Xiquan Dong

Interests

Research

1) Ground-based and satellite remote sensing of aerosol, clouds, radiation and precipitation, as well as their interactions. 2) Validation of Satellite cloud retrievals3) Evaluation of GCM/WRF and Reanalyses 4) Investigation of the impact of Arctic clouds and radiation and longe-scale water vapor transport on Sea-ice changes.

Teaching

Physical meteorology I and II, Atmospheric remote sensing, and Physical Climate for both graduate and undergraduate students.

Courses

Scholarly Contributions

Books

• Dong, X. (2020). Chapter 8 Stratus and stratocumulus clouds In the book of Fast Physics in Large Scale Atmospheric Models: Parameterization, Evaluation, and Observations.
• Dong, X. (2023).

  Stratus, Stratocumulus and Remote Sensing, Chapter 8 of Fast Physic in Large Scale Atmospheric Models: Parameterization, Evaluation, and Observations. 
  
  

  . AGU.
• Dong, X., Minnis, P., & Xi, B. (2005).

  A Climatology of Midlatitude Continental Clouds from the ARM SGP Central Facility.Part II; Cloud Fraction and Radiative Forcing

  . doi:DOI: https://doi.org/10.1175/JCLI3342.1

Journals/Publications

• Brendecke, J., Dong, X., Xi, B., Zhong, X., Li, J., Barker, H., & Pilewskie, P. (2024). Evaluation of clear-sky surface downwelling shortwave fluxes computed by three atmospheric radiative transfer models. Journal of Quantitative Spectroscopy and Radiative Transfer, 328. doi:10.1016/j.jqsrt.2024.109164
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  In this study the clear-sky total, direct, and diffuse shortwave (SW) fluxes at the surface, have been calculated by three radiation transfer models (RTMs) – MODTRAN6.0 (M6.0), Canadian Centre for Climate Modelling and Analysis (CCCma), and Langley-modified Fu-Liou (NASA CERES). These calculations have been evaluated by surface measurements collected from seven sites that represent different climatological regimes with various surface scene types including ocean, grassland/continental, desert, and snow/sea ice. For pristine atmospheric conditions, SW fluxes predicted by CCCma and M6.0 shows little variation, which lays a baseline for further analysis. Note that computing time required by CCCma is ∼1000 times smaller than M6.0. Based on all samples collected from seven sites, mean differences of total, direct, and diffuse fluxes between surface measurements and CCCma / M6.0 / Fu-Liou are [5.3 / 2.4 / 0.9], [-2.2 / -5.1 / -13.7], and [7.5 / 7.5 / 14.6] W m-2, respectively. Histograms of differences between the three RTM calculations and surface measurements show that CCCma computed direct and diffuse fluxes have the smallest biases with standard deviations similar to those for M6.0, while Fu-Liou values have the largest biases and standard deviations. While Fu-Liou outperforms for total flux, especially for desert conditions, it is hampered by large biases for direct and diffuse across all scene types. The three RTMs are consistent with showing the least error for total flux and the largest in diffuse based on bias, correlation, and root mean square error.
• Dong, X. (2019). Aerosol spatial distribution and impacts on clouds from aircraft observations under Meiyu Front weather background over Central China. JGR.
• Dong, X. (2019). Characteristics of Meiyu season mesoscale convective systems over central eastern China.. JGR.
• Dong, X. (2019). Classification of Hydrometeors Using a C-band Poloarimetric Radar and In-situ Measurements during IMFRE in Central China.. JGR.
• Dong, X. (2019). Organized Variations in MBL Cloud Microphysical Properties Observed by Aircraft and Satellite and Simulated by Model. GRL.
• Dong, X. (2019). Quantifying the seasonal climatological trends and impacts of aerosols in North America using a novel aerosol component classification index. Earth and Space Science.
• Dong, X. (2019). Simulating Heavy Meiyu Rainfall: A Note on the Choice of the Model Microphysics Scheme. JGR.
• Dong, X. (2019). The Climate response to increased Cloud Liquid Water in CESM1: A sensitivity study of Wegener-Bergeron-Findeisen Process.. J. Climate.
• Dong, X. (2019). Vertical Distributions of Raindrops and Z-R Relationships Using Micro Rain Radar and 2D-Video Distrometer Measurements during the Integrative Monsoon Frontal Rainfall Experiment (IMFRE).. JGR.
• Dong, X. (2020). The summertime low clouds: bridging large-scale circulation and sea ice variations over the Arctic. Nature Communications Earth & Environment..
• Xi, B., Dong, X., Zheng, X., & Wu, P. (2020). The cloud properties over Southern Ocean during MARCUS field campaign. JGR.
• Zheng, X., Dong, X., Xi, B., Logan, T., & Wang, Y. (2024). Distinctive aerosol–cloud–precipitation interactions in marine boundary layer clouds from the ACE-ENA and SOCRATES aircraft field campaigns. Atmospheric Chemistry and Physics, 24(18). doi:10.5194/acp-24-10323-2024
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  The aerosol–cloud–precipitation interactions within the cloud-topped marine boundary layer (MBL) are examined using aircraft in situ measurements from Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) and Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) field campaigns. SOCRATES clouds exhibit a larger number concentration and smaller cloud droplet effective radius (148.3 cm−3 and 8.0 µm) compared to ACE-ENA summertime (89.4 cm−3 and 9.0 µm) and wintertime clouds (70.6 cm−3 and 9.8 µm). The ACE-ENA clouds, especially during the winter, feature stronger drizzle formation via droplet growth through enhanced collision–coalescence that is attributed to a relatively cleaner environment and deeper cloud layer. Furthermore, the aerosol–cloud interaction (ACI) indices from the two aircraft field campaigns exhibit distinct sensitivities, indicating different cloud microphysical responses to aerosols. The ACE-ENA winter season features relatively fewer aerosols, which are more likely activated into cloud droplets under the conditions of sufficient water vapor availability and strong turbulence. The enriched aerosol loading during ACE-ENA summer and SOCRATES generally leads to smaller cloud droplets competing for the limited water vapor and exhibiting a stronger ACI. Notably, the precipitation susceptibilities are stronger during the ACE-ENA than during the SOCRATES campaigns. The in-cloud drizzle behavior significantly alters sub-cloud cloud condensation nuclei (CCN) budgets through the coalescence-scavenging effect and, in turn, impacts the ACI assessments. The results of this study can enhance understanding and aid in future model simulation and assessment of the aerosol–cloud interaction.
• Zhong, X., Dong, X., Xi, B., Brendecke, J., & Pilewskie, P. (2024). Tracing the physical signatures among the calculated global clear-sky spectral shortwave radiative flux distribution. Journal of Quantitative Spectroscopy and Radiative Transfer, 328. doi:10.1016/j.jqsrt.2024.109167
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  This study utilized the high-spectral resolution radiative transfer model (MODerate resolution atmospheric TRANsmission, MODTRAN6.0.2.5) to compute global clear-sky shortwave (SW) radiative flux and compared it with NASA's Clouds and the Earth's Radiant Energy System (CERES) Synoptic Radiative Fluxes and Clouds (SYN1deg) product. The comparison revealed that the global distributions of clear-sky downwelling SW fluxes at the surface from the M6.0 calculations and SYN1 results are similar, with annual means of 246.51 Wm-2 and 242.42 Wm-2, respectively. Analysis further showed that most of the M6.0 calculations are slightly higher from low to mid-latitudes, particularly in the Northern Hemisphere (NH), but lower in higher latitudes compared to SYN1 results. However, these differences mostly fall within the CERES estimated uncertainty (6 Wm-2) of monthly mean clear-sky downwelling SW flux at the surface. The sensitivity of clear-sky SW/μ0 fluxes to changes in Precipitable Water Vapor (PWV), represented by the clear-sky water vapor radiative kernel, is about -0.7 Wm-2/(kgm-2) over oceans for both M6.0 and CERES SYN1 products, except for SYN1 results over the Southern Hemisphere (SH) ocean. Additionally, the zonal means of land coverage and SW/VIS/NIR albedos from M6.0 calculations indicate that VIS albedos are highest in polar regions (>60°), followed by SW and NIR albedos, while NIR albedos become highest from low to mid-latitudes (
• Dong, X., Zheng, X., Xi, B., & Xie, S. (2023). A Climatology of Midlatitude Maritime Cloud Fraction and Radiative Effect Derived from the ARM ENA Ground-Based Observations. Journal of Climate, 36(2), 531-546.
• 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).
• Tang, S., Varble, A. C., Fast, J. D., Zhang, K., Wu, P., Dong, X., Mei, F., Pekour, M., Hardin, J. C., & Ma, P. (2023). Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol-cloud interactions via field campaign and long-term observations. Geoscientific Model Development, 16(21), 6355-6376.
• Wang, Y., Zheng, X., Dong, X., Xi, B., & Yung, Y. L. (2023). Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations. Atmospheric Chemistry and Physics, 23(15), 8591-8605.
• Zhang, X., Dong, X., Xi, B., & Zheng, X. (2023). Aerosol Properties and Their Influences on Marine Boundary Layer Cloud Condensation Nuclei over the Southern Ocean. Atmosphere, 14(8).
• Zheng, X., Tao, C., Zhang, C., Xie, S., Zhang, Y., Xi, B., & Dong, X. (2023). Assessment of CMIP5 and CMIP6 AMIP Simulated Clouds and Surface Shortwave Radiation Using ARM Observations over Different Climate Regions. Journal of Climate, 36(24), 8475-8495.
• Brendecke, J., Dong, X., Xi, B., & Zheng, X. (2022). Maritime Aerosol and CCN Profiles Derived From Ship-Based Measurements Over Eastern North Pacific During MAGIC. Earth and Space Science, 9(4).
• Dong, X. (2022).

  Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models

  . Advances in Atmospheric Sciences. doi:10.1007/s00376-022-2036-z
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  Abstract The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.
• Dong, X., Wang, Y., Ward, D. E., Wu, P., Xi, B., & Zheng, X. (2022).

  Aerosol-Cloud-Precipitation Interactions in a Closed-cell and Non-homogenous MBL Stratocumulus Cloud

  . Advances in Atmospheric Sciences. doi:10.1007/s00376-022-2013-6
• Hu, Y., Deng, Y., Lin, Y., Zhou, Z., Cui, C., Li, C., & Dong, X. (2022). Indirect effect of diabatic heating on Mei-yu frontogenesis. Climate Dynamics, 59(3-4), 851-868.
• Wang, J., Wood, R., Jensen, M. P., Christine Chiu, J., Liu, Y., Lamer, K., Desai, N., Giangrande, S. E., Knopf, D. A., Kollias, P., Laskin, A., Liu, X., Lu, C., Mechem, D., Mei, F., Starzec, M., Tomlinson, J., Wang, Y., Yum, S. S., , Zheng, G., et al. (2022). Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA). Bulletin of the American Meteorological Society, 103(2), E619-E641.
• Xi, B., Dong, X., Zheng, X., & Wu, P. (2022). Cloud phase and macrophysical properties over the Southern Ocean during the MARCUS field campaign. Atmospheric Measurement Techniques, 15(12), 3761-3777.
• Yang, H., Deng, Y., Cui, C., Wang, X., & Dong, X. (2022). Dynamic Trigger and Moisture Source of Two Typical Meiyu Front Rainstorms Associated with Eastward-Moving Cloud Clusters from the Tibetan Plateau. Journal of Meteorological Research, 36(3), 478-499.
• Zheng, X., Xi, B., Dong, X., Wu, P., Logan, T., & Wang, Y. (2022). Environmental effects on aerosol-cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic. Atmospheric Chemistry and Physics, 22(1), 335-354.
• Bankert, R. L., Campbell, J. R., Dolinar, E. K., Dong, X., Garnier, A., Kuciauskas, A. P., Lolli, S., Marquis, J. W., Mchardy, T. M., Miller, S. D., Peterson, D. A., & Surratt, M. L. (2021).

  Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” Band

  . Journal of Atmospheric and Oceanic Technology. doi:10.1175/jtech-d-20-0130.1
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  AbstractWe describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.
• Brendecke, J., Dong, X., Xi, B., & Wu, P. (2021). Maritime Cloud and Drizzle Microphysical Properties Retrieved From Ship-Based Observations During MAGIC. Earth and Space Science, 8(5).
• Clark, R. T., Dong, X., Ho, C., Sun, J., Yuan, H., & Takemi, T. (2021). Preface to the Special Issue on Summer 2020: Record Rainfall in Asia ??? Mechanisms, Predictability and Impacts. Advances in Atmospheric Sciences, 38(12), 1977-1979.
• Cui, C., Dong, X., Wang, B., Xi, B., Deng, Y., & Ding, Y. (2021). Integrative Monsoon Frontal Rainfall Experiment (IMFRE-I): A Mid-Term Review. Advances in Atmospheric Sciences, 38(3), 357-374.
• Cui, W., Dong, X., Feng, Z., & Xi, B. (2021).

