GPM Refereed Publications
Emmanuel, R., M. Deshpande, Anandh T.S., R. Toumi, G. M. Kranthi, and S. T. Ingle,
: Application of stream function in tracking a quasi-closed circulation and its characteristics in developing and non-developing tropical cyclones over the North Indian Ocean. Tropical Cyclone Research and Review, 14(3), 185-202, doi:10.1016/j.tcrr.2025.07.002.
Emberson, R., D. Kirschbaum, and T. Stanley,
: Global connections between El Nino and landslide impacts. Nature Communications, 12, Article 2262, doi:10.1038/s41467-021-22398-4.
Emberson, R., D. Kirschbaum, and T. Stanley,
: Landslide Hazard and Exposure Modelling in Data-Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh. Earth's Future, 9, 2, doi:10.1029/2020EF001666.
Emberson, R., D. Kirschbaum, and T. Stanley,
: New global characterisation of landslide exposure. Nat. Hazards Earth Syst. Sci., 20, 3413–3424, doi:10.5194/nhess-20-3413-2020.
Elsaesser, Gregory S., M. Van Lier‐Walqui, Q. Yang, M. Kelley, A. S. Ackerman, A. M. Fridlind, G. V. Cesana, G. A. Schmidt, J. Wu, A. Behrangi, S. J. Camargo, B. De, K. Inoue, N. M. Leitmann‐Niimi, Nicolas M., and J. D. O. Strong,
: Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE). Journal of Advances in Modeling Earth Systems, 17(4), e2024MS004713, doi:10.1029/2024MS004713.
Elsaesser, G. S., R. Roca, T. Fiolleau, A. D. Del Genio, and J. Wu,
: A Simple Model for Tropical Convective Cloud Shield Area Growth and Decay Rates Informed by Geostationary IR, GPM, and Aqua/AIRS Satellite Data. JGR Atmospheres, 127(10), e2021JD035599, doi:10.1029/2021JD035599.
Elsaesser, G. S., C. W. O'Dell, M. D. Lebsock, R. Bennartz, T. J. Greenwald, and F. J. Wentz,
: The Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP). J. Climate, 30(24), 10193–10210, doi:10.1175/JCLI-D-16-0902.1.
ElSadaani, M., W. F. Krajewski, and D. L. Zimmerman,
: River network based characterization of errors in remotely sensed rainfall products in hydrological applications. Rem. Sens. Letts., 9(8), 743-752, doi:10.1080/2150704X.2018.1475768.
El-Bouhali, A., K. E. O. Ech-Chahdi, M. Y. Ztait, M. Amyay, and M. El Mazi ,
: Performance Evaluation of IMERG Satellite-Based Precipitation Estimates Against Rain Gauge Records in the Sebou Watershed, Morocco. Rem. Sens. in Earth Sys. Sci., 9(13), 13, doi:10.1007/s41976-025-00262-z.
Ejaz, N., and J. Bahrawi,
: Assessment of Drought Severity and Their Spatio-Temporal Variations in the Hyper Arid Regions of Kingdom of Saudi Arabia: A Case Study from Al-Lith and Khafji Watersheds. Atmosphere, 13(8), 1264, doi:10.3390/atmos13081264.
Eidhammer, T., A. Gettelman, K. Thayer-Calder, D. Watson-Parris, G. Elsaesser, H. Morrison, M. van Lier-Walqui, C. Song, and D. McCoy,
: An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6. Geoscientific Model Development, 17(21), 7835–7853, doi:10.5194/gmd-17-7835-2024.
Ehsani, M. R., S. Heflin, C. B. Risanto, and A. Behrangi,
: How well do satellite and reanalysis precipitation products capture North American monsoon season in Arizona and New Mexico?. Weather and Climate Extremes (Science Direct), 38, 100521, doi:10.1016/j.wace.2022.100521.
Ehsani, M. R., and Coauthors,
: 2019-2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities. Water, 12, 3067, doi:10.3390/w12113067.
Ehsani, M. R., and A. Behrangi,
: A comparison of correction factors for the systematic gauge-measurement errors to improve the global land precipitation estimate. J. Hydrology, 610, 127884, doi:10.1016/j.jhydrol.2022.127884.
Ehsani, M. R., A. Zarei, H. V. Gupta, K. Barnard, E. Lyons, and A. Behrangi,
: NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-21, doi:10.1109/TGRS.2022.3158888.
Ehsani, M. R., A. Behrangi, A. Adhikari, Y. Song, G. J. Huffman, and D. T. Bolvin,
: Assessment of the Advanced Very High Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning. J. Hydrometeor., 22(6), 1591–1608, doi:10.1175/JHM-D-20-0240.1.
Eghdami, M., and A. P. Barros,
: Vertical Dependence of Horizontal Scaling Behavior of Orographic Wind and Moisture Fields in Atmospheric Models. Earth and Space Science, 6(10), 1957-1975, doi:10.1029/2018EA000513.
Eghdami, M., and A. P. Barros,
: Extreme Orographic Rainfall in the Eastern Andes Tied to Cold Air Intrusions. Front. Environ. Sci., 7, 101, doi:10.3389/fenvs.2019.00101.
Edrich, A.-K., A. Yildiz, R. Roscher, A. Bast, F. Graf, and J. Kowalski,
: A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning. Natural Hazards, 120(9), 8953–8982, doi:10.1007/s11069-024-06563-8.
Eckert, E., D. Hudak, É. Mekis, P. Rodriguez, B. Zhao, Z. Mariani, S. Melo, K. Strong, and K. A. Walker,
: Validation of the Final Monthly Integrated Multisatellite Retrievals for GPM (IMERG) Version 05 and Version 06 with Ground-Based Precipitation Gauge Measurements across the Canadian Arctic. J. Hydrometeorology, 23(5), 715–731, doi:10.1175/JHM-D-21-0040.1.