School of Marine and Atmospheric Sciences

Berry Wen

Assistant Professor, Atmospheric Sciences DivisionBerry Wen

 

Education: Ph.D. 2015 - University of Oklahoma, Norman, OK

Research Topics

Radar and satellite remote sensing, retrieval, validation and application in Meteorology and Hydrology, AI/ML, Long-term climate data analysis, Extreme events and natural hazards, Community engagement

yixin.wen@stonybrook.edu

 

Biography and Research Focus

Berry received her PhD degree from the School of Meteorology, University of Oklahoma.  She further honed her skills during postdoctoral research at the NASA/ Jet Propulsion Laboratory (JPL) before returning to Oklahoma as a research scientist. In this role, she was affiliated with Radar Division in National Severe Storms Laboratory. Berry is the Chair of User Working Group with NASA/Goddard Earth Sciences Data and Information Services Center (GES DISC) as an expert in AI and data fusion. Additionally, she serves as an editor of the AGU’s new journal, JGR: Machine Learning and Computation.

Berry's research areas of focus include radar and satellite remote sensing, machine learning, and interpretable AI. Additionally, she engages in long-term climate data analysis, studying extreme events and water-related natural hazards. Beyond her scientific pursuits, Dr. Wen is committed to working with Indigenous Peoples and Native Nations.

 