  Climatology of Linear Mesoscale Convective System Morphology in the United States based on Random Forests Method

  . Journal of Climate, 1-52. doi:10.1175/jcli-d-20-0862.1
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  AbstractThis study uses machine learning methods, specifically the random forest (RF), on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004-2016. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, and reanalysis environmental information from 5-yr manually identified nonlinear and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, in which half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (through March to October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus have higher potential in producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.
• Dong, X. (2021). Cloud and Drizzle Microphysical Properties Retrieved from Ship-Based Observations during MAGIC.. Earth and Space Science.
• Dong, X. (2021). Detecting Transparent Cirrus Clouds Over Ocean Using the GOES-16 ABI 1.378 µm Channel. 38, 1093-1109.. J. Atmos. and Oceanic Tech.
• Dong, X. (2021). Dynamics of the Spatiotemporal Morphology of Mei-yu Fronts: An Initial Survey. Climate Dynamics.
• Dong, X. (2021). Precipitation Influence on and Response to Early and Late Arctic Sea Ice Melt Onset During Melt Season. International Journal of Climate.
• Dong, X. (2021). Summertime atmosphere-sea ice coupling in the Arctic simulated by CMIP5/6 models: Importance of large-scale circulation and low-level clouds. Clim. Dyn., 56: 1467-1485..
• Dong, X. (2021). The Phase Two of the Integrative Monsoon Frontal Rainfall Experiment (IMFRE-Ⅱ) in the Middle and Lower Reaches of the Yangtze River in 2020. Adv. In Atmos. Sci..
• Dong, X., Wu, P., Wang, Y., Xi, B., & Huang, Y. (2021). New Observational Constraints on Warm Rain Processes and Their Climate Implications. Geophysical Research Letters, 48(6).
• Dong, X., Wu, P., Wang, Y., Xi, B., & Huang, Y. (2021). Observational constraints on warm rain processes in climate models using new ground-based retrievals. GRL.
• Huang, Y., Ding, Q., Dong, X., Xi, B., & Baxter, I. (2021). Summertime low clouds mediate the impact of the large-scale circulation on Arctic sea ice. Communications Earth and Environment, 2(1).
• Zhang, H., Dong, X., Xi, B., Xin, X., Liu, Q., He, H., Xie, X., Li, L., & Yu, S. (2021). Retrieving high-resolution surface photosynthetically active radiation from the MODIS and GOES-16 ABI data. Remote Sensing of Environment, 260.
• Zhang, Z., Song, Q., Mechem, D., Larson, V., Wang, J., Liu, Y., K., W. M., Dong, X., & Wu, P. (2021). Vertical dependence of horizontal variation of cloud microphysics: Observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models. Atmospheric Chemistry and Physics, 21(4), 3103-3121.
• Cui, W., Dong, X., Xi, B., Feng, Z., & Fan, J. (2020). Can the GPM IMERG final product accurately represent MCSs??? precipitation characteristics over the central and eastern United States?. Journal of Hydrometeorology, 21(1), 39-57.
• Dong, X. (2020). Hydrometeor budget of the Meiyu frontal rainstorms associated with two different atmospheric circulation patterns. JGR.
• Dong, X., & Xi, B. (2020). Quantifying Long‐Term Seasonal and Regional Impacts of North American Fire Activity on Continental Boundary Layer Aerosols and Cloud Condensation Nuclei.. Earth and Space Science. doi:https://doi.org/10.1029/2020EA001113.
• Fu, Z., Dong, X., Zhou, L., Cui, W., Wang, J., Wan, R., Leng, L., & Xi, B. (2020). Statistical Characteristics of Raindrop Size Distributions and Parameters in Central China During the Meiyu Seasons. Journal of Geophysical Research: Atmospheres, 125(19).
• He, H., Wang, H., Guan, Z., Chen, H., Fu, Q., Wang, M., Dong, X., Cui, C., Wang, L., Wang, B., Chen, G., Li, Z., & Zhang, D. (2020). Facilitating international collaboration on climate change research. Bulletin of the American Meteorological Society, 101(5), E650-E654.
• Li, C., Deng, Y., Cui, C., Wang, X., Dong, X., & Jiang, X. (2020). Hydrometeor Budget of the Meiyu Frontal Rainstorms Associated With Two Different Atmospheric Circulation Patterns. Journal of Geophysical Research: Atmospheres, 125(16).
• Liu, L., Cui, C., Deng, Y., Zhou, Z., Hu, Y., Wang, B., Ren, J., Cai, Z., Bai, Y., Yang, J., & Dong, X. (2020). Localization and Invigoration of Mei-yu Front Rainfall due to Aerosol-Cloud Interactions: A Preliminary Assessment Based on WRF Simulations and IMFRE 2018 Field Observations. Journal of Geophysical Research: Atmospheres, 125(13).
• Sun, Y., Dong, X., Cui, W., Zhou, Z., Fu, Z., Zhou, L., Deng, Y., & Cui, C. (2020). Vertical Structures of Typical Meiyu Precipitation Events Retrieved From GPM-DPR. Journal of Geophysical Research: Atmospheres, 125(1).
• Wang, Y., Zheng, X., Dong, X., Xi, B., Wu, P., Logan, T., & Yung, Y. L. (2020). Impacts of long-range transport of aerosols on marine-boundary-layer clouds in the eastern North Atlantic. Atmospheric Chemistry and Physics, 20(23), 14741-14755.
• Wu, P., Dong, X., & Xi, B. (2020). A Climatology of Marine Boundary Layer Cloud and Drizzle Properties Derived from Ground-Based Observations over the Azores. Journal of Climate, 33(23), 10133-10148. doi:10.1175/jcli-d-20-0272.1
• Wu, P., Dong, X., Xi, B., Tian, J., & Ward, D. M. (2020). Profiles of MBL Cloud and Drizzle Microphysical Properties Retrieved From Ground-Based Observations and Validated by Aircraft In Situ Measurements Over the Azores. Journal of Geophysical Research: Atmospheres, 125(9).
• Xi, B., Cui, W., Dong, X., & Liu, M. (2020).

