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., & Minnis, P. (2023). Stratus, Stratocumulus, and Remote Sensing.
• 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

• 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.
• Tian, J., Ward, D. M., Xi, B., Dong, X., & Wu, P. (2020). Profiles of MBL Cloud and Drizzle Microphysical Properties Retrieved From Ground-Based Observations and Validated by Aircraft In Situ Measurements Over the Azores. JGR Atmospheres, 125(8). doi:https://doi.org/10.1029/2019JD032205
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  Abstract: The profiles of marine boundary layer (MBL) cloud and drizzle microphysical properties are important for studying the cloud-to-rain conversion and growth processes in MBL clouds. However, it is challenging to simultaneously retrieve both cloud and drizzle microphysical properties within an MBL cloud layer using ground-based observations. In this study, methods were developed to first decompose drizzle and cloud reflectivity in MBL clouds from Atmospheric Radiation Measurement cloud radar reflectivity measurements and then simultaneously retrieve cloud and drizzle microphysical properties during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign. These retrieved microphysical properties, such as cloud and drizzle particle size (rc and rm,d), their number concentration (Nc and Nd) and liquid water content (LWCc and LWCd), have been validated by aircraft in situ measurements during ACE-ENA (~158 hr of aircraft data). The mean surface retrieved (in situ measured) rc, Nc, and LWCc are 10.9 μm (11.8 μm), 70 cm−3 (60 cm−3), and 0.21 g m−3 (0.22 g m−3), respectively. For drizzle microphysical properties, the retrieved (in situ measured) rd, Nd, and LWCd are 44.9 μm (45.1 μm), 0.07 cm−3 (0.08 cm−3), and 0.052 g m−3 (0.066 g m−3), respectively. Treating the aircraft in situ measurements as truth, the estimated median retrieval errors are ~15% for rc, ~35% for Nc, ~30% for LWCc and rd, and ~50% for Nd and LWCd. The findings from this study will provide insightful information for improving our understanding of warm rain processes, as well as for improving model simulations. More studies are required over other climatic regions.
• 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., Dong, X., Ward, D. M., Xi, B., Wu, P., & Wang, Y. (2022). Aerosol-Cloud-Precipitation Interactions in a typical Overcast Close-cellular and Non-homogenous MBL Stratocumulus Cloud. Advances in Atmospheric Sciences., 39(12), 1-17.
• 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).

  A climatology of mid-latitude maritime cloud fraction and radiative effect derived from the ARM ENA ground-based observations

  . Journal of Climate, 36(2), 1-31. doi:10.1175/jcli-d-22-0290.1
• 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.
• 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.
• McHardy, T. M., Campbell, J. R., Peterson, D. A., Lolli, S., Garnier, A., Kuciauskas, A. P., Surratt, M. L., Marquis, J. W., Miller, S. D., Dolinar, E. K., & Dong, X. (2022). GOES ABI Detection of Thin Cirrus over Land. Journal of Atmospheric and Oceanic Technology, 39(9), 1415-1429.
• 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.
• Cui, W., Dong, X., Xi, B., & Feng, Z. (2021). Climatology of Linear Mesoscale Convective System Morphology in the United States Based on the Random-Forests Method. Journal of Climate, 34(17), 7257-7276.
• Dong, X. (2021). Brendecke, J., X. Dong, B. Xi, and P. Wu, 2021a: Cloud and Drizzle Microphysical Properties Retrieved from Ship-Based Observations during MAGIC.. Earth and Space Science. DOI: 10.1029/2020EA001588.
• Dong, X. (2021). Clark, R. T., X. Q. Dong, C.-H. Ho, J. H. Sun, H. L. Yuan, and T. Takemi, 2021: Preface to the Special Issue on Summer 2020: Record Rainfall in Asia — Mechanisms, Predictability and Impacts.. Adv. Atmos. Sci., 38(12), 1977−1979, https://doi.org/10.1007/s00376-021-1010-5..
• Dong, X. (2021). Cui, C., X. Dong, B. Wang, B. Xi, Y. Deng, and Y. Ding, 2021: Integrative Monsoon Frontal Rainfall Experiment (IMFRE-1): A Mid-Term Review. Adv. In Atmos. Sci. 38 (3), 357-374. doi:10.1007/s00376-020-0209-1.. Adv. In Atmos. Sci. 38 (3), 357-374. doi:10.1007/s00376-020-0209-1..
• Dong, X. (2021). Cui, C., X. Dong, B. Wang, and H. Yang: 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. 38 (3), 357-374. doi: 10.1007/s00376-020-0209-1..