Publications

  1. Sun, , Wen, Y. *, and Yang, H*. (2026). ReSearch: A Multi-Stage Machine Learning Framework for Earth Science Data Discovery, https://haizhaoyang.github.io/publications/Geo-ReSearch.pdf
  2. Yan, , Chen, M., Li, Z., Wen, Y., et al. (2026). AI Agent for Hydrologic Modeling: Definition, Development, and Application. Geophysical Research Letters. https://doi.org/10.13140/RG.2.2.34824.48643
  3. Yan, , Chen, M., Zhu, S., Wen, Y., et al. (2026). AQUAH: Automatic Quantification and Unified Agent in Hydrology. https://arxiv.org/abs/2508.02936.
  4. Yi, , Yu, M., Qian, W., Wen, Y.*, & Yang, H.* (2026). Efficient kilometer-scale precipitation downscaling with conditional wavelet diffusion. Journal of Geophysical Research: Machine Learning and Computation, 3, e2025JH000941. https://doi.org/10.1029/2025JH000941
  5. Kisembe, , Wen, Y.*, Wainwright, C., Odongo, R., Qian, W., (2026), Contrasting Changes in Rainy Season Length,Rainfall Frequency,and Intensity across Eastern Africa, accepted by Journal of Hydrometeorology, doi: 10.1175/JHM-D-25-0080.1
  6. Zeraati, , Farahmand, A., Seager, R., Fowler, H. J., Madani, N., Parazoo, N., Manning, C., White, C. J., Wen, Y., Mehran, A., AghaKouchak, A., (2026), Assessing Flash Drought Development and Propagation Across the Contiguous United States Using Remote Sensing, Earth's Future, 14(3), doi: 10.1029/2025EF007037
  7. Zhu, , Tang, G., Sorooshian, S., Huffman, G. J., Duan, Q., Yan, S., Behrangi, A., Papalexiou, S. M., Nguyen, P., Hsu, K., Wen, Y., Liu, Z., Li, Z., Chen, M., Laviola, S., Hong, Y., (2026), A Review of the Past Half Century of Geostationary Satellite Thermal Observations for Global Precipitation Estimation: Developments, Achievements, and Future Prospects, IEEE Geoscience and Remote Sensing Magazine, doi: 10.1109/MGRS.2026.3665828
  8. Song, , Yong, B., Zhang, H., Zhang, F., Ahmed, Z., Chan, N. W., Wang, G., Wen, Y., (2026), Advancing Spaceborne Precipitation Radar Monitoring: Systematic Comparison Between PMR and DPR Observations, IEEE Transactions on Geoscience and Remote Sensing, 64, doi: 10.1109/TGRS.2025.3650492
  9. Wang, , Yong, B., Qi, W., Wen, Y., (2026), Recent oceanic performance of GPM multisatellite precipitation estimates benchmarked by passive aquatic listeners, Journal of Hydrology, 664, 134475, doi: 10.1016/j.jhydrol.2025.
  10. Liu, and Wen, Y. (2025). Accelerating Earth Science to Action. Bull. Amer. Meteor. Soc., 106, E2043– E2051, https://doi.org/10.1175/BAMS-D-24-0226.1.
  11. Camporeale, , Marino, R., Rundle, J. B., Folini, D., Chen, Y., Lucas, D. D., Li, X., Berger, T. E., Fox,
  12. C., Wen, Y., Shen, C., Wentzcovitch, R. M., & Fox, G. C. (2025). The First 18 Months of JGR: MLC. Journal of Geophysical Research: Machine Learning and Computation, 2(2). https://doi.org/10.1029/2
  13. Liu, , Li, S., Fan, D., Wen, Y., Madson, A., & Mitchell, J. (2025). A deep-learning workflow for CORONA-based historical land use classifications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 16066–16080. https://doi.org/10.1109/JSTARS.2025.3582789
  14. Qian, , Wen, Y.*, Gao, S., Li, Z., Kisembe, J., & Jing, H. (2025). Evaluation of near-surface specific humidity and air temperature from Atmospheric Infrared Sounder (AIRS) over oceans. Earth and Space Science. Advance online publication. https://doi.org/10.1029/2024EA003856
  15. Rahaman M, Southworth J, Wen , Keellings D. Assessing Model Trade-Offs in Agricultural Remote Sensing: A Review of Machine Learning and Deep Learning Approaches Using Almond Crop Mapping. Remote Sensing. 2025; 17(15):2670. https://doi.org/10.3390/rs17152670