  Cloud and Precipitation Properties of MCSs Along the Meiyu Frontal Zone in Central and Southern China and Their Associated Large-Scale Environments

  . Journal of Geophysical Research, 125(6). doi:10.1029/2019jd031601
• Xi, B., Kay, J., Dong, X., & Huang, Y. (2020). The Climate response to increased Cloud Liquid Water in CESM1: A sensitivity study of Wegener-Bergeron-Findeisen Process.. Climate Dynamics. doi:DOI: 10.1007/s00382-021-05648-5.
• Yang, J., Li, J., Li, P., Sun, G., Cai, Z., Yang, X., Cui, C., Dong, X., Xi, B., Wan, R., Wang, B., & Zhou, Z. (2020). Spatial Distribution and Impacts of Aerosols on Clouds Under Meiyu Frontal Weather Background Over Central China Based on Aircraft Observations. Journal of Geophysical Research: Atmospheres, 125(15).
• Zheng, X., Xi, B., Dong, X., Logan, T., Wang, Y., & Wu, P. (2020). Investigation of aerosol-cloud interactions under different absorptive aerosol regimes using Atmospheric Radiation Measurement (ARM) southern Great Plains (SGP) ground-based measurements. Atmospheric Chemistry and Physics, 20(6), 3483-3501.
• Zhou, L., Dong, X., Fu, Z., Wang, B., Leng, L., Xi, B., & Cui, C. (2020). Vertical Distributions of Raindrops and Z-R Relationships Using Microrain Radar and 2-D-Video Distrometer Measurements During the Integrative Monsoon Frontal Rainfall Experiment (IMFRE). Journal of Geophysical Research: Atmospheres, 125(3).
• Cui, W., Dong, X., Xi, B., Fan, J., Tian, J., Wang, J., & McHardy, T. M. (2019). Understanding Ice Cloud-Precipitation Properties of Three Modes of Mesoscale Convective Systems During PECAN. Journal of Geophysical Research: Atmospheres, 124(7), 4121-4140.
• Dolinar, E. K., Dong, X., Xi, B., Jiang, J. H., Loeb, N. G., Campbell, J. R., & Su, H. (2019). A global record of single-layered ice cloud properties and associated radiative heating rate profiles from an A-Train perspective. Climate Dynamics, 53(5-6), 3069-3088.
• Dong, X. (2019). A Record of Global Single-layered Ice Cloud Properties and Associated Radiative Heating Rate Profiles from an A-Train Perspective.. Climate DYNAMIC. doi:DOI: 10.1007/s00382-019-04682-8
• Dong, X. (2019). A Regime Based Evaluation of Southern and Northern Great Plains Warm Season Precipitation Events in WRF. Wea. Forecasting, 34, 805-834. doi:DOI: 10.1175/WAF-D-19-0025.1.
• Dong, X. (2019). A Synoptic-View-Based Assessment of the Summer Extreme Rainfall over the Middle Reaches of Yangtze River in CMIP5 Models. Clim. Dyn. doi:https://doi.org/10.1007/s00382-019-04803-3.
• Dong, X. (2019). Can the GPM IMERG product accurately represent MCSs’ precipitation characteristics over the CONUS?. Atmos. Chem. Phys. doi:https://doi.org/10.5194/acp-2019-478.
• Dong, X. (2019). Characteristics of Ice Cloud-Precipitation of Warm Season Mesoscale Convective Systems over the Great Plains. J. Hydrometeo. doi:DOI: 10.1175/JHM-D-19-0123.1.
• Dong, X. (2019). East Asian Study of Tropospheric Aerosols and Impact on Regional Cloud, Precipitation, and Climate (EAST-AIRCPC).. JGR, 124. doi:2019JD030758
• Dong, X. (2019). Estimation of Liquid Water Path in Stratiform Precipitation Systems using Radar Measurements. Atmos. Meas. Tech, 12, 3743-3759. doi:https://doi.org/10.5194/amt-2018-388.
• Dong, X. (2019). Investigation of aerosol cloud Interactions under different absorptive aerosol regimes using ARM SGP ground-based measurements.. Atmos. Chem. Phys. doi:https://doi.org/10.5194/acp-2019-478.
• Dong, X. (2019). Investigation of the ice cloud-precipitation properties of three modes of MCSs during PECAN.. JGR-Atmosphere, 124. doi:10.1029/2019JD030330
• Dong, X., Ward, D. M., Wu, P., Xi, B., & Zheng, X. (2019).

  Impacts of aerosols on MBL Cloud Microphysical and Drizzle Properties using Aircraft in-Situ Measurements during ACE-ENA

