which have lower tops, show a smooth cloud fractional peak about 28° cooler. Deep convective and other high cloud types decrease sharply above SSTs of 303 K, in accordance with previous work suggesting a narrow window of tropical deep convection. Finally, significant cloud frequency differences exist between CloudSat early morning and afternoon overpasses, suggesting a diurnal cycle of some cloud types, particularly stratocumulus, high, and deep convective clouds. ©2012 American Meteorological Society.
• Behrangi, A., Lebsock, M., Wong, S., & Lambrigtsen, B. (2012). On the quantification of oceanic rainfall using spaceborne sensors. Journal of Geophysical Research Atmospheres, 117(Issue 20). doi:10.1029/2012jd017979
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  Much of our knowledge about oceanic rainfall comes from spaceborne sensors. These sensors provide direct or indirect information used for precipitation retrievals through various algorithms. A thorough understanding of rain frequency and intensity and its regional distribution, which is especially important in a warming climate, requires an evaluation of the performance of rain-measuring sensors and identification of strengths and limitations offered by each sensor. The Tropical Rainfall Measuring Mission (TRMM) has enabled significant advancement in quantification of moderate to intense rainfall. However, a common limitation of the current suite of rain-measuring sensors is their lack of sensitivity to light rainfall, especially over subtropical and high-latitude oceans. Among various spaceborne sensors, CloudSat enables superior retrieval of light rainfall and drizzle. By using 3 years (2007-2009) of rainfall data from CloudSat and the precipitation radar aboard TRMM, it was determined that the quasi-global (60°S-60°N) oceanic mean rain rate is about 3.05 mm/d, considerably larger than that obtained from any individual sensor product. In the deep tropics, especially within 20S-20N, the sensors show the highest agreement, with a large fraction of total rain volume captured by the majority of sensors. However, toward higher latitudes and within the subtropical high-pressure regions, a significant fraction of rainfall, which can exceed 50% or more of total rain volume, is missed by the majority of the sensors. © 2012. American Geophysical Union. All Rights Reserved.
• Behrangi, A., Lebsock, M., Wong, S., & Lambrigtsen, B. (2012). On the quantification of oceanic rainfall using spaceborne sensors. Journal of Geophysical Research: Atmospheres, 117(D20).
• Behrangi, A., Sorooshian, S., & Hsu, K. (2012). Summertime evaluation of REFAME over the Unites States for near real-time high resolution precipitation estimation. Journal of hydrology, 456, 130--138.
• Behrangi, A., Sorooshian, S., & Hsu, K. l. (2012). Summertime evaluation of REFAME over the Unites States for near real-time high resolution precipitation estimation. Journal of Hydrology, 456-457(Issue). doi:10.1016/j.jhydrol.2012.06.033
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  Precipitation is the key input for hydrometeorological modeling and applications. In many regions of the world, including populated areas, ground-based measurement of precipitation (whether from radar or rain gauge) is either sparse in time and space or nonexistent. Therefore, high-resolution satellite-based precipitation products are recognized as critical data sources, especially for rapidly-evolving hydrometeorological events such as flash floods which primarily occur during summer/warm seasons. As " proof of concept" , a recently proposed algorithm called Rain Estimation using Forward Adjusted-advection of Microwave Estimates (REFAME) and its variation REFAMEgeo are evaluated over the contiguous United States during summers of 2009 and 2011. Both methods are originally designed for near real-time high resolution precipitation estimation from remotely sensed data. High-resolution Q2 (ground radar) precipitation data, in conjunction with two operational near real-time satellite-based precipitation products (PERSIANN, PERSIANN-CCS) are used as evaluation reference and for comparison. The study is performed at half-hour temporal resolution and at a range of spatial resolutions (0.08-, 0.25-, 0.5-, and 1-degree latitude/longitude). The statistical analyses suggest that REFAMEgeo performs favorably among the studied products in terms of capturing both spatial coverage and intensity of precipitation at near real-time with the temporal resolution offered by geostationary satellites. With respect to volume precipitation, REFAMEgeo together with REFAME demonstrates slight overestimation of intense precipitation and underestimation of light precipitation events. Compared to REFAME, It is observed that REFAMEgeo maintains stable performance, even when the amount of accessible microwave (MW) overpasses is limited. Based on the encouraging outcome of this study which was intended as " proof of concept" , further testing for other seasons and data-rich regions is the next logical step. Upon confirmation of the relative reliability of the algorithm, it is reasonable to recommend the use of its precipitation estimates for data-sparse regions of the world. © 2012 Elsevier B.V.
• AghaKouchak, A., Behrangi, A., Sorooshian, S., Hsu, K., & Amitai, E. (2011). Evaluation of satellite-retrieved extreme precipitation rates across the central United States. Journal of Geophysical Research: Atmospheres, 116(D2).
• Aghakouchak, A., Behrangi, A., Sorooshian, S., Hsu, K., & Amitai, E. (2011). Evaluation of satellite-retrieved extreme precipitation rates across the central United States. Journal of Geophysical Research Atmospheres, 116(Issue 2). doi:10.1029/2010jd014741
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  Water resources management, forecasting, and decision making require reliable estimates of precipitation. Extreme precipitation events are of particular importance because of their severe impact on the economy, the environment, and the society. In recent years, the emergence of various satellite-retrieved precipitation products with high spatial resolutions and global coverage have resulted in new sources of uninterrupted precipitation estimates. However, satellite-based estimates are not well integrated into operational and decision-making applications because of a lack of information regarding the associated uncertainties and reliability of these products. In this study, four satellite-derived precipitation products (CMORPH, PERSIANN, TMPA-RT, and TMPA-V6) are evaluated with respect to their performance in capturing precipitation extremes. The Stage IV (radar-based, gauge-adjusted) precipitation estimates are used as reference data. The results show that with respect to the probability of detecting extremes and the volume of correctly identified precipitation, CMORPH and PERSIANN data sets lead to better estimates. However, their false alarm ratio and volume are higher than those of TMPA-RT and TMPA-V6. Overall, no single precipitation product can be considered ideal for detecting extreme events. In fact, all precipitation products tend to miss a significant volume of rainfall. With respect to verification metrics used in this study, the performance of all satellite products tended to worsen as the choice of extreme precipitation threshold increased. The analyses suggest that extensive efforts are necessary to develop algorithms that can capture extremes more reliably. Copyright 2011 by the American Geophysical Union.
• Aumann, H. H., DeSouza-Machado, S. G., & Behrangi, A. (2011). Deep convective clouds at the tropopause. Atmospheric Chemistry and Physics, 11(3), 1167--1176.