  16. Mei, , Yong, B., Lyu, Y., Qi, W., Wen, Y., Wang, G., & Zhang, J. (2025). Runoff evolution responses to climate change: A case study in the headwater area of Yellow River, China. Journal of Environmental Management, 384, 125512. https://doi.org/10.1016/j.jenvman.2025.125512
  17. Cham, , Zhang, O., Jing, H., Qian, W., Wen, Y., & Wang, J. (2025). NS-QPE: A Neuro-Symbolic Approach Towards Accurate and Interpretable Quantitative Precipitation Estimation Using Polarimetric Radar Data. International Conference on Advanced Machine Learning and Data Science (AMLDS), 784-792
  18. Zhu, , Li, Z., Chen, M., Wen, Y. , Liu, Z., Huffman, G. J., Tsoodle, T. E., Ferraro, S. C., Wang, Y., & Hong, Y. (2025). Evaluation of IMERG climate trends over land in the TRMM and GPM eras. Environmental Research Letters, 20(1), 014064. https://doi.org/10.1088/1748-9326/AD984E
  19. Song, , Qi, W., Lyu, Y., Zhang, H., Song, Y., Shi, T., Wen, Y. , & Yong, B. (2024). Detecting the Vertical Structure of Extreme Precipitation in the Headwater Area of Yellow River Using the Dual‐Frequency Precipitation Radar Onboard the Global Precipitation Measurement Mission. International Journal of Climatology, 44(16), 5918-5933. https://doi.org/10.1002/JOC.8675
  20. Zhang, , Grissom, B., Pulido, J., Munoz-Ordaz, K., He, J., Cham, M., Jing, H., Qian, W., Wen, Y.* , & Wang, J. (2024). Accurate and Interpretable Radar Quantitative Precipitation Estimation with Symbolic Regression. IEEE International Conference on Big Data (BigData), 2254-2263. https://doi.org/10.1109/BIGDATA62323.2024.10825069
  21. Zhu, , Li, Z., Chen, M., Wen, Y., Gao, S., Zhang, J., Wang, J., Nan, Y., Ferraro, S. C., Tsoodle, T. E., & Hong, Y. (2024). How has the latest IMERG V07 improved the precipitation estimates and hydrologic utility over CONUS against IMERG V06? Journal of Hydrology, 645, 132257. https://doi.org/10.1016/J.JHYDROL.2024.132257
  22. Li, Z. , Tsoodle, , Chen, M., Gao, S., Zhang, J., Wen, Y., Yang, T., King, F. N., & Hong, Y. (2024). Future Heavy Rainfall and Flood Risks for Native Americans under Climate and Demographic Changes: A Case Study in Oklahoma. Weather, Climate, and Society, 16(1), 143-154. https://doi.org/10.1175/WCAS-D-23-0005.1
  23. Jing, , Li, Z., Wen, Y. *, Gao, S., Wang, Y., Qian, W., & Kisembe, J. (2024). Changes in Convective Precipitation Reflectivity over the CONUS Revealed by High-Resolution Radar Observations from 2015 to 2021. Atmosphere, 15(6), 627. https://doi.org/10.3390/ATMOS15060627
  24. Kisembe, , Li, J.-L. F., Wen, Y. *, Lee, W.-L., Qian, W., Li, Z., & Jiang, J. H. (2024). Assessing the Impacts of Falling Ice Radiative Effects on the Seasonal Variation of Land Surface Properties. Journal of Geophysical Research: Atmospheres, 129(15). https://doi.org/10.1029/2024JD040991
  25. Liu, , Wen, Y., Mantas, V. & Meyer, D. (2023). We Need a Better Way to Share Earth Observations. Eos Transactions American Geophysical Union [00963941], 104. https://doi.org/10.1029/2023EO230190
  26. Li, Z. , Wen, *, Liao, L., Wolff, D., Meneghini, R., & Schuur, T. J. (2023). Joint Collaboration on Comparing NOAA’s Ground-Based Weather Radar and NASA–JAXA’s Spaceborne Radar. Bulletin of the American Meteorological Society, 104(8), E1435-E1451. https://doi.org/10.1175/BAMS-D-22- 0127.1
  27. Gao, , Wen, Y. *, Fishbein, E., Lambrigtsen, B., Zhang, J., Van Dang, H., & Galli, C. (2023). Ground‐ Validation and Error Attribution of Near‐Surface Air Temperature From AIRS in North America. Earth and Space Science, 10(6). https://doi.org/10.1029/2022EA002658