  . AGU abstract.
• Hu, Y., Deng, Y., Zhou, Z., Cui, C., & Dong, X. (2019). A statistical and dynamical characterization of large-scale circulation patterns associated with summer extreme precipitation over the middle reaches of Yangtze river. Climate Dynamics, 52(9-10), 6213-6228.
• Hu, Y., Deng, Y., Zhou, Z., Li, H., Cui, C., & Dong, X. (2019). A synoptic assessment of the summer extreme rainfall over the middle reaches of Yangtze River in CMIP5 models. Climate Dynamics, 53(3-4), 2133-2146.
• Huang, Y., Dong, X., Bailey, D. A., Holland, M. M., Xi, B., DuVivier, A. K., Kay, J. E., Landrum, L. L., & Deng, Y. (2019). Thicker Clouds and Accelerated Arctic Sea Ice Decline: The Atmosphere-Sea Ice Interactions in Spring. Geophysical Research Letters, 46(12), 6980-6989.
• Huang, Y., Dong, X., Xi, B., & Deng, Y. (2019). A survey of the atmospheric physical processes key to the onset of Arctic sea ice melt in spring. Climate Dynamics, 52(7-8), 4907-4922.
• Pu, Z., Lin, C., Dong, X., & Krueger, S. K. (2019). Sensitivity of Numerical Simulations of a Mesoscale Convective System to Ice Hydrometeors in Bulk Microphysical Parameterization. Pure and Applied Geophysics, 176(5), 2097-2120.
• Wang, X., Dong, X., Deng, Y. I., Cui, C., Wan, R., & Cui, W. (2019). Contrasting pre-Mei-Yu and Mei-Yu extreme precipitation in the Yangtze river valley: Influencing systems and precipitation mechanisms. Journal of Hydrometeorology, 20(9), 1961-1980.
• Zhang, Z., Song, H., Ma, P., Larson, V. E., Wang, M., Dong, X., & Wang, J. (2019). Subgrid variations of the cloud water and droplet number concentration over the tropical ocean: satellite observations and implications for warm rain simulations in climate models. ATMOSPHERIC CHEMISTRY AND PHYSICS, 19(2), 1077-1096. doi:https://doi.org/10.5194/acp-19-1-2019.
• Chen, X., Huang, X., Dong, X., Xi, B., Dolinar, E. K., Loeb, N. G., Kato, S., Stackhouse, P. W., & Bosilovich, M. G. (2018). Using AIRS and ARM SGP Clear-Sky Observations to Evaluate Meteorological Reanalyses: A Hyperspectral Radiance Closure Approach. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 123(20), 11720-11734.
• Chen, X., Huang, X., Dong, X., Xi, B., Dolinar, E. K., Loeb, N. G., Kato, S., Stackhouse, P. W., & Bosilovich, M. G. (2018). Using AIRS and ARM SGP Clear-Sky Observations to Evaluate Meteorological Reanalyses: A Hyperspectral Radiance Closure Approach. Journal of Geophysical Research: Atmospheres, 123(20), 11,720-11,734.
• Clark, A. J., Jirak, I. L., Dembek, S. R., Creagager, G. J., Kong, F., Thomas, K. W., Knopfmeier, K. H., Gallo, B. T., Melicick, C. J., Xue, M., Brewster, K. A., Jung, Y., Kennedy, A., Dong, X., Markel, J., Gilmore, M., Romine, G. S., Fossell, K. R., Sobash, R. A., , Carley, J. R., et al. (2018). The community leveraged unified ensemble (CLUE) in the 2016 NOAA/hazardous weather testbed spring forecasting experiment. Bulletin of the American Meteorological Society, 99(7), 1433-1448.
• Dong, X. (2018). A Survey of the Atmospheric Physical and Dynamical Processes Key to the Onset of Arctic Sea Ice Melting in Spring. Clim Dyn.. doi:https://doi.org/10.1007/s00382-018-4422-x
• Dong, X. (2018). Comparisons of Water Path in Deep Convective Systems among CERES-MODIS, GOES, and Ground-based retrievals.. JGR, 123. doi:https://doi.org/10.1002/2017JD027498.
• Dong, X. (2018). Influence of wind directions on thermodynamic properties and Arctic mixed-phase clouds at Barrow, Alaska in autumn season.. JGR, 123. doi:https://doi.org/10.1029/2018JD028631
• Dong, X. (2018). Investigation of liquid cloud microphysical properties of deep convective systems: 2. Parameterization of rain drop size distribution and its application for convective rain estimation.. JGR, 123. doi:https://doi.org/10.1029/2018JD028727
• Dong, X. (2018). Preface to the special issue: Aerosols, clouds, radiation, precipitation, and their interactions. ADVANCES IN ATMOSPHERIC SCIENCES, 35(2), 133-134.
• Fan, T., Zhao, C., Dong, X., Liu, X., Yang, X., Zhang, F., Shi, C., Wang, Y., & Wu, F. (2018). Quantify contribution of aerosol errors to cloud fraction biases in CMIP5 Atmospheric Model Intercomparison Project simulations. INTERNATIONAL JOURNAL OF CLIMATOLOGY, 38(7), 3140-3156.
• Fan, T., Zhao, C., Dong, X., Liu, X., Yang, X., Zhang, F., Shi, C., Wang, Y., & Wu, F. (2018). Quantify contribution of aerosol errors to cloud fraction biases in CMIP5 Atmospheric Model Intercomparison Project simulations. International Journal of Climatology, 38(7), 3140-3156.
• Logan, T., Dong, X., & Xi, B. (2018). Aerosol properties and their impacts on surface CCN at the ARM Southern Great Plains site during the 2011 Midlatitude Continental Convective Clouds Experiment. ADVANCES IN ATMOSPHERIC SCIENCES, 35(2), 224-233.
• McHardy, T. M., Dong, X., Xi, B., Thieman, M. M., Minnis, P., & Palikonda, R. (2018). Comparison of Daytime Low-Level Cloud Properties Derived From GOES and ARM SGP Measurements. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 123(15), 8221-8237.
• Qiu, S., Xi, B., & Dong, X. (2018). Influence of Wind Direction on Thermodynamic Properties and Arctic Mixed-Phase Clouds in Autumn at Utqiagvik, Alaska. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 123(17), 9589-9603.
• Tian, J., Dong, X., Xi, B., Minnis, P., Smith, W. L., Sun-Mack, S. .., Thieman, M., & Wang, J. (2018). Comparisons of Ice Water Path in Deep Convective Systems Among Ground-Based, GOES, and CERES-MODIS Retrievals. Journal of Geophysical Research: Atmospheres, 123(3), 1708-1723.
• Wang, Y., Vogel, J. M., Lin, Y., Pan, B., Hu, J., Liu, Y., Dong, X., Jiang, J. H., Yung, Y. L., & Zhang, R. (2018). Aerosol microphysical and radiative effects on continental cloud ensembles. Advances in Atmospheric Sciences, 35(2), 234-247.
• Wu, P., Xi, B., Dong, X., & Zhang, Z. (2018). Evaluation of autoconversion and accretion enhancement factors in general circulation model warm-rain parameterizations using ground-based measurements over the Azores. ATMOSPHERIC CHEMISTRY AND PHYSICS, 18(23).
• Cui, W., Dong, X., Xi, B., & Kennedy, A. (2017). Evaluation of Reanalyzed Precipitation Variability and Trends Using the Gridded Gauge-Based Analysis over the CONUS. JOURNAL OF HYDROMETEOROLOGY, 18(8), 2227-2248.
• Dong, X. (2017). Cloud-radiation-precipitation associations over the Asian monsoon region: an observational analysis. Clim Dyn, 1-19. doi:10.1007/s00382-016-3509-5
• Dong, X. (2017). Seasonal Characteristics of Cloud Radiative Effects and their associations with cloud fraction and precipitation over Asian Monsoon Regions. Climate Dynamics. doi:DOI :10.1007/s00382-016-3509-5
• Dong, X. (2017). Use of observation-based aerosol profiles in simulations of a mid- latitude squall line during MC3E: Similarity of microphysics regime to tropical conditions.. ACP. doi:ACP-2016-948
• Fan, J., Han, B., Varble, A., Morrison, H., North, K., Kollias, P., Chen, B., Dong, X., Giangrande, S. E., Khain, A., Lin, Y., Mansell, E., Milbrandt, J. A., Stenz, R., Thompson, G., & Wang, Y. (2017). Cloud-resolving model intercomparison of an MC3E squall line case: Part I-Convective updrafts. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 122(17), 9351-9378.
• Fridlind, A. M., Li, X., Wu, D. i., van, L. M., Ackerman, A. S., Tao, W., McFarquhar, G. M., Wu, W., Dong, X., Wang, J., Ryzhkov, A., Zhang, P., Poellot, M. R., Neumann, A., & Tomlinson, J. M. (2017). Derivation of aerosol profiles for MC3E convection studies and use in simulations of the 20 May squall line case. ATMOSPHERIC CHEMISTRY AND PHYSICS, 17(9), 5947-5972.
• Huang, Y., Dong, X., Xi, B., Dolinar, E. K., & Stanfield, R. E. (2017). The footprints of 16 year trends of Arctic springtime cloud and radiation properties on September sea ice retreat. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 122(4), 2179-2193.
• Huang, Y., Dong, X., Xi, B., Dolinar, E. K., Stanfield, R. E., & Qiu, S. (2017). Quantifying the Uncertainties of Reanalyzed Arctic Cloud and Radiation Properties Using Satellite Surface Observations. JOURNAL OF CLIMATE, 30(19), 8007-8029.
• Wu, P., Dong, X., Xi, B., Liu, Y., Thieman, M., & Minnis, P. (2017). Effects of environment forcing on marine boundary layer cloud-drizzle processes. Journal of Geophysical Research, 122(8), 4463-4478.
• Zhang, L., Dong, X., Kennedy, A., Xi, B., & Li, Z. (2017). Evaluation of NASA GISS post-CMIP5 single column model simulated clouds and precipitation using ARM Southern Great Plains observations. Advances in Atmospheric Sciences, 34(3), 306-320.
• Zhang, Z., Dong, X., Xi, B., Song, H., Ma, P., Ghan, S. J., Platnick, S., & Minnis, P. (2017). Intercomparisons of marine boundary layer cloud properties from the ARM CAP-MBL campaign and two MODIS cloud products. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 122(4), 2351-2365.
• Cui, W., Dong, X., Xi, B., & Stenz, R. (2016). Comparison of the GPCP 1DD precipitation product and NEXRAD Q2 precipitation estimates over the continental United States. Journal of Hydrometeorology, 17(6), 1837-1853.
• Dolinar, E. K., Dong, X., Xi, B., Jiang, J. H., & Loeb, N. G. (2016). A clear-sky radiation closure study using a one-dimensional radiative transfer model and collocated satellite-surface-reanalysis data sets. Journal of Geophysical Research: Atmospheres, 121(22), 698-714.
• Dong, X. (2016). Cloud Fraction at the ARM SGP Site: reducing uncertainty with self-organizing maps. Theor. Appl. Climatol, 124, 43-54. doi:doi:10.1007/s00704-015-1384-3.
• Dong, X. (2016). Determining Best Method for Estimating Observed Level of Maximum Convective Detrainment based on Radar Reflectivity. Monthly Weather Review, 144, 2915-2926. doi:10.1175/MWR-D-15-0427.1
• Dong, X. (2016). Improving Satellite Quantitative Precipitation Estimates With Optical Depth Retrievals.. J. Hydrometeorology, 17, 1837-1853. doi:DOI:10.1175/JHM-D-15-0235.1
• Dong, X. (2016). Investigation of liquid cloud microphysical properties of deep convective systems: 1. Parameterization raindrop size distribution and its application for stratiform rain estimation. JGR-Atmosphere, 121, 10739-10760. doi:doi:10.1002/2016JD024941
• Dong, X., Xi, B., & Qiu, S. (2016).