• Aumann, H. H., Desouza-Machado, S. G., & Behrangi, A. (2011). Deep convective clouds at the tropopause. Atmospheric Chemistry and Physics, 11(Issue 3). doi:10.5194/acp-11-1167-2011
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  Data from the Atmospheric Infrared Sounder (AIRS) on the EOS Aqua spacecraft each day show tens of thousands of Cold Clouds (CC) in the tropical oceans with 10 μm window channel brightness temperatures colder than 225 K. These clouds represent a mix of cold anvil clouds and Deep Convective Clouds (DCC). This mix can be separated by computing the difference between two channels, a window channel and a channel with strong CO2 absorption: for some cold clouds this difference is negative, i.e. the spectra for some cold clouds are inverted. We refer to cold clouds with spectra which are more than 2 K inverted as DCCi2. Associated with DCCi2 is a very high rain rate and a local upward displacement of the tropopause, a cold "bulge", which can be seen directly in the brightness temperatures of AIRS and Advanced Microwave Sounding Unit (AMSU) temperature sounding channels in the lower stratosphere. The very high rain rate and the local distortion of the tropopause indicate that DCCi2 objects are associated with severe storms. Significant long-term trends in the statistical properties of DCCi2 could be interesting indicators of climate change. While the analysis of the nature and physical conditions related to DCCi2 requires hyperspectral infrared and microwave data, the identification of DCCi2 requires only one good window channel and one strong CO2 sounding channel. This suggests that improved identification of severe storms with future advanced geostationary satellites could be accomplished with the addition of one or two narrow band channels. © 2011 Author(s).
• Behrangi, A., Khakbaz, B., Jaw, T. C., AghaKouchak, A., Hsu, K., & Sorooshian, S. (2011). Hydrologic evaluation of satellite precipitation products over a mid-size basin. Journal of Hydrology, 397(3-4), 225--237.
• Behrangi, A., Khakbaz, B., Jaw, T. C., AghaKouchak, A., Hsu, K., & Sorooshian, S. (2011). Hydrologic evaluation of satellite precipitation products over a mid-size basin. Journal of Hydrology, 397(Issue 3-4). doi:10.1016/j.jhydrol.2010.11.043
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  Since the past three decades a great deal of effort is devoted to development of satellite-based precipitation retrieval algorithms. More recently, several satellite-based precipitation products have emerged that provide uninterrupted precipitation time series with quasi-global coverage. These satellite-based precipitation products provide an unprecedented opportunity for hydrometeorological applications and climate studies. Although growing, the application of satellite data for hydrological applications is still very limited. In this study, the effectiveness of using satellite-based precipitation products for streamflow simulation at catchment scale is evaluated. Five satellite-based precipitation products (TMPA-RT, TMPA-V6, CMORPH, PERSIANN, and PERSIANN-adj) are used as forcing data for streamflow simulations at 6-h and monthly time scales during the period of 2003-2008. SACramento Soil Moisture Accounting (SAC-SMA) model is used for streamflow simulation over the mid-size Illinois River basin.The results show that by employing the satellite-based precipitation forcing the general streamflow pattern is well captured at both 6-h and monthly time scales. However, satellites products, with no bias-adjustment being employed, significantly overestimate both precipitation inputs and simulated streamflows over warm months (spring and summer months). For cold season, on the other hand, the unadjusted precipitation products result in under-estimation of streamflow forecast. It was found that bias-adjustment of precipitation is critical and can yield to substantial improvement in capturing both streamflow pattern and magnitude. The results suggest that along with efforts to improve satellite-based precipitation estimation techniques, it is important to develop more effective near real-time precipitation bias adjustment techniques for hydrologic applications. © 2010 Elsevier B.V.
• Behrangi, A., Hsu, K., Imam, B., & Sorooshian, S. (2010). Daytime precipitation estimation using bispectral cloud classification system. Journal of Applied Meteorology and Climatology, 49(5), 1015--1031.
• Behrangi, A., Hsu, K., Imam, B., & Sorooshian, S. (2010). Daytime precipitation estimation using bispectral cloud classification system. Journal of Applied Meteorology and Climatology, 49(Issue 5). doi:10.1175/2009jamc2291.1
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  Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitudelongitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bis-pectral (visible and infrared) rain estimation scenarios were compared to investigate the technique's ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude-longitude) scales. Overall, the results using daytime data during June-August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively. © 2010 American Meteorological Society.
• Behrangi, A., Imam, B., Hsu, K., Sorooshian, S., Bellerby, T. J., & Huffman, G. J. (2010). REFAME: Rain estimation using forward-adjusted advection of microwave estimates. Journal of Hydrometeorology, 11(6), 1305--1321.
• Behrangi, A., Imam, B., Hsu, K., Sorooshian, S., Bellerby, T. J., & Huffman, G. J. (2010). REFAME: Rain estimation using forward-adjusted advection of microwave estimates. Journal of Hydrometeorology, 11(Issue 6). doi:10.1175/2010jhm1248.1
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  A new multiplatform multisensor satellite rainfall estimation technique is proposed in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to advect microwave (MW)-derived precipitation estimates along cloud motion streamlines and to further adjust the rainfall rates using local cloud classification. The main objective of the Rain Estimation using Forward-Adjusted advection of Microwave Estimates (REFAME) is to investigate whether inclusion of GEO-IR information can help to improve the advected MW precipitation rate as it gets farther in time from the previous MW overpass. The technique comprises three steps. The first step incorporates a 2D cloud tracking algorithm to capture cloud motion streamlines through successive IR images. The second step classifies cloudy pixels to a number of predefined clusters using brightness temperature (Tb) gradients between successive IR images along the cloud motion streamlines in combination with IR cloud-top brightness temperatures and textural features. A mean precipitation rate for each cluster is calculated using available MW-derived precipitation estimates. In the third step, the mean cluster precipitation rates are used to adjust MW precipitation intensities advected between available MW overpasses along cloud motion streamlines. REFAME is a flexible technique, potentially capable of incorporating diverse precipitation-relevant information, such as multispectral data. Evaluated over a range of spatial and temporal scales over the conterminous United States, the performance of the full REFAMEalgorithm compared favorably with products incorporating either no cloud tracking or no intensity adjustment. The observed improvements in root-mean-square error and especially in correlation coefficient between REFAME outputs and ground radar observations demonstrate that the new approach is effective in reducing the uncertainties and capturing the variation of precipitation intensity along cloud advection streamlines between MW sensor overpasses. An extended REFAME algorithm combines the adjusted advected MW rainfall rates with infrared-derived precipitation rates in an attempt to capture precipitation events initiating and decaying during the interval between two consecutive MW overpasses. Evaluation statistics indicate that the extended algorithm is effective to capture the life cycle of the convective precipitation, particularly for the interval between microwave overpasses in which precipitation starts or ends. © 2010 American Meteorological Society.