  28. Weber, M., Hondl, , Yussouf, N., Jung, Y., Stratman, D. R., Putnam, B., Wang, X., Schuur, T. J.,  Kuster, C. M., Wen, Y., Sun, J., Keeler, J., Ying, Z., Cho, J., Kurdzo, J., Torres, S. M., Curtis, C. D., Schvartzman Cohenca, D., Boettcher, J., ...Mirkovic, D. (2023). Future U.S. Operational Weather  Radar: Opportunities and Challenges for Its Next Generation. Bulletin of the American Meteorological Society, 104(2), 99-102. https://doi.org/10.1175/BAMS-D-20-0067.A
  29. Li, Z. , Xue, , Clark, R., Vergara Arrieta, H., Gourley, J., Tang, G., Shen, X., Kan, G., Zhang, K., Wang, J., Chen, M., Gao, S., Zhang, J., Yang, T., Wen, Y., Kirstetter, P.  E., & Hong, Y.  (2023). A decadal review of the CREST model family: Developments, applications, and outlook. Journal of Hydrology X, 20, 100159. https://doi.org/10.1016/J.HYDROA.2023.100159
  30. Li, Z. , Gao, , Chen, M., Zhang, J., Gourley, J. J., Wen, Y., Yang, T., & Hong, Y. (2023). Introducing Flashiness‐Intensity‐Duration‐Frequency (F‐IDF): A New Metric to Quantify Flash Flood Intensity. Geophysical Research Letters, 50(23). https://doi.org/10.1029/2023GL104992
  31. Chen, , Huang, Y., Li, Z., Larico, A. J., Xue, M., Hong, Y., Hu, X., Novoa, H. M., Martin, E. R., Mc Pherson, R. A., Zhang, J., Gao, S., Wen, Y., Perez, A. V., & Morales, I. Y. (2022). Cross-Examining Precipitation Products by Rain Gauge, Remote Sensing, and WRF Simulations over a South American Region across the Pacific Coast and Andes. Atmosphere, 13(10), 1666. https://doi.org/10.3390/ATMOS13101666
  32. Kalmus, , Nguyen, H., Roman, J., Wang, T., Yue, Q., Wen, Y., Hobbs, J., & Braverman, A. (2022). Data Fusion of AIRS and CrIMSS Near Surface Air Temperature. Earth and Space Science, 9(10). https://doi.org/10.1029/2022EA002282
  33. Li, Z. +, Chen, , Gao, S., Wen, Y., Gourley, J. J., Yang, T., Kolar, R. L., & Hong, Y. (2022). Can re- infiltration process be ignored for flood inundation mapping and prediction during extreme storms? A case study in Texas Gulf Coast region. Environmental Modelling and Software, 155, 105450. https://doi.org/10.1016/J.ENVSOFT.2022.105450
  34. Li, Z., Tang, G., Kirstetter, E., Gao, S., Li, J.-L., Wen, Y. *, & Hong, Y. * (2022). Evaluation of GPM IMERG and its Constellations in Extreme Events over the Conterminous United States. Journal of Hydrology, 606, 127357. https://doi.org/10.1016/J.JHYDROL.2021.127357
  35. Li, Z. , Chen, , Gao, S., Luo, X., Gourley, J. J., Kirstetter, P. E., Yang, T., Kolar, R. L., Mcgovern, A., Wen, Y., Rao, B., Yami, T., & Hong, Y. (2021). CREST-iMAP v1.0: A fully coupled hydrologic- hydraulic modeling framework dedicated to flood inundation mapping and prediction. Environmental Modelling and Software, 141, 105051. https://doi.org/10.1016/J.ENVSOFT.2021.105051
  36. Weber, M., Hondl, , Yussouf, N., Jung, Y., Stratman, D. R., Putnam, B., Wang, X., Schuur, T. J., Cooley, K., Kuster, C. M., Istok, M., Wen, Y., Zhang, G., Palmer, R. D., Sun, J., Keeler, J., Ying, Z., Cho, J., Kurdzo, J., ...Mirkovic, D. (2021). Towards the Next Generation Operational Meteorological Radar. Bulletin of the American Meteorological Society, 102(7), E1357-E1383. https://doi.org/10.1175/BAMS-D-20-0067.1
  37. Wen, *, Schuur, T. J., Vergara Arrieta, H., & Kuster, C. M. (2021). Effect of Precipitation Sampling Error on Flash Flood Monitoring and Prediction: Anticipating Operational Rapid-Update Polarimetric Weather Radars. Journal of Hydrometeorology. https://doi.org/10.1175/JHM-D-19-0286.1
  38. Li, Z. , Tang, G., Hong, , Chen, M., Gao, S., Kirstetter, P. E., Gourley, J. J., Wen, Y., Yami, T., Nabih, S., & Hong, Y. (2021). Two-decades of GPM IMERG early and final run products intercomparison: Similarity and difference in climatology, rates, and extremes. Journal of Hydrology, 594, 125975. https://doi.org/10.1016/J.JHYDROL.2021.125975