  A Radiation Closure Study of Arctic Stratus Cloud Microphysical Properties Using the Collocated Satellite-Surface Data and Fu-Liou Radiative Transfer Model (Invited Presentation)

  . Journal of Geophysical Research, 121(17), 10,175-10,198. doi:10.1002/2016JD025255
• Stanfield, R. E., Jiang, J. H., Dong, X., Xi, B., Su, H., Donner, L., Rotstayn, L., Wu, T., Cole, J., & Shindo, E. (2016). A quantitative assessment of precipitation associated with the ITCZ in the CMIP5 GCM simulations. Climate Dynamics, 47(5-6), 1863-1880.
• Tian, J., Dong, X., Xi, B., Wang, J., Homeyer, C. R., McFarquhar, G. M., & Fan, J. (2016). Retrievals of ice cloud microphysical properties of deep convective systems using radar measurements. Journal of Geophysical Research, 121(18), 10,820-10,839.
• Cui, C., Wan, R., Wang, B., Dong, X., Li, H., Wang, X., Xu, G., Wang, X., Wang, Y., Xiao, Y., Zhou, Z., Fu, Z., Wan, X., Zhang, W., Peng, T., Leng, L., Stenz, R., & Wang, J. (2015). The mesoscale heavy rainfall observing system (MHROS) over the middle region of the Yangtze river in China. Journal of Geophysical Research, 120(19), 10399-10417.
• Dolinar, E. K., Dong, X., Xi, B., Jiang, J. H., & Su, H. (2015). Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations. Climate Dynamics, 44(7-8), 2229-2247.
• Dong, X., Schwantes, A. C., Xi, B., & Wu, P. (2015). Investigation of the marine boundary layer cloud and CCN properties under coupled and decoupled conditions over the azores. Journal of Geophysical Research, 120(12), 6179-6191.
• Fan, J., Liu, Y., Xu, K., North, K., Collis, S., Dong, X., Zhang, G. J., Chen, Q., Kollias, P., & Ghan, S. J. (2015). Improving representation of convective transport for scale-aware parameterization: 1. Convection and cloud properties simulated with spectral bin and bulk microphysics. Journal of Geophysical Research, 120(8), 3485-3509.
• Qiu, S., Dong, X., Xi, B., & Li, J. (2015). Characterizing Arctic mixed-phase cloud structure and its relationship with humidity and temperature inversion using ARM NSA observations. Journal of Geophysical Research, 120(15), 7737-7746.
• Stanfield, R. E., Dong, X., Xi, B., Del, G. A., Minnis, P., Doelling, D., & Loeb, N. (2015). Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part II: TOA radiation budget and CREs. Journal of Climate, 28(5), 1842-1864.
• Wang, J., Dong, X., & Xi, B. (2015). Investigation of ice cloud microphysical properties of DCSs using aircraft in situ measurements during MC3E over the ARM SGP site. Journal of Geophysical Research, 120(8), 3533-3552.
• Wood, R., Wyant, M., Bretherton, C. S., R??millard, J., Kollias, P., Fletcher, J., Stemmler, J., De Szoeke, S., Yuter, S., Miller, M., Mechem, D., Tselioudis, G., Chiu, J. C., Mann, J., O'Connor, E., Hogan, R. J., Dong, X., Miller, M., Ghate, V., , Jefferson, A., et al. (2015). Clouds, aerosols, and precipitation in the marine boundary layer: An arm mobile facility deployment. Bulletin of the American Meteorological Society, 96(3), 419-439.
• Wu, P., Dong, X., & Xi, B. (2015). Marine boundary layer drizzle properties and their impact on cloud property retrieval. Atmospheric Measurement Techniques, 8(9), 3555-3562.
• Xu, G., Xi, B., Zhang, W., Cui, C., Dong, X., Liu, Y., & Yan, G. (2015). Comparison of atmospheric profiles between microwave radiometer retrievals and radiosonde soundings. Journal of Geophysical Research, 120(19), 10313-10323.
• Dong, X., Kennedy, A. D., & Xi, B. (2014).