• Hong, Y., Krajewski, W. F., Gourley, J. J., Behrangi, A., Villarini, G., Skofronic, G., Jackson, A., Berne, P., Xie, E., Amitai, E., & others, . (2010). Outlook for high-resolution precipitation measurements for hydrologic applications. AGU Hydrology news letter.
• Behrangi, A., Hsu, K. L., Imam, B., Sorooshian, S., & Kuligowski, R. J. (2009). Evaluating the utility of multispectral information in delineating the areal extent of precipitation. Journal of Hydrometeorology, 10(Issue 3). doi:10.1175/2009jhm1077.1
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  Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 μm) and 4 (10.7 μm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society.
• Behrangi, A., Hsu, K. L., Imam, B., Sorooshian, S., Huffman, G. J., & Kuligowski, R. J. (2009). PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis. Journal of Hydrometeorology, 10(Issue 6). doi:10.1175/2009jhm1139.1
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  Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society.
• Behrangi, A., Hsu, K., Imam, B., Sorooshian, S., & Kuligowski, R. J. (2009). Evaluating the utility of multispectral information in delineating the areal extent of precipitation. Journal of Hydrometeorology, 10(3), 684--700.
• Behrangi, A., Hsu, K., Imam, B., Sorooshian, S., Huffman, G. J., & Kuligowski, R. J. (2009). PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis. Journal of Hydrometeorology, 10(6), 1414--1429.
• Behrangi, A., Khakbaz, B., Vrugt, J. A., Duan, Q., & Sorooshian, S. (2008). Comment on "Dynamically dimensioned search algorithm for computationally efficient watershed model calibration" by Bryan A. Tolson and Christine A. Shoemaker. Water Resources Research, 44(Issue 12). doi:10.1029/2007wr006429
• Behrangi, A., Khakbaz, B., Vrugt, J. A., Duan, Q., & Sorooshian, S. (2008). Comment on “Dynamically dimensioned search algorithm for computationally efficient watershed model calibration” by Bryan A. Tolson and Christine A. Shoemaker. Water Resources Research, 44(12).

Proceedings Publications

• Behrangi, A., Huffman, G. J., Adler, R. F., Song, Y., Bolvin, D. T., Nelkin, E. J., & Gu, G. (2025). The latest GPCP Daily and Monthly Products: Current Status, Assessments, and the Future Plans. In EGU General Assembly Conference Abstracts.
• Dong, X., Das, A., Xi, B., Girone, D., Marcovecchio, A. R., Zheng, X., & Behrangi, A. (2025). Quantifying the Differences of Clouds Observed By Radar and Lidar from Three Platforms over the Southern Ocean. In 105th Annual AMS Meeting 2025, 105.
• Behrangi, A. (2024). Opportunities and challenges in using new Remotely Sensed Products to Advance Merged Precipitation Products at Weather and Climate Scales. In Chapman Conference on Remote Sensing of the Water Cycle.
• Bolvin, D. T., Huffman, G. J., Behrangi, A., Adler, R. F., Nelkin, E. J., & Gu, G. (2024). Comparison of Monthly GPCPV3 Precipitation Estimates with PACRAIN Atoll Gauge Observations. In 104th AMS Annual Meeting.
• Huffman, G. J., Behrangi, A., Adler, R. F., Bolvin, D. T., Nelkin, E. J., Gu, G., & Ehsani, M. R. (2024). Results from the GPCP Version 3.2 Products. In 104th American Meteorological Society Annual Meeting.
• Huffman, G. J., Bolvin, D. T., Joyce, R., Nelkin, E. J., Tan, J., Adler, R. F., Behrangi, A., & Gu, G. (2024). Insights into Long-Term Global Precipitation from IMERG and GPCP. In 9th Global Energy and Water Exchanges Open Science Conference.
• Huffman, G. J., Behrangi, A., Adler, R. F., Bolvin, D. T., Nelkin, E., Gu, G., & Ehsani, M. R. (2023). GPCP Version 3.2 Products and Results. In 20th Annual Meeting of the Asia Oceania Geosciences Society (AOGS).
• Savtchenko, A., Huffman, G., Behgrangi, A., & Adler, R. (2023). IMERG and GPCP Seasonality and Response to Climate and Weather Variability. In 103rd American Meteorological Society (AMS) Annual Meeting.
• Behrangi, A., Huffman, G. J., Adler, R. F., Ehsani, M. R., Bolvin, D., Nelkin, E., & Gu, G. (2022). The latest Global Precipitation Climatology Project Daily and Monthly products (Version 3.2): Early results and comparisons. In Fall Meeting 2022.
• Adler, R. F., Gu, G., Huffman, G. J., Behrangi, A., Bolvin, D. T., & Wang, J. (2021). Global Precipitation Means and Variations with the New Version of GPCP (Invited Presentation). In 101st American Meteorological Society Annual Meeting.
• Agnihotri, J., Niu, G., Behrangi, A., Tavakoly, A., & Geheran, M. (2021). Improving Predictive Understanding of the Impacts of Frozen Soil on Streamflow through Modeling and Data Analysis. In AGU Fall Meeting Abstracts, 2021.
• Agnihotri, J., Niu, G., Behrangi, A., Tavakoly, A., & Geheran, M. P. (2021). Improving Predictive Understanding of the Impacts of Frozen Soil on Streamflow through Modeling and Data Analysis. In AGU Fall Meeting 2021.
• Ayat, H., Evans, J., & Behrangi, A. (2021). The effect of different contributing sensors in IMERG-Final precipitation estimates. In EGU General Assembly Conference Abstracts.
• Behrangi, A., Huffman, G., Adler, R., Bolvin, D., Nelkin, E., & Gu, G. (2021). High Latitude Considerations in the Latest GPCP monthly and daily products. In AGU Fall Meeting Abstracts, 2021.
• Draper, C. S., Behrangi, A., Fox, A. M., & Yin, J. (2021). 35HYDRO Wed-Applications of Remotely Sensed Observations to Enhance Land Surface Modeling I. In 101st American Meteorological Society Annual Meeting.
• Heflin, S., Behrangi, A., & Ehsani, M. R. (2021). Evaluating the Diurnal Cycle of Precipitation Over Finland. In AGU Fall Meeting 2021.
• Huffman, G. J., Behrangi, A., Adler, R. F., Bolvin, D. T., Nelkin, E. J., Song, Y., & Wang, J. (2021). The Global Precipitation Climatology Project Version 3 Products. In EGU General Assembly Conference Abstracts.