• Dong, X. (2021). Cui, W., X. Dong, B. Xi and Z Feng, 2021: Climatology of Linear Mesoscale Convective System Morphology in the United States based on Random Forests Method.. J. Clim. DOI: 10.1175/JCLI-D- 20-0862.1.
• Dong, X. (2021). 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, e2020GL091836. https://doi.org/10.1029/2020GL091836..
• Dong, X. (2021). Hu. Y., Y. Deng, Y. Lin, Z. Zhou, C. Cui and X. Dong, 2021: Dynamics of the Spatiotemporal Morphology of Mei-yu Fronts: An Initial Survey. Climate Dynamics, DOI: 10.1007/s00382-020-05619-2.
• Dong, X. (2021). Huang*, Y., X. Dong, J. Kay, B. Xi, and E.A. McIlhattan, 2021: The Climate response to increased Cloud Liquid Water in CESM1: A sensitivity study of Wegener-Bergeron-Findeisen Process.. Climate Dynamics, DOI: 10.1007/s00382-021-05648-5. CLDY-D-20-00372R3..
• Dong, X. (2021). Huang, Y., Q. Ding, X. Dong, and B. Xi, 2021; The summertime low clouds: bridging large-scale circulation and sea ice variations over the Arctic.. Communications Earth & Environment. https://doi.org/10.1038/s43247-021-00114-w。.
• Dong, X. (2021). Luo, R. Q. Ding, Z. Wu, I. Baxer, M. Bushuk, E. Blanchard-Wrigglesworth, Y. Huang, and X. Dong, 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). Marcovecchio, A., A. Behrangi, X. Dong, B. Xi and Y. Huang, 2021: Precipitation Influence on and Response to Early and Late Arctic Sea Ice Melt Onset During Melt Season. International Journal of Climate, DOI: 10.1002/joc.7233..
• Dong, X. (2021). McHardy, T.M., J.R. Campbell, …X. Dong, 2021; Detecting Transparent Cirrus Clouds Over Ocean Using the GOES-16 ABI 1.378 µm Channel. 38, 1093-1109.. J. Atmos. and Oceanic Tech. DOI: 10.1175/JTECH-D-20-0130.1.
• Dong, X. (2021). Zhang, H., X. Dong, Z. Xin, B. Xi, Q. Liu, H. He, X. Xie, L. Li, and S. Yu, 2021: Retrieving high resolution photosynthetically active radiation from GOES-R ABI data.. Remote Sensing of Environment. 260 (2021), 112436..
• Dong, X. (2021). Zheng, X., B. Xi, X. Dong, P. Wu, T. Logan and Y. Wang, 2021: Environmental Effects on Aerosol-Cloud Interaction in non-precipitating MBL clouds over the Eastern North Atlantic. ACP. https://doi.org/10.5194/acp-2021-391.
• Dong, X., & Xi, B. (2021). Huang*, Y., X. Dong, J. Kay, B. Xi, and E.A. McIlhattan, 2021: 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
• 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.
• Hu, Y., Deng, Y., Lin, Y., Zhou, Z., Cui, C., & Dong, X. (2021). Dynamics of the spatiotemporal morphology of Mei-yu fronts: an initial survey. Climate Dynamics, 56(9-10), 2715-2728.
• 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).
• Huang, Y., Dong, X., Kay, J. E., Xi, B., & McIlhattan, E. A. (2021). The climate response to increased cloud liquid water over the Arctic in CESM1: a sensitivity study of Wegener???Bergeron???Findeisen process. Climate Dynamics, 56(9-10), 3373-3394.
• Luo, R., Ding, Q., Wu, Z., Baxter, I., Bushuk, M., Huang, Y., & Dong, X. (2021). Summertime atmosphere???sea ice coupling in the Arctic simulated by CMIP5/6 models: Importance of large-scale circulation. Climate Dynamics, 56(5-6), 1467-1485.
• 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, C., Cui, C., Dong, X., Dong, X., Wang, B., Wang, B., Xi, B., Xi, B., Deng, Y., Deng, Y., Ding, Y., & Ding, Y. (2020). Integrative Monsoon Frontal Rainfall Experiment (IMFRE-1): A Mid-Term Review. Adv. in Atmospheric Science. doi:10.1007/s00376-020-0209-1
• 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). Cui, C., X. Dong, B. Wang, and H. Yang, 2020: 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. doi:doi: 10.1007/s00376-020-0262-9
• Dong, X. (2020). Facilitating International Collaboration on Climate Change Research. BAMS.