  Cloud fraction at the ARM SGP site. Instrument and sampling considerations from 14 years of ARSCL

  . Theoretical and Applied Climatology, 115(1), 91-105. doi:10.1007/s00704-013-0853-9
  More info

  The Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site has a rich history of actively sensed cloud observations. Fourteen years (1997–2010) of observations from the Millimeter Cloud Radar (MMCR), Micropulse Lidar (MPL), and Belfort/Vaisala Ceilometers are used to understand how instrument selection and sampling impacts estimates of Cloud Fraction (CF) at this location. Although all instruments should be used in combination for the best estimates of CF, instrument downtime limits available samples and increases observational errors, demanding that users make sacrifices when calculating CF at longer intervals relevant to climate studies. Selection of MMCR or MMCR + MPL cloud masks changes very little in the overall understanding of total CF. Addition of the MPL increases the 14-year average CF by 9 %, mainly through an increase in optically thin high clouds year-round, and mid-level clouds during the summer months. Splitting the period into two equal 7-year periods reveals negligible change in MMCR + MPL CF. For the MMCR, however, CF deceases by 6.1 %. This sudden change in CF occurs around the time the radar was upgraded, suggesting that this decrease is tied to hardware sensitivity or scanning strategy changes. Users must be cognizant of this and other issues when calculating CF from the variety of observations available at the ARM SGP site.
• Dong, X., Xi, B., & Wu, P. (2014). Investigation of the diurnal variation of marine boundary layer cloud microphysical properties at the Azores. Journal of Climate, 27(23), 8827-8835.
• Dong, X., Zib, B. J., Xi, B., Stanfield, R., Deng, Y., Zhang, X., Lin, B., & Long, C. N. (2014). Critical mechanisms for the formation of extreme arctic sea-ice extent in the summers of 2007 and 1996. Climate Dynamics, 43(1-2), 53-70.
• Jiang, T., Evans, K. J., Deng, Y., & Dong, X. (2014). Intermediate frequency atmospheric disturbances: A dynamical bridge connecting western U.S. extreme precipitation with East Asian cold surges. Journal of Geophysical Research, 119(7), 3723-3735.
• Logan, T., Xi, B., & Dong, X. (2014). Aerosol properties and their influences on marine boundary layer cloud condensation nuclei at the ARM mobile facility over the Azores. Journal of Geophysical Research, 119(8), 4859-4872.
• Stenz, R., Dong, X., Xi, B., & Kuligowski, R. J. (2014). Assessment of SCaMPR and NEXRAD Q2 precipitation estimates using Oklahoma Mesonet observations. Journal of Hydrometeorology, 15(6), 2484-2500.
• Xi, B., Dong, X., Minnis, P., & Sun-Mack, S. .. (2014). Comparison of marine boundary layer cloud properties from CERES-MODIS Edition 4 and DOE ARM AMF measurements at the Azores. Journal of Geophysical Research, 119(15), 9509-9529.
• Dong, X., Logan, T., & Xi, B. (2013).

  A Comparison of the Mineral Dust Absorptive Properties between Two Asian Dust Events

  . Atmosphere, 4(1), 1-16. doi:10.3390/atmos4010001
  More info

  Asian dust events are generated by deep convection from strong low pressure systems that form over mineral dust source regions. This study compares the mineral dust optical properties of two strong Asian dust events from the winter (December 2007) and spring (March 2010) seasons using AERONET retrieved parameters from three sites along the dust event path: SACOL (dust source region), Xianghe (downwind mixed aerosol region), and Taihu (downwind pollution region). The parameters include: aerosol effective radius, optical depth (t), absorptive optical depth (tabs), their respective wavelength dependences or Angstrom exponents (a and aabs), and the spectral single scattering albedo (wo(λ)). The a440–870 values in both cases do not exceed 0.62 indicating coarse mode particle dominance at all three sites. The winter case is shown to have carbonaceous influences at all three sites as given by aabs440–870 between 1.3 and 1.8 with strong spectral tabs absorption. The spring case is more dust dominant with aabs440–870 of 1.7–2.5 (noting that the largest value occurred at Taihu) with strong tabs absorption primarily in the visible wavelengths. Comparison studies between the observed and theoretically calculated wo(λ) for the winter and spring cases have shown an excellent agreement except for the winter case at Taihu due to pollution influences. The comparison studies also suggest that wo(λ) is more sensitive to particle absorptive properties rather than particle size. The sharp increase in the aerosol radiative effect (ARE) during the dust events with AREBOA > ARETOA suggests a stronger aerosol cooling effect at the surface than at the TOA.
• Logan, T., Xi, B., Dong, X., Li, Z., & Cribb, M. (2013). Classification and investigation of Asian aerosol absorptive properties. Atmospheric Chemistry and Physics, 13(4), 2253-2265.
• Wood, K. R., Overland, J. E., Salo, S. A., Bond, N. A., Williams, W. J., & Dong, X. (2013). Is there a "new normal"climate in the Beaufort Sea?. Polar Research, 32(SUPPL.).
• Wu, D., Dong, X., Xi, B., Feng, Z., Kennedy, A., Mullendore, G., Gilmore, M., & Tao, W. (2013). Impacts of microphysical scheme on convective and stratiform characteristics in two high precipitation squall line events. Journal of Geophysical Research Atmospheres, 118(19), 11,119-11,135.
• Yu-Jun, Q. .., Xi-Quan, D. .., Bai-Ke, X. .., & Zhen-Hui1, W. .. (2013). Effects of Clouds and Aerosols on Surface Radiation Budget Inferred from DOE AMF at Shouxian, China. Atmospheric and Oceanic Science Letters, 6(1), 39-43.
• Feng, Z., Dong, X., Xi, B., McFarlane, S. A., Kennedy, A., Lin, B., & Minnis, P. (2012). Life cycle of midlatitude deep convective systems in a Lagrangian framework. Journal of Geophysical Research Atmospheres, 117(23).
• Huang, D., Zhao, C., Dunn, M., Dong, X., MacE, G. G., Jensen, M. P., Xie, S., & Liu, Y. (2012). An intercomparison of radar-based liquid cloud microphysics retrievals and implications for model evaluation studies. Atmospheric Measurement Techniques, 5(6), 1409-1424.
• Zib, B. J., Dong, X., Xi, B., & Kennedy, A. (2012). Evaluation and intercomparison of cloud fraction and radiative fluxes in recent reanalyses over the arctic using BSRN surface observations. Journal of Climate, 25(7), 2291-2305.
• Dong, X., Xi, B., Kennedy, A., Feng, Z., Entin, J. K., Houser, P. R., Schiffer, R. A., L'Ecuyer, T., Olson, W. S., Hsu, K., Liu, W. T., Lin, B., Deng, Y., & Jiang, T. (2011). Investigation of the 2006 drought and 2007 flood extremes at the Southern Great Plains through an integrative analysis of observations. Journal of Geophysical Research Atmospheres, 116(3).
• Feng, Z., Dong, X., Xi, B., Schumacher, C., Minnis, P., & Khaiyer, M. (2011). Top-of-atmosphere radiation budget of convective core/stratiform rain and anvil clouds from deep convective systems. Journal of Geophysical Research Atmospheres, 116(23).
• Minnis, P., Sun-Mack, S. .., Chen, Y., Khaiyer, M. M., Yi, Y., Ayers, J. K., Brown, R. R., Dong, X., Gibson, S. C., Heck, P. W., Lin, B., Nordeen, M. L., Nguyen, L., Palikonda, R., Smith Jr., ,., Spangenberg, D. A., Trepte, Q. Z., & Xi, B. (2011). CERES edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data-Part II: Examples of average results and comparisons with other data. IEEE Transactions on Geoscience and Remote Sensing, 49(11 PART 2), 4401-4430.
• Dong, X. (2010).