• Javadian, M., Lee, K., Smith, W., Behrangi, A., Knowles, J., Scott, R., Fisher, J., Moore, D., & Leeuwen, W. (2021). Diurnal Vegetation Water Stress Over a Semiarid Mixed Conifer Forest. In AGU Fall Meeting Abstracts, 2021.
• Javadian, M., San, L. K., Smith, W. K., Behrangi, A., Knowles, J. F., Scott, R. L., Fisher, J. B., Moore, D. J., & Leeuwen, W. (2021). Diurnal Vegetation Water Stress Over a Semiarid Mixed Conifer Forest. In AGU Fall Meeting 2021.
• Lau, A., & Behrangi, A. (2021). Assessment of Satellite Datasets for Rainfall Predictions based on Canonical Correlations. In AGU Fall Meeting 2021.
• Lau, A., & Behrangi, A. (2021). Assessment of Satellite Datasets for Rainfall Predictions based on Canonical Correlations. In AGU Fall Meeting Abstracts, 2021.
• Li, Z., Wen, Y., Schreier, M., Behrangi, A., Hong, Y., & Lambrigtsen, B. (2021). Explainable AI models for precipitation retrievals: a case study based on atmospheric sounding systems. In AGU Fall Meeting Abstracts, 2021.
• Marcovecchio, A., Xi, B., Zheng, X., Zhang, X., Dong, X., & Behrangi, A. (2021). Similarities and Differences of Marine Boundary Layer Cloud and Drizzle Properties Measured Over Two Hemispheres. In AGU Fall Meeting Abstracts, 2021.
• Nelkin, E., Huffman, G. J., Bolvin, D. T., Behrangi, A., & Adler, R. F. (2021). Introducing the GPCP Version 3.1 Daily Precipitation Dataset. In AGU Fall Meeting 2021.
• Adhikari, A., Ehsani, M. R., & Behrangi, A. (2020). Snowfall Retrieval from Satellite-based Microwave Humidity Sounders using Machine Learning Methods. In AGU Fall Meeting 2020.
• Arabzadeh, A., Ehsani, M. R., Guan, B., Heflin, S., & Behrangi, A. (2020). Global Analysis of Atmospheric Rivers Precipitation using Remote Sensing and Reanalysis Products. In AGU Fall Meeting 2020.
• Ehsani, M. R., Adhikari, A., Song, Y., & Behrangi, A. (2020). On the Potential of the Advance Very High-Resolution Radiometer for Precipitation Retrieval in High Latitudes Using Machine Learning. In AGU Fall Meeting 2020.
• Javadian, M., Behrangi, A., Smith, W. K., & Fisher, J. (2020). Global Trends in Evapotranspiration Dominated by Increases Across Cropland Regions: Insights Into Sustainability in Food Production. In AGU Fall Meeting 2020.
• Marcovecchio, A., Behrangi, A., Dong, X., Xi, B., & Huang, Y. (2020). Melt Season Precipitation Influence on and Response to Early and Late Arctic Sea Ice Melt Onset. In AGU Fall Meeting 2020.
• Song, Y., & Behrangi, A. (2020). Consistency of Global SWE and Precipitation Variations using Satellite, In-Situ and Reanalysis Products. In AGU Fall Meeting 2020.
• Wang, Y., Gupta, H. V., Broxton, P. D., Fang, Y., Behrangi, A., Zeng, X., & Niu, G. (2020). Developing the Snow Cover Fraction Schemes for land surface model using Machine Learning Approach. In 100th American Meteorological Society Annual Meeting.
• Behrangi, A. (2019). Observation of precipitation extremes: opportunities, challenges, and flood prediction. In AGU Fall Meeting 2019.
• Behrangi, A., Song, Y., Huffman, G. J., Adler, R. F., Yang, D., Bolvin, D. T., & Nelkin, E. (2019). Improvement of the Global Precipitation Climatology Project in High Latitudes Using the Latest Satellite and Surface Observations. In AGU Fall Meeting 2019.
• Dadashazar, H., Crosbie, E., Majdi, M. S., Panahi, M., Moghaddam, M. A., Behrangi, A., Brunke, M., Zeng, X., Jonsson, H., Flagan, R. C., & others, . (2019). Stratocumulus Cloud Clearings: Statistics from Satellites, Reanalysis Models, and Airborne Measurements. In AGU Fall Meeting 2019.
• Farahmand, A., Behrangi, A., & AghaKouchak, A. (2019). Coupling Irrigation Demand With Drought Conditions. In AGU Fall Meeting 2019.
• Hobbs, J., Braverman, A. J., Behrangi, A., Fetzer, E. J., Foster, K., Lee, H., McDuffie, J., Nguyen, H., Shen, M., & Teixeira, J. (2019). Lessons Learned in Simulation-Based Uncertainty Quantification for Satellite Retrievals. In AGU Fall Meeting 2019.
• Javadian, M., Behrangi, A., & Sorooshian, A. (2019). Drought Effect on Dust Storm Severity and Predictability. In AGU Fall Meeting 2019.
• Kucera, P. A., Behrangi, A., & Habib, E. H. (2019). Using Precipitation Data Sets and Quantifying Associated Uncertainties in Hydrometeorological and Climate Impact Applications II Posters. In AGU Fall Meeting 2019.
• Panahi, M., & Behrangi, A. (2019). Using Diverse Data Sets to Assess Satellite-Based Snowfall Accumulation and Gauge Undercatch Correction Factors. In AGU Fall Meeting 2019.
• Song, Y., Behrangi, A., & Blanchard-Wrigglesworth, E. (2019). Assessment of Satellite and Reanalysis Precipitation Products over Arctic Sea Ice. In AGU Fall Meeting 2019.
• Wang, Y., Fang, Y., Broxton, P. D., Behrangi, A., Zeng, X., Gupta, H. V., & Niu, G. (2019). Developing a New Snow Cover Fraction Scheme for Hydrological Predictions. In AGU Fall Meeting 2019.
• Behrangi, A., Huffman, G. J., & Adler, R. (2018, December). Constraining precipitation amount over cold regions. In AGU Fall Meeting Abstracts.
• Behrangi, A., Singh, A., & Reager, J. (2018, December). Probabilistic drought recovery analysis using GRACE and precipitation data. In AGU Fall Meeting Abstracts.
• Farahmand, A., Reager, J., Stavros, N., & Behrangi, A. (2018, December). Introducing a Gridded Wildfire Risk Model in the United States for Allocation of Fire Management Resources Using NASA Satellite Observations. In AGU Fall Meeting Abstracts.