• Dong, X. (2020). 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. JGR.
• Dong, X. (2020). Li, Z., Y. Wang, J. Guo, M. Cribb, X. Dong et al., 2020: East Asian Study of Tropospheric Aerosols and Impact on Regional Cloud, Precipitation, and Climate (EAST-AIRCPC). JGR, 124, 2019JD030758.. JGR. doi:https://doi.org/10.1029/2019JD030758
• Dong, X. (2020). Sun, Y., X. Dong, W. Cui, Z. Zhou, Z. Fu, L. Zhou, Y. Deng, C. Cui, 2020: Vertical Structures of Typical Meiyu Precipitation Characteristics Retrieved from GPM-DPR. JGR. doi:https://doi.org/10.1029/2019JD031466
• Dong, X. (2020). Yang, J., Li, J., Li, P., Sun, G., Cai, Z., Dong, X., et al. (2020). Spatial distribution and impacts of aerosols on clouds under Meiyu frontal weather background over central China based on aircraft observations.. JGR.
• Dong, X. (2020). Zhang, Z., Q. Song, D. Mechem, V. Larson, J. Wang, Y. Liu, M. Witte, X. Dong, and P. Wu, 2020; Vertical Dependence of Horizontal Variation of Cloud Microphysics: Observations from the ACE- ENA field campaign and implications for warm rain simulation in climate models.. ACP.
• Dong, X., & Xi, B. (2020). Fu, Z., X. Dong, L. Zhou, J. Wang, W. Cui, R. Wan, L. Leng and B. Xi, 2020: Statistical Characteristics of the Raindrop Size Distributions in Central China During Meiyu Season. JGR. doi:https://doi.org/ 10.1029/2019JD031954
• Dong, X., & Xi, B. (2020). Huang*, Y., X. Dong, J. Kay, B. Xi, and E.A. McIlhattan, 2021: 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.
• Dong, X., & Xi, B. (2020). Integrative Monsoon Frontal Rainfall Experiment (IMFRE-1): A Mid-Term Review.. Adv. In Atmos. Sci. doi:doi: 10.1007/s00376-020-0209-1.
• Dong, X., & Xi, B. (2020). Logan, T., X. Dong, B. Xi, X. Zheng, Y. Wang, P. Wu. E. Marlow and J. Maddux, 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.
• 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.
• Dong, X., & Xi, B. (2020). Statistical Characteristics of the Raindrop Size Distributions in Central China During Meiyu Season. JGR. doi:https://doi.org/ 10.1029/2019JD031954
• Dong, X., Logan, T., Wang, Y., Wu, P., Xi, B., Yung, Y. L., & Zheng, X. (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. doi:10.5194/acp-20-14741-2020
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  Abstract. Vertical profiles of aerosols are inadequately observed and poorly represented in climate models, contributing to the current large uncertainty associated with aerosol–cloud interactions. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) aircraft field campaign near the Azores islands provided ample observations of vertical distributions of aerosol and cloud properties. Here we utilize the in situ aircraft measurements from the ACE-ENA and ground-based remote-sensing data along with an aerosol-aware Weather Research and Forecast (WRF) model to characterize the aerosols due to long-range transport over a remote region and to assess their possible influence on marine-boundary-layer (MBL) clouds. The vertical profiles of aerosol and cloud properties measured via aircraft during the ACE-ENA campaign provide detailed information revealing the physical contact between transported aerosols and MBL clouds. The European Centre for Medium-Range Weather Forecasts Copernicus Atmosphere Monitoring Service (ECMWF-CAMS) aerosol reanalysis data can reproduce the key features of aerosol vertical profiles in the remote region. The cloud-resolving WRF sensitivity experiments with distinctive aerosol profiles suggest that the transported aerosols and MBL cloud interactions (ACIs) require not only aerosol plumes to get close to the marine-boundary-layer top but also large cloud top height variations. Based on those criteria, the observations show that the occurrence of ACIs involving the transport of aerosol over the eastern North Atlantic (ENA) is about 62 % in summer. For the case with noticeable long-range-transport aerosol effects on MBL clouds, the susceptibilities of droplet effective radius and liquid water content are − 0.11 and + 0.14, respectively. When varying by a similar magnitude, aerosols originating from the boundary layer exert larger microphysical influence on MBL clouds than those entrained from the free troposphere.
• 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).
• Logan, T., Dong, X., Xi, B., Zheng, X., Wang, Y., Wu, P., Marlow, E., & Maddux, J. (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, 7(12).
• 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).