  A 10-yr Climatology of Arctic Cloud Fraction and Radiative Forcing at Barrow, Alaska

  . EGUGA.
• Kennedy, A. D., Dong, X., Xi, A. B., Minnis, P., Genio, A. D., Wolf, A. B., & Khaiyer, M. M. (2010). Evaluation of the NASA GISS Single-column model simulated clouds using combined surface and satellite observations. Journal of Climate, 23(19), 5175-5192.
• Logan, T., Xi, B., Dong, X., Obrecht, R., Li, Z., & Cribb, M. (2010). A study of Asian dust plumes using satellite, surface, and aircraft measurements during the INTEX-B field experiment. Journal of Geophysical Research Atmospheres, 115(20).
• Xi, B., Dong, X., Minnis, P., & Khaiyer, M. M. (2010). A 10 year climatology of cloud fraction and vertical distribution derived from both surface and GOES observations over the DOE ARM SPG site. Journal of Geophysical Research Atmospheres, 115(12).
• Feng, Z., Dong, X., & Xi, B. (2009). A method to merge WSR-88D data with ARM SGP millimeter cloud radar data by studying deep convective systems. Journal of Atmospheric and Oceanic Technology, 26(5), 958-971.
• Dong, X., Minnis, P., Xi, B., Sun-Mack, S. .., & Chen, Y. (2008). Comparison of CERES-MODIS stratus cloud properties with ground-based measurements at the DOE ARM Southern Great Plains site. Journal of Geophysical Research Atmospheres, 113(3).
• Dong, X., Wielicki, B. A., Xi, B., Hu, Y., Mace, G. G., Benson, S., Rose, F., Kato, S., Charlock, T., & Minnis, P. (2008). Using observations of deep convective systems to constrain Atmospheric column absorption of solar radiation in the optically thick limit. Journal of Geophysical Research Atmospheres, 113(10).
• Dong, X., Xi, B., & Minnis, P. (2006). Observational evidence of changes in water vapor, clouds, and radiation at the ARM SGP site. Geophysical Research Letters, 33(19).
• Mace, G. G., Benson, S., Sonntag, K. L., Kato, S., Min, Q., Minnis, P., Twohy, C. H., Poellot, M., Dong, X., Long, C., Zhang, Q., & Doelling, D. R. (2006). Cloud radiative forcing at the Atmospheric Radiation Measurement Program Climate Research Facility: 1. technique, validation, and comparison to satellite-derived diagnostic quantities. Journal of Geophysical Research Atmospheres, 111(11).
• Dong, X. (2005). The impact of surface albedo on the retrievals of low-level stratus cloud properties: An updated parameterization. Geophysical Research Letters, 32(10), 1-4.
• Dong, X., Minnis, P., & Xi, B. (2005). A climatology of midlatitude continental clouds from the ARM SGP central facility: Part I: Low-level cloud macrophysical, microphysical, and radiative properties. Journal of Climate, 18(9), 1391-1410.
• Garrett, T. J., Zhao, C., Dong, X., Mace, G. G., & Hobbs, P. V. (2004). Effects of varying aerosol regimes on low-level Arctic stratus. Geophysical Research Letters, 31(17), L17105 1-4.
• Penner, J. E., Dong, X., & Chen, Y. (2004). Observational evidence of a change in radiative forcing due to the indirect aerosol effect. Nature, 427(6971), 231-234.
• Dong, X., & Mace, G. G. (2003). Arctic stratus cloud properties and radiative forcing derived from ground-based data collected at Barrow, Alaska. Journal of Climate, 16(3), 445-461.
• Dong, X., & Mace, G. G. (2003). Profiles of low-level stratus cloud microphysics deduced from ground-based measurements. Journal of Atmospheric and Oceanic Technology, 20(1), 42-53.

Proceedings Publications

• Minnis, P., Geier, E., Wielicki, B. A., Sun-Mack, S. .., Chen, Y., Trepte, Q. Z., Dong, X., Doelling, D. R., Ayers, J. K., & Khaiyer, M. M. (2006). Overview of CERES cloud properties derived from virs and modis data.
• Minnis, P., Sun-Mack, S. .., Trepte, Q. Z., Chen, Y., Brown, R. R., Gibson, S., Heck, P. W., Dong, X., & Xi, B. (2006). A multi-year data set of cloud properties derived for CERES from Aqua, Terra, and TRMM.
• Chen, Y., Dong, X., Heck, P. W., Minnis, P., Sun-mack, S., Trepte, Q. Z., Wielicki, B. A., & Young, D. F. (2003).

  A global cloud database from VIRS and MODIS for CERES

  . In SPIE Proceedings, 4891, 115-126.
  More info

  The NASA CERES Project has developed a combined radiation and cloud property dataset using the CERES scanners and matched spectral data from high-resolution imagers, the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. The diurnal cycle can be well-characterized over most of the globe using the combinations of TRMM, Aqua, and Terra data. The cloud properties are derived from the imagers using state-of-the-art methods and include cloud fraction, height, optical depth, phase, effective particle size, emissivity, and ice or liquid water path. These cloud products are convolved into the matching CERES fields of view to provide simultaneous cloud and radiation data at an unprecedented accuracy. Results are available for at least 3 years of VIRS data and 1 year of Terra MODIS data. The various cloud products are compared with similar quantities from climatological sources and instantaneous active remote sensors. The cloud amounts are very similar to those from surface observer climatologies and are 6-7% less than those from a satellite-based climatology. Optical depths are 2-3 times smaller than those from the satellite climatology, but are within 5% of those from the surface remote sensing. Cloud droplet sizes and liquid water paths are within 10% of the surface results on average for stratus clouds. The VIRS and MODIS retrievals are very consistent with differences that usually can be explained by sampling, calibration, or resolution differences. The results should be extremely valuable for model validation and improvement and for improving our understanding of the relationship between clouds and the radiation budget.