• Hobbs, J., Behrangi, A., Braverman, A., & Fetzer, E. (2018, December). Simulation-Based Uncertainty Quantification for Atmospheric Remote Sensing Retrievals. In AGU Fall Meeting Abstracts.
• Song, Y., & Behrangi, A. (2018, December). Baseline Precipitation Climatology in Support of GPCP. In AGU Fall Meeting Abstracts.
• Tang, G., Behrangi, A., Long, D., Li, C., & Hong, Y. (2018, December). Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded satellite precipitation products. In AGU Fall Meeting Abstracts.
• Yanovsky, I., Wen, Y., Behrangi, A., Schreier, M., & Lambrigtsen, B. (2018). Validating Enhanced Resolution of Microwave Sounder Imagery Through Fusion with Infrared Sensors| Data. In 2018 IEEE 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad).
• Aumann, H. H., Behrangi, A., & Ruzmaikin, A. (2017). Frequency of Tropical Ocean Deep Convection and Global Warming. In AGU Fall Meeting Abstracts.
• Barkhordarian, A., Behrangi, A., & Mechoso, C. R. (2017, December). Detection of non-natural springtime precipitation change over northern South America. In AGU Fall Meeting Abstracts.
• Farahmand, A., Reager, J. T., Behrangi, A., Stavros, E. N., & Randerson, J. T. (2017). Using NASA Satellite Observations to Map Wildfire Risk in the United States for Allocation of Fire Management Resources. In AGU Fall Meeting Abstracts.
• Ray, S. E., Fetzer, E. J., Lambrigtsen, B., Olsen, E. T., Licata, S. J., Hall JR, ., Penteado, P. F., Realmuto, V. J., Thrastarson, H. T., Teixeira, J., & others, . (2017). Atmospheric Infrared Sounder on NASA's Aqua Satellite: Applications for Volcano Rapid Response, Influenza Outbreak Prediction, and Drought Onset Prediction. In AGU Fall Meeting Abstracts.
• Yanovsky, I., Behrangi, A., Schreier, M., Dang, V., Wen, B., & Lambrigtsen, B. (2017). Fusion of microwave and infrared data for enhancing its spatial resolution. In Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International.
• Behrangi, A., Richardson, M., Stephens, G. L., Huffman, G. J., Adler, R. F., & Fetzer, E. J. (2016). Using new observations to improve high latitude and high altitude precipitation estimates in support of water and energy budget calculations and prediction of future changes. In AGU Fall Meeting Abstracts.
• Kubar, T. L., & Behrangi, A. (2016). Ocean-Atmosphere Coupling in SST Hot Spot Regimes as a Function of ENSO: Local SST and Deep Convection Relationships, Larger-Scale Interactions With and Modulations on El Nino, and Remote Tropical and Extratropical Connections Using Multi-Satellite Observations and ERA-Interim Reanalysis. In AGU Fall Meeting Abstracts.
• Stavros, E. N., Abatzoglou, J. T., Rao, P., Behrangi, A., Miller, C. E., & Schimel, D. (2016). Observing fire danger globally: applying a VPD indicator globally independent of region specific fire danger models. In AGU Fall Meeting Abstracts.
• Wen, Y. B., Behrangi, A., Lambrigtsen, B., & Kirstetter, P. E. (2016). Evaluation and Uncertainty Estimation of Radar and Satellite Precipitation Products over Western United States. In AGU Fall Meeting Abstracts.
• Behrangi, A., Richardson, M., Christensen, M., Huffman, G. J., Adler, R. F., Stephens, G. L., & Lambrigtsen, B. (2015). Status of High Latitude Precipitation Estimates: The Role of GPM in Advancing our Current Understanding. In AGU Fall Meeting Abstracts.
• Chen, S., Hu, J., Zhang, Z., Behrangi, A., Hong, Y., Gebregiorgis, A. S., Cao, J., Hu, B., Xue, X., & Zhang, X. (2015). Hydrologic Evaluation of the TRMM Multisatellite Precipitation Analysis over Ganjiang Basin in Humid Southeastern China. In it is journal, 8.
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  This study assesses the successive Version-6 and Version-7 TRMM Multisatellite Precipitation Analysis (TMPA) products including near-real-time products (3B42RTV6 and 3B42RTV7) and post-real-time research products (3B42V6 and 3B42V7) from March 2002 to February 2008 over Ganjiang Basin in the humid southeastern China, located in the lower reach of Yangtze River. Direct comparison of TMPA rainfall estimates with ground observation shows that the spatial and temporal rainfall characteristics over this region are well captured by 3B42V6, 3B42RTV7, and 3B42V7. In terms of daily grid-based comparison, 3B42RTV7 has been improved over 3B42RTV6 by reducing relative bias (RB) from-30.25% to 4.93%; 3B42V6, 3B42RTV7, and 3B42V7 show close performance with each other with RB less than 5% and moderate correlative coefficient (CC, 0.59). Daily hydrologic simulation with Xin'anjiang hydrologic model using these TMPA products as input shows that: 1) 3B42V6 and 3B42V7 demonstrate very comparable hydrologic skills, which are close to those of the reference rainfall with high Nash-Sutcliffe index (NSCE, 0.71 and 0.72, respectively) and strong correlation (CC = 0.88); and 2) 3B42RTV7 displays a better hydrologic performance than 3B42RTV6 by increasing the NSCE from 0.56 to 0.59, improving CC from 0.81 to 0.87, and reducing RB from-33.15% to 20.93%. This improvement of real-time TMPA product shows its potential hydrologic utility in water resource management and flood forecast. Finally, this study provides useful reference for TMPA developers and insights for the end users in their applications.
• Granger, S. L., & Behrangi, A. (2015). A Simple Drought Product and Indicator Derived from Temperature and Relative Humidity Observed by the Atmospheric InfraRed Sounder (AIRS). In AGU Fall Meeting Abstracts.
• Kubar, T. L., & Behrangi, A. (2015). The Coupling of Convection, Large-Scale Atmospheric Dynamics, Surface Radiation, and Sea-Surface Temperature Hot Spots as Characterized by MODIS, TRMM, CERES, and ECMWF-Interim Reanalysis Data. In AGU Fall Meeting Abstracts.
• Lambrigtsen, B., Brown, S. T., Schreier, M. M., Dang, H., & Behrangi, A. (2015). Atmospheric River Observations with the HAMSR Aircraft Microwave Sounder. In AGU Fall Meeting Abstracts.