• Tian, J., Dong, X., Xi, B., & Feng, Z. (2020). Characteristics of ice cloud???precipitation of warm season mesoscale convective systems over the great plains. Journal of Hydrometeorology, 21(2), 317-334.
• 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., 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 ARM SGP ground-based measurements.. ACP. doi:https://doi.org/10.5194/acp-2019-478.
• 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. (2019). Thicker clouds and accelerated Arctic Sea ice decline: The atmosphere‐sea ice interactions in spring. GRL, 46. doi:https://doi.org/10.1029/2019GL082791
• Dong, X. (2019). Vertical Structures of Typical Meiyu Precipitation Characteristics Retrieved from GPM-DPR.. JGR, 125. doi:https://doi.org/10.1029/2019JD031466.
• 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.
• Han, B., Fan, J., Varble, A., Morrison, H., Williams, C. R., Chen, B., Dong, X., Giangrande, S. E., Khain, A., Mansell, E., Milbrandt, J. A., Shpund, J., & Thompson, G. (2019). Cloud-Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 124(2), 1090-1117.
• 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.
• Li, Z., Wang, Y., Guo, J., Zhao, C., Cribb, M. C., Dong, X., Fan, J., Gong, D., Huang, J., Jiang, M., Jiang, Y., Lee, S., Li, H., Li, J., Liu, J., Qian, Y., Rosenfeld, D., Shan, S., Sun, Y., , Wang, H., et al. (2019). East Asian Study of Tropospheric Aerosols and their Impact on Regional Clouds, Precipitation, and Climate (EAST-AIRCPC). Journal of Geophysical Research: Atmospheres, 124(23), 13026-13054.
• 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.
• Tian, J., Dong, X., Xi, B., Williams, C. R., & Wu, P. (2019). Estimation of liquid water path below the melting layer in stratiform precipitation systems using radar measurements during MC3E. Atmospheric Measurement Techniques, 12(7), 3743-3759.
• Wang, J., Dong, X., Kennedy, A., Hagenhoff, B., & Xi, B. (2019). A regime-based evaluation of southern and northern great plains warm-season precipitation events in wrf. Weather and Forecasting, 34(4), 805-831.
• 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.
• 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.
• Clark, A. J., Jirak, I. L., Dembek, S. R., Creager, G. J., Kong, F., Thomas, K. W., Knopfmeier, K. H., Gallo, B. T., Melick, 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). Evaluation of autoconversion and accretion enhancement factors in GCM warm-rain parameterizations using ground-based measurements over the Azores.. ACP, 18, 1–16. doi:https://doi.org/10.5194/acp-18-1-2018
• 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.
• Dong, X. (2018). Preface to the special issue: Aerosols, clouds, radiation, precipitation, and their interactions. Advances in Atmospheric Sciences, 35(2), 133-134.
• Dong, X. (2018). Quantify contribution of aerosol errors to cloud fraction biases in CMIP5 Atmospheric Model Intercomparison Project simulations. International Journal of Climatology, 1-17. doi:DOI:10.1002/joc.5490
• Dong, X. (2018). Sensitivity of numerical simulations of a mesoscale convective system to ice hydrometeors in bulk microphysical parameterization. Pure and Applied Geophysics.. doi:DOI 10.1007/s00024-018-1787-z
• Dong, X. (2018). The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. BAMS, 1433-1448. doi:https://doi.org/10.1175/BAMS-D-16-0309.1.
• Dong, X. (2018). Using AIRS and ARM SGP clear-sky observations to evaluate meteorological reanalyses: a hyperspectral radiance closure approach. JGR, 123. doi:https://doi.org/10.1029/2018JD028850
• 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.
• 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 Utqia??vik, Alaska. Journal of Geophysical Research: Atmospheres, 123(17), 9589-9603.
• 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, J., Dong, X., & Xi, B. (2018). Investigation of Liquid Cloud Microphysical Properties of Deep Convective Systems: 2. Parameterization of Raindrop Size Distribution and its Application for Convective Rain Estimation. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 123(20), 11637-11651.
• Wang, J., Dong, X., & Xi, B. (2018). Investigation of Liquid Cloud Microphysical Properties of Deep Convective Systems: 2. Parameterization of Raindrop Size Distribution and its Application for Convective Rain Estimation. Journal of Geophysical Research: Atmospheres, 123(20), 11,637-11,651.
• 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).
• 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), 17405-17420.
• 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.
