GPM Refereed Publications
Tyagi, A., R. K. Tiwari, and N. James,
: Mapping the landslide susceptibility considering future land-use land-cover scenario. Landslides, 20, 65–76, doi:10.1007/s10346-022-01968-7.
Tyagi, S., D. K. Pandey, D. Putrevu P. K. Srivastava and A. Misra,
: Machine Learning Based Soil Moisture Retrieval Algorithm and Validation at Selected Agricultural Sites Over India Using Cygnss Data. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, , 6335-6338, doi:10.1109/IGARSS47720.2021.9555095.
Tyralis, H., G. Papacharalampous, N. Doulamis, and A. Doulamis,
: Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 6969-6979, doi:10.1109/JSTARS.2023.3297013.
Tzepkenlis, A., N. Grammalidis, C. Kontopoulos, V. Charalampopoulou, D. Kitsiou, Z. Pataki, A. Patera, and T. Nitis,
: An Integrated Monitoring System for Coastal and Riparian Areas Based on Remote Sensing and Machine Learning. J. Marine Science and Engineering, 10(9), 1322, doi:10.3390/jmse10091322.
Uddin, Md. J., Y. Li, Md. Abdus Sattar, M. Liu, and N. Yang,
: An Improved Cluster-Wise Typhoon Rainfall Forecasting Model Based on Machine Learning and Deep Learning Models Over the Northwestern Pacific Ocean. JGR Atmospheres, 127(14), e2022JD036603, doi:10.1029/2022JD036603.
Ueno, K., W. Mito, R. Kanai, Y. Ueji, K. Suzuki, H. Kobayashi, I. Tamagawa, M. K. Yamamoto, and S. Shige,
: Distribution of precipitation depending on synoptic scale disturbances with satellite estimate comparisons in the Japanese Alps area during warm seasons. Journal of Geography, 128, 31-47, doi:.
Ueyama, R., M. Schoeberl, E. Jensen, L. Pfister, M. Park, J.-M. Ryoo,
: Convective Impact on the Global Lower Stratospheric Water Vapor Budget. JGR Atmospheres, 128(6), e2022JD037135, doi:10.1029/2022JD037135.
Ullah, S., N. Shahzad, L. Yan, Z. Zuo, I. Iqbal, and M. J. Tareen,
: Enhancing Fine-Resolution Precipitation Estimates in Data-Scarce Regions: A Novel Spatial Downscaling and Correction Framework. Earth Systems and Environment, , , doi:10.1007/s41748-025-00758-0.
Ullah, S., Z. Zuo, F. Zhang, J. Zheng, S. Huang, Y. Lin, I. Iqbal, Y. Sun, M. Yang, and L. Yan,
: GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm. Rem. Sens., 12(19), 3162, doi:10.3390/rs12193162.
Ullrich, P. A., C. M. Zarzycki, E. E. McClenny, M. C. Pinheiro, A. M. Stansfield, and K. A. Reed,
: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets. Geosci. Model Dev., 14(8), 5023–5048, doi:10.5194/gmd-14-5023-2021.
Umakanth, N., R. Gogineni, K. M. M. Rao, B. R. Reddy, S. H. Ahammad, and M. C. Rao,
: Spatial Analysis of Tropical Cyclone Yaas using Satellite Data. Malaysian Journal of Science, 43(4), 54-67, doi:10.22452/mjs.vol43no4.7.
Umirbekov, A., M. D. Peña-Guerrero, I. Didovets, H. Apel, A. Gafurov, and D. Müller,
: The value of hydroclimatic teleconnections for snow-based seasonal streamflow forecasting in central Asia. Hydrology and Earth System Sciences, 29(14), 3055–3071, doi:10.5194/hess-29-3055-2025.
Upadhyaya, S., P. E. Kirstetter, J. J. Gourley, and R. J. Kuligowski,
: On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates. J. Hydrometeor., 21(6), 1367–1381, doi:10.1175/JHM-D-19-0293.1.
Upadhyaya, S., P. E. Kirstetter, R. Kuligowski, and M. Searls,
: Classifying precipitation from GEO satellite observations: Diagnostic model. Quart. Journal of the Royal Meteorological Society, 147(739), 3318-3334, doi:10.1002/qj.4130.
Upadhyaya, S., P. E. Kirstetter, R. Kuligowski, J. Gourley, and H. Grams,
: Classifying precipitation from GEO satellite observations: Prognostic model. Quart. Journal of the Royal Meteorological Society, 147(739), 3394-3409, doi:10.1002/qj.4134.
Upadhyaya, S., P.E. Kirstetter, R. Kuligowski, M. Searls,
: Exploring the Temporal Information From GEO Satellites for Estimating Precipitation With Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters, 19, 1005905, doi:10.1109/LGRS.2022.3189535.
Upadhyaya, S., P