• Stampoulis, D., Andreadis, K., Granger, S. L., Fisher, J. B., Turk, F. J., Behrangi, A., Das, N. N., & Ines, A. (2015). Assessing Hydro-Ecological Vulnerability from Space. In AGU Fall Meeting Abstracts.
• Wen, Y., Kirstetter, P., Gourley, J. J., Hong, Y., & Behrangi, A. (2015). Evaluation of Ground Radar Snowfall Products Using SNOTEL Measurements over Mountainous Regions in Western United States. In AGU Fall Meeting Abstracts.
• Behrangi, A., Andreadis, K., Fisher, J. B., Turk, F. J., Painter, T. H., Granger, S. L., Das, N. N., & Stephens, G. L. (2014). Hydrologic Assessment of Remotely Sensed High Resolution Precipitation Products over Cold-Mountainous Regions, and Analysis of the GPM Impact. In AGU Fall Meeting Abstracts.
• Chen, S., Hong, Y., Behrangi, A., Qi, Y., & Hu, J. (2014). Performance and Uncertainty Analysis of Precipitation Retrievals Derived from Dual-frequency Precipitation Radar and Microwave Imager onboard GPM over CONUS. In AGU Fall Meeting Abstracts.
• Famiglietti, J. S., Thomas, B. F., Reager, J. T., Castle, S. L., David, C. H., Thomas, A. C., Andreadis, K., Argus, D. F., Behrangi, A., Farr, T., & others, . (2014). Satellite Observations of the Epic California Drought. In AGU Fall Meeting Abstracts.
• Manning, E. M., Aumann, H. H., & Behrangi, A. (2014). AIRS Level-1C and applications to cross-calibration with MODIS and CrIS. In Earth Observing Systems XIX, 9218.
• Stampoulis, D., Andreadis, K., Granger, S. L., Fisher, J. B., Behrangi, A., Das, N. N., & Turk, J. (2014). Quantifying the resilience of vegetation and soil moisture during dry spells using satellite remote sensing. In AGU Fall Meeting Abstracts.
• Turk, F. J., Behrangi, A., & Tian, Y. (2014). Precipitation Data from Space for Hydrology: TRMM Era to GPM. In AGU Fall Meeting Abstracts.
• Ye, H., Fetzer, E. J., Behrangi, A., Wong, S., Lambrigtsen, B., Wnag, C. Y., Cohen, J. L., & Gamelin, B. (2014). Increasing precipitation intensity under a warming climate over Northern Eurasia. In AGU Fall Meeting Abstracts.
• Andreadis, K., Behrangi, A., Das, N. N., Fisher, J. B., Granger, S. L., Landerer, F. W., Painter, T. H., & Turk, F. J. (2013). Assimilating remote sensing observations across the terrestrial water cycle in a drought forecasting system. In AGU Fall Meeting Abstracts.
• Aumann, H. H., Manning, E. M., & Behrangi, A. (2013). Detection of Extremes with AIRS and CRIS. In Earth Observing Systems XVIII, 8866.
• Behrangi, A., & Aumann, H. H. (2013). Intercalibration and concatenation of climate quality infrared cloudy radiances from multiple instruments. In Earth Observing Systems XVIII, 8866.
• Behrangi, A., Stephens, G. L., Adler, R. F., Huffman, G. J., Lambrigtsen, B., & Lebsock, M. D. (2013). Complementary information from TRMM and CloudSat to improve our global estimate of precipitation. In AGU Fall Meeting Abstracts.
• Bhawar, R., Reddy, R., Behrangi, A., & Shinde, M. (2013). Cloud types and its radiative forcing during summer monsoon season over South-East Asia. In EGU General Assembly Conference Abstracts, 15.
• Lambrigtsen, B., Brown, S. E., Behrangi, A., & Dang, H. T. (2013). Hurricane Observations with HAMSR in the GRIP and HS3 Field Campaigns. In AGU Fall Meeting Abstracts.
• Tian, Y., Liu, Y., Arsenault, K. R., & Behrangi, A. (2013). A New Approach to Measuring Precipitation over Snow Cover. In AGU Fall Meeting Abstracts.
• Ye, H., Fetzer, E. J., Wong, S., Behrangi, A., Olsen, E. T., Lambrigtsen, B., & Cohen, J. L. (2013). Relationships Among Precipitation, Precipitable Water, and Surface Air Temperature over Northern Eurasia. In AGU Fall Meeting Abstracts.
• Aumann, H. H., Ruzmaikin, A., & Behrangi, A. (2012). Net Incident Radiation and Cloud Feedback from 10 Years of Airs Data. In AGU Fall Meeting Abstracts.
• Behrangi, A., Lebsock, M. D., Wong, S., & Lambrigtsen, B. (2012). The status of over ocean rainfall measurements from current space borne sensors. In AGU Fall Meeting Abstracts.
• Behrangi, A., Kubar, T. L., & Lambrigtsen, B. (2011). Characteristics of tropical clouds using A-train information and their relationships with sea surface temperature. In AGU Fall Meeting Abstracts.
• Fishbein, E., Behrangi, A., Fetzer, E., Kahn, B. H., Schreier, M. M., Teixeira, J., & Yue, Q. (2011). The Unique Capabilities of the Aqua Sensors to Characterize the Planetary Boundary Layer. In AGU Fall Meeting Abstracts.
• Lambrigtsen, B., Brown, S., & Behrangi, A. (2011). Observing Tropical Cyclones from the Global Hawk: HAMSR Results from GRIP. In AGU Fall Meeting Abstracts.
• Wong, S., Behrangi, A., Lambrigtsen, B., & Fetzer, E. (2011). Quantification of Global Sea Surface Water Exchange in the TRMM-era and the Importance of A-Train Observation of Light Rainfall. In AGU Fall Meeting Abstracts.
• Zahraei, A., Hsu, K., Sorooshian, S., & Behrangi, A. (2011). Advection-based Short-Term Quantitative Precipitation Forecasting Algorithms Using Radar and Satellite Information. In AGU Fall Meeting Abstracts.
• Aghakouchak, A., Hsu, K., Behrangi, A., & Sorooshian, S. (2010). Error Decomposition in Satellite-Derived Precipitation Estimates. In AGU Fall Meeting Abstracts.
• Aumann, H. H., Desouza-Machado, S. G., & Behrangi, A. (2010). High resolution diagnosis and monitoring of extreme precipitation events using multi-sensor multi-platform remotely sensed data. In AGU Fall Meeting Abstracts.
• Behrangi, A., Irvine, C. A., Hsu, K., Imam, B., & Sorooshian, S. (2010). Forward morphing of passive microwave derived precipitation field with adjusted intensity from GOES information. In 6th Annual Symposium on Future National Operational Environmental Satellite Systems-NPOESS and GOES-R.