• 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). Evaluation of NASA GISS Post-CMIP5 Single Column Model Simulated Cloud and Precipitation Using the ARM SGP Observations.. Adv. Atmos Sci, 34, 306-320. doi:10.1007/s00376-016-5254-4
• Dong, X. (2017). Intercomparisons of marine boundary layer cloud properties from the ARM CAP-MBL campaign and two MODIS cloud products. JGR-Atmosphere, 122, 1-15. doi:10.1002/2016JD025763
• 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). The footprints of 16-year trends of Arctic springtime cloud and radiation properties on September sea-ice retreat. JGR-Atmosphere, 122, 1-15. doi:10.1002/2016JD026020
• 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.
• Fridlind, A. M., Li, X., Wu, D., Van Lier-Walqui, 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., Qiu, S., Xi, B., Dolinar, E. K., & Stanfield, R. E. (2017). Quantifying the uncertainties of reanalyzed Arctic cloud and radiation properties using satellite surface observations. Journal of Climate, 30(19), 8007-8029.
• 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. (2017). The footprints of 16year trends of Arctic springtime cloud and radiation properties on September sea ice retreat. Journal of Geophysical Research, 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.
• Li, J., Wang, W., Dong, X., & Mao, J. (2017). Cloud-radiation-precipitation associations over the Asian monsoon region: an observational analysis. Climate Dynamics, 49(9-10), 3237-3255.
• 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, 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.
• 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, 122(4), 2351-2365.
• Carletta, N. D., Mullendore, G. L., Starzec, M., Xi, B., Feng, Z., & Dong, X. (2016). Determining the best method for estimating the observed level of maximum detrainment based on radar reflectivity. Monthly Weather Review, 144(8), 2915-2926.
• 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. (2016). Evaluation and intercomparison of clouds, precipitation, and radiation budgets in recent reanalyses using satellite-surface observations. CLIMATE DYNAMICS, 46(7-8), 2123-2144.
• Dolinar, E. K., Dong, X., & Xi, B. (2016). Evaluation and intercomparison of clouds, precipitation, and radiation budgets in recent reanalyses using satellite-surface observations. Climate Dynamics, 46(7-8), 2123-2144.
• 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). Evaluation and intercomparison of clouds, precipitation and radiation budgets in recent reanalyses using satellite-surface data. Climate Dynamic.
• 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. (2016). Retrievals of ice cloud microphysical properties of deep convective systems using radar measurements. JGR-Atmosphere, 121, 10820-10839. doi:10.1002/2015JD024686
• 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.
• Stenz, R., Dong, X., Xi, B., Feng, Z., & Kuligowski, R. J. (2016). Improving satellite quantitative precipitation estimation using GOES-retrieved cloud optical depth. Journal of Hydrometeorology, 17(2), 557-570.
• 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.
• Wang, J., Dong, X., Xi, B., & Heymsfield, A. J. (2016). Investigation of liquid cloud microphysical properties of deep convective systems: 1. parameterization raindrop size distribution and its application for stratiform rain estimation. Journal of Geophysical Research, 121(18), 10,739-10,760.
• 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.
• Deng, Y., Dong, X., Evans, K. J., & Jiang, T. (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. doi:10.1002/2013jd021209
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  In this study, an atmospheric river (AR) detection algorithm is developed to investigate the downstream modulation of the eastern North Pacific ARs by another weather extreme, known as the East Asian cold surge (EACS), in both reanalysis data and high-resolution global model simulations. It is shown that following the peak of an EACS, atmospheric disturbances of intermediate frequency (IF; 10–30 day period) are excited downstream. This leads to the formation of a persistent cyclonic circulation anomaly over the eastern North Pacific that dramatically enhances the AR occurrence probability and the surface precipitation over the western U.S. between 30°N and 50°N. A diagnosis of the local geopotential height tendency further confirms the essential role of IF disturbances in establishing the observed persistent anomaly. This downstream modulation effect is then examined in the two simulations of the National Center for Atmospheric Research Community Climate System Model version 4 with different horizontal resolutions (T85 and T341) for the same period (1979–2005). The connection between EACS and AR is much better captured by the T341 version of the model, mainly due to a better representation of the scale interaction and the characteristics of IF atmospheric disturbances in the higher-resolution model. The findings here suggest that faithful representations of scale interaction in a global model are critical for modeling and predicting the occurrences of hydrological extremes in the western U.S. and for understanding their potential future changes.
• 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
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  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
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  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). A climatology of midlatitude continental clouds from the ARM SGP Central Facility. Part II: Cloud fraction and surface radiative forcing. Journal of Climate, 19(9), 1765-1783.
• 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.