  39. Li, Z. , Wen, *, Schreier, M., Behrangi, A., Hong, Y., & Lambrigtsen, B. (2021). Advancing Satellite Precipitation Retrievals with Data Driven Approaches: Is black box model explainable? Earth and Space Science, 8(2). https://doi.org/10.1029/2020EA001423
  40. Coffer, B., Kubacki, , Wen, Y., Zhang, T., Barajas, C. A., & Gobbert, M. K. (2021). Machine Learning with Feature Importance Analysis for Tornado Prediction from Environmental Sounding Data. PAMM, 20(1). https://doi.org/10.1002/PAMM.202000112
  41. Li, Z. , Chen, , Gao, S., Hong, Z., Tang, G., Wen, Y., Gourley, J. J., & Hong, Y. (2020). Cross- Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation. Remote Sensing, 12(8), 1258. https://doi.org/10.3390/RS12081258
  42. Wen, *, Behrangi, A., Chen, H., & Lambrigtsen, B. (2018). How well were the early 2017 California Atmospheric River precipitation events captured by satellite products and ground‐based radars? Quarterly Journal of the Royal Meteorological Society, 144(S1), 344-359. https://doi.org/10.1002/QJ.3253
  43. Lambrigtsen, , Dang, H. V., Turk, F. J., Hristova-Veleva, S. M., Su, H., & Wen, Y. (2018). All-Weather Tropospheric 3-D Wind From Microwave Sounders. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 11(6), 1949-1956. https://doi.org/10.1109/JSTARS.2018.2814540
  44. Gou, , Ma, Y., Chen, H., & Wen, Y. (2018). Radar-derived quantitative precipitation estimation in complex terrain over the eastern Tibetan Plateau. Atmospheric Research, 203, 286-297. https://doi.org/10.1016/J.ATMOSRES.2017.12.017
  45. Yanovsky, I., Wen, , Behrangi, A., Schreier, M., & Lambrigtsen, B. (2018). Validating Enhanced Resolution of Microwave Sounder Imagery Through Fusion with Infrared Sensors| Data. IEEE Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad) [Conference], 1-5. https://doi.org/10.1109/MICRORAD.2018.8430703
  46. Behrangi, , & Wen, Y. (2017). On the Spatial and Temporal Sampling Errors of Remotely Sensed Precipitation Products. Remote Sensing, 9(11), 1127. https://doi.org/10.3390/RS9111127
  47. Zhong, , Yang, R., Wen, Y., Chen, L., Gou, Y., Li, R., Zhou, Q., & Hong, Y. (2017). Cross-evaluation of reflectivity from the space-borne precipitation radar and multi-type ground-based weather radar network in China. Atmospheric Research, 196, 200-210. https://doi.org/10.1016/J.ATMOSRES.2017.06.016
  48. Yanovsky, I., Behrangi, , Wen, Y., Schreier, M., Dang, V., & Lambrigtsen, B. (2017). Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data. Remote Sensing, 9(11), 1097. https://doi.org/10.3390/RS9111097
  49. Tang, G., Zeng, , Ma, M., Liu, R., Wen, Y., & Hong, Y. (2017). Can Near-Real-Time Satellite Precipitation Products Capture Rainstorms and Guide Flood Warning for the 2016 Summer in South China? IEEE Geoscience and Remote Sensing Letters, 14(8), 1208-1212. https://doi.org/10.1109/LGRS.2017.2702137
  50. Zhong, , Yang, R., Chen, L., Wen, Y., Li, R., Tang, G., & Hong, Y. (2017). Combined Space and Ground Radars for Improving Quantitative Precipitation Estimations in the Eastern Downstream Region of the Tibetan Plateau. Part I: Variability in the Vertical Structure of Precipitation in ChuanYu Analyzed from Long-Term Spaceborne Observations by TRMM PR. Journal of Applied Meteorology and Climatology, 56(8), 2259-2274. https://doi.org/10.1175/JAMC-D-16-0382.1
  51. Yanovsky, I., Behrangi, , Schreier, M., Dang, V., Wen, Y., & Lambrigtsen, B. (2017). Fusion of microwave and infrared data for enhancing its spatial resolution. IGARSS - IEEE International Geoscience and Remote Sensing Symposium [Conference], 2625-2628. https://doi.org/10.1109/IGARSS.2017.8127533
  52. Wen, , Kirstetter, P. E., Gourley, J. J., Hong, Y., Behrangi, A., & Flamig, Z. (2017). Evaluation of MRMS snowfall products over the western United States. Journal of Hydrometeorology, 18(6), 1707- 1713. https://doi.org/10.1175/JHM-D-16-0266.1