• Behrangi, A., Sorooshian, S., Hsu, K., Bellerby, T. J., Huffman, G. J., & Lambrigtsen, B. (2010). Preparation for GPM: Development of a New Near Real-time High Resolution Multi-sensor Precipitation Estimation Product Based on Analyzing the Existing Precipitation Estimation Techniques. In AGU Fall Meeting Abstracts.
• Behrangi, A., Khakbaz, B., Jaw, T. C., Imam, B., Hsu, K., & Sorooshian, S. (2009). Evaluation of Satellite-Based High Resolution Precipitation Products for Catchment Hydrologic Forecasting. In AGU Fall Meeting Abstracts.
• Behrangi, A., Hsu, K., Sorooshian, S., & Kuligowski, R. J. (2008, January). Multi-spectral precipitation estimation using Artificial Neural Networks. In AMS; 5th GOES Users' Conference.
• Imam, B., Kuranjekar, P., Behrangi, A., Hsu, K., & Sorooshian, S. (2008). The Value of Real-time High Resolution Satellite Precipitation in Capturing Extreme Rainfall Event. In AGU Spring Meeting Abstracts.
• Khakbaz, B., Behrangi, A., Hsu, K., Imam, B., & Sorooshian, S. (2008). Distributed hydrologic modeling in the western US using SNOW17 and SAC-SMA. In AGU Spring Meeting Abstracts.
• Behrangi, A., Hsu, K., Sorooshian, S., & Kuligowski, B. (2007). Delineation of Aerial Extent of Precipitation Using Multi-Spectral Remotely Sensed Data. In AGU Fall Meeting Abstracts.
• Sorooshian, S., Behrangi, A., Khakbaz, B., Vrugt, J. A., & Duan, Q. (2007). Global optimization with a limited budget of function evaluations. In AGU Fall Meeting Abstracts.

Presentations

• Sohi, H. Y., Bennett, A., & Behrangi, A. (2025).

  High-Resolution Climate Data for Arid Regions: A Generative Correction Diffusion Approach for Hydrological Applications in Arizona

  . AGU25.
• Zandi, O., Pfreundschuh, S., Kumah, K. K., & Behrangi, A. (2025).

  University of Arizona--High-latitude Infrared-based Precipitation Analysis Version 2 (UA-HIPA)

  . AGU25.
• Niu, G., Fang, Y., Zeng, X., Behrangi, A., Broxton, P. D., Gupta, H. V., & Wang, Y. H. (2020, Spring). Developing the Snow Cover Fraction Schemes using Machine Learning Approach for Hydrological Predictions. 2020 Meeting of the American Meteorological Society, Phoenix Arizona, Jan 12-16.. Boston, MA: AMS.
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  Wang YH, HV Gupta, PD Broxton, A Behrangi, X Zeng, Y Fang, Guo-Yue Niu (2020), Developing the Snow Cover Fraction Schemes using Machine Learning Approach for Hydrological Predictions, Session on Applications of Machine Learning in Earth System Modeling, presented at 2020 Annual Meeting of the American Meteorological Society, Boston MA, Jan 12–16.
• Behrangi, A., Huffman, G. J., & Adler, R. F. (2023). Precipitation convergence among products: Insights from the latest GPM and GPCP products. AGU23.
• Farahmand, A., Zeraati, M., & Behrangi, A. (2023). Developing a Multivariate Agro-Meteorological Index to Improve Capturing the Onset and Persistence of Droughts. AGU23.
• Niu, G., Fang, Y., Neto, A., Guo, B. o., Farmani, M., & Behrangi, A. (2023). Representing Preferential Flow through Variably Saturated Soils with Surface Ponding in Noah-MP. AGU23.
• Sorooshian, A., Ajayi, T., Arellano, A. F., Behrangi, A., Bonine, K. E., Bugaj, A., Castro, C. L., Dong, X., Ogden, G., Sullivan, S., & others, . (2023). EXCITE: Expanding Reach of NASA Earth Sciences Research At a Hispanic Serving Institution In Southern Arizona. AGU23.
• Ehsani, M. R., Zarei, A., Gupta, H. V., & Behrangi, A. (2022).

  NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks

  . 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.. Fall Meeting of the American Geophysical Union, San Francisco CA.
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  Ehsani MR, A Zarei, H Gupta and A Behrangi (2022), NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks, Session H115: Utilizing Precipitation Datasets and Quantifying Associated Uncertainties in Hydrometeorological and Climate Impact Applications, 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.
• Behrangi, A. (2020, 10). INVITED TALK: Cold region precipitation estimation: challenges, progress, and opportunities,. Civil Seminar, Western University,. London, Ontario, Canada,.
• Gupta, H. V., Wang, Y. H., Broxton, P. D., Fang, Y., Behrangi, A., Zeng, X., & Niu, G. (2020, Fall). Toward Improving Snowpack Prediction and Snow Cover Fraction Parameterization in Land Surface Models. 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11. Online: American Geophysical Union.
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  H Gupta, YH Wang , PD Broxton, Y Fang, A Behrangi, X Zeng and GY Niu (2020), Toward Improving Snowpack Prediction and Snow Cover Fraction Parameterization in Land Surface Models, Session C029: Quantifying Spatial and Temporal Variability of Snow and Snow Processes, presented at 2020 Fall Meeting of the American Geophysical Union, Online, Dec 1-17.
• Wang, Y. H., Gupta, H. V., Broxton, P. D., Fang, Y., Behrangi, A., Zeng, X., & Niu, G. (2020, Spring). Toward Improving Snowpack Prediction and its Parameterization in Land Surface Models. El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, April. Tucson AZ: Department of Hydrology and Atmospheric Sciences, The University of Arizona.
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  YH Wang, HV Gupta, P Broxton, Y Fang, A Behrangi, X Zeng, GYue Niu (2020), Toward Improving Snowpack Prediction and its Parameterization in Land Surface Models, presented at El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, April
• Behrangi, A. (2019, December). INVITED talk : Observation of precipitation extremes: opportunities, challenges, and flood prediction. AGU FALL 2019. San Francisco.
• Niu, G., Gupta, H. V., Zeng, X., Behrangi, A., Broxton, P. D., Fang, Y., & Wang, Y. H. (2019, Fall). Developing a New Snow Cover Fraction Scheme for Hydrological Predictions. 2019 Fall Meeting of the American Geophysical Union. San Francisco, CA: AGU.
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  Wang YH, Y Fang, PD Broxton, A Behrangi, X Zeng, HV Gupta, Guo-Yue Niu (2019), Developing a New Snow Cover Fraction Scheme for Hydrological Predictions, Session H025 - Applications in Snow Hydrology: Linking Seasonal Snow to Natural Processes and Society, presented at 2019 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 9-13.
• Wang, Y. H., Gupta, H. V., Broxton, P. D., Behrangi, A., Zeng, X., Fang, Y., & Niu, G. (2019, Fall). Investigation of Constructing a Snow Cover Fraction Scheme for Land Surface Model. 2019 Annual Meeting of the Arizona Hydrological Society, Casino del Sol, Tucson AZ, Sept 25–27. Tucson AZ: AHS.
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  Wang YH, HV Gupta, PD Broxton, A Behrangi, X Zeng, Y Fang, Guo-Yue Niu (2019), Investigation of Constructing a Snow Cover Fraction Scheme for Land Surface Model, presented at 2019 Annual Meeting of the Arizona Hydrological Society, Casino del Sol, Tucson AZ, Sept 25–27.
• Behrangi, A. (2014, December). Hydrologic application and assessment of remotely sensed high-resolution precipitation products over cold-mountainous regions. International precipitation working group (IPWG).
• Behrangi, A. (2019, January). INVITED TALK: Precipitation estimation from space: progress, challenges, and reaching out to diverse data sets and science communities. Univ. of Arizona, Department of Geosciences.
• Behrangi, A. (2017, February). INVITED: Remote sensing of precipitation for weather and climate extreme analysis. an invitation-only workshop convened by WMO Secretariat for Operational Space-based Weather and Climate Extremes Monitoring, Feb. 15-17 2017, Geneva, Switzerland..
• Behrangi, A. (2017, June). INVITED: Monitoring and Predicting Natural Disasters from Space. National Academy of Sciences, Kavli Frontiers of Science, Irvine, CA, June 26-28 2017.
• Behrangi, A., Reager, J. T., Gardner, A. S., & Fisher, J. (2017, December). Constraining precipitation amount and distribution over cold regions using GRACE. AGU Fall Meeting Abstracts.
• Singh, A., Behrangi, A., Fisher, J., Reager, J. T., & Gardner, A. S. (2017, December). Utilizing a suite of satellite missions to address poorly constrained hydrological fluxes. AGU Fall Meeting Abstracts.
• Behrangi, A. (2016, June). INVITED: Remote sensing of precipitation and GEWEX ground challenges. GEWEX Hydrological Sensitivity Workshop, Exeter, UK, June 2016.
• Behrangi, A. (2016, June). INVITED: WCRP weather and climate extreme panel report: Document. the 13th international meeting on statistical climatology, Canmore, Alberta, Canada, June 2016.
• Behrangi, A. (2016, May). INVITED: using remote sensing of temperature and humidity profile for early detection of drought and its intensity. National Drought Mitigation Center, Nebraska, Lincoln, May 19, 2016.
• Behrangi, A. (2016, Septemebr). INVITED: Progress, challenges, and opportunities in remote sensing of precipitation extremes and hydrology. Invitation-only workshop on Sub-daily rainfall extremes: data, processes and modelling, Newcastle upon, September 13-15, 2016..
• Behrangi, A. (2015, April). INVITED: Probabilistic Seasonal Prediction of Meteorological Drought Using long term precipitation and temperature. Western Water States Council, San Diego, CA, April 2015..
• Behrangi, A. (2015, February). INVITED: Precipitation Extremes. workshop on “Data requirements to address the WCRP Grand Challenge on Weather and Climate Extremes, WCRP/GEWEX/Extreme Panel, University of New South Wales, Sydney, Australia, Sydney, Feb. 2015..
• Behrangi, A. (2015, July). INVITED: Precipitation extreme indices from space. The Expert Team on Climate Change Detection and Indices (ETCCDI) Work Plan Review, UNESCO, Paris, France, July 6-8 2015..
• Behrangi, A. (2012, May). INVITED: High Resolution Precipitation Estimation in Hydroclimatic Studies. University of Texas at San Antonio, San Antonio, TX, 2012..
• Behrangi, A. (2014, May). INVITED: Remote Sensing of Water Cycle and Hydroclimatic Analysis. University of California, Riverside, 2014..
• Behrangi, A. (2012, April). INVITED : Multi-sensor Multi-platform High Resolution Precipitation Estimation for Hydrologic Applications. Idaho State University, Pocatello, ID, 2011..
• Behrangi, A. (2012, March). INVITED: Remote Sensing of Precipitation for Hydrologic Applications. Oregon State University, Corvallis, OR, 2012..
• Lambrigtsen, B., Behrangi, A., Brown, S., Denning, R., Lim, B., Tanabe, J., & Tanner, A. (2011, 2014). Microwave sounder observations during GRIP: preliminary results. 65th Interdepartmental Hurricane Conference Ocean and.
• Behrangi, A. (2010, August). INVITED: Evaluating the Utility of Multi-Spectral Information for Delineating the Areal Extent and Intensity of Precipitation. NASA GSFC LIS Science Seminar (invited talk, Hydrospheric and Biospheric Sciences Laboratory Branch), NASA Goddard Space Flight Center, Greenbelt, Maryland, August 6, 2008..
• Behrangi, A. (2010, January). INVITED: Status of Global High Resolution Precipitation Estimation from Remotely Sensed Information. Environmental Engineering Seminar Series, UC Irvine, Jan 7, 2010..
• Behrangi, A. (2010, March). INVITED: Multi-spectral Algorithms. Advanced Concept Workshop on Remote Sensing of Precipitation at Multiple Scales, Beckman Center, Irvine, CA, March 15-17, 2010.

Others

• Behrangi, A., Huffman, G. J., & Adler, R. F. (2025). A Summary and Comparison of the latest GPCP Daily and Monthly Products (Version 3.2) and the Plan Forward.
• Huffman, G. J., Adler, R. F., Behrangi, A., Bolvin, D. T., Nelkin, E. J., & Ehsani, M. R. (2023). Algorithm Theoretical Basis Document (ATBD) for Global Precipitation Climatology Project Version 3.2 Daily Precipitation Data.
• Behrangi, A., Huffman, G. J., Adler, R. F., Ehsani, M. R., Bolvin, D. T., Nelkin, E. L., & Gu, G. (2022). The Latest GPCP products (V3. 2) and high latitudes analysis.
• Lambrigtsen, B., Behrangi, A., Brown, S., & Hristova-Veleva, S. (2013, December). An RI Case Study: Hurricane Karl (2010). JPL Technical Reports Server.
• Behrangi, A. (2009, December). Improved global high resolution precipitation estimation using multi-satellite multi-spectral information.