  53. Tang, G. , Wen, , Gao, J., Long, D., Ma, Y., Wan, W., & Hong, Y. (2017). Similarities and differences between three coexisting spaceborne radars in global rainfall and snowfall estimation. Water Resources Research, 53(5), 3835-3853. https://doi.org/10.1002/2016WR019961
  54. Wen, , Behrangi, A., Lambrigtsen, B., & Kirstetter, P. E. (2016). Evaluation and Uncertainty Estimation of the Latest Radar and Satellite Snowfall Products Using SNOTEL Measurements over Mountainous Regions in Western United States. Remote Sensing, 8(11), 904. https://doi.org/10.3390/RS8110904
  55. Wen, , Kirstetter, P. E., Hong, Y., Gourley, J. J., Cao, Q., Zhang, J., Flamig, Z., & Xue, X. (2016). Evaluation of a Method to Enhance Real-Time, Ground Radar–Based Rainfall Estimates Using Climatological Profiles of Reflectivity from Space. Journal of Hydrometeorology, 17(3), 761-775. https://doi.org/10.1175/JHM-D-15-0062.1
  56. Wen, , Hong, Y., Kirstetter, P. E., Cao, Q., Gourley, J. J., Zhang, J., Xue, X., & Wen, Y. (2014). Systematical evaluation of VPR- Identification and Enhancement (VPR-IE) approach for different precipitation types. SPIE Asia Pacific Remote Sensing Conference, 9259, 92590C. https://doi.org/10.1117/12.2069334
  57. Cao, , Wen, Y., Hong, Y., Gourley, J. J., & Kirstetter, P. E. (2014). Enhancing Quantitative Precipitation Estimation Over the Continental United States Using a Ground-Space Multi-Sensor Integration Approach. IEEE Geoscience and Remote Sensing Letters, 11(7), 1305-1309. https://doi.org/10.1109/LGRS.2013.2295768
  58. Wen, , Cao, Q., Kirstetter, P. E., Hong, Y., Gourley, J. J., Zhang, J., Zhang, G., & Yong, B. (2013). Incorporating NASA Spaceborne Radar Data into NOAA National Mosaic QPE System for Improved Precipitation Measurement: A Physically Based VPR Identification and Enhancement Method. Journal of Hydrometeorology, 14(4), 1293-1307. https://doi.org/10.1175/JHM-D-12-0106.1
  59. Yong, B., Ren, , Hong, Y., Gourley, J. J., Tian, Y., Huffman, G. J., Chen, X., Wang, W., & Wen, Y. (2013). First evaluation of the climatological calibration algorithm in the real‐time TMPA precipitation estimates over two basins at high and low latitudes. Water Resources Research, 49(5), 2461-2472. https://doi.org/10.1002/WRCR.20246
  60. Cao, , Hong, Y., Qi, Y., Wen, Y., Zhang, J., Gourley, J. J., & Liao, L. (2013). Empirical conversion of the vertical profile of reflectivity from Ku‐band to S‐band frequency. Journal of Geophysical Research: Atmospheres, 118(4), 1814-1825. https://doi.org/10.1002/JGRD.50138
  61. Cao, , Hong, Y., Gourley, J. J., Qi, Y., Zhang, J., Wen, Y., & Kirstetter, P. E. (2013). Statistical and Physical Analysis of the Vertical Structure of Precipitation in the Mountainous West Region of the United States Using 11+ Years of Spaceborne Observations from TRMM Precipitation Radar. Journal of Applied Meteorology and Climatology, 52(2), 408-424. https://doi.org/10.1175/JAMC-D-12-095.1
  62. Wen, , Hong, Y., Zhang, G., Schuur, T. J., Gourley, J. J., Flamig, Z., Morris, K. R., & Cao, Q. (2011). Cross Validation of Spaceborne Radar and Ground Polarimetric Radar Aided by Polarimetric Echo Classification of Hydrometeor Types. Journal of Applied Meteorology and Climatology, 50(7), 1389- 1402. https://doi.org/10.1175/2011JAMC2622.1
  63. Wen, , Hong, Y., Zhang, G., Chen, S., Zhang, J., Gourley, J. J. (2011). Incorporating NASA space- borne precipitation research products into National Mosaic QPE operational system for improved

 

 

precipitation measurements. IEEE Radar Conference (RadarCon), 995-999. https://doi.org/10.1109/radar.2011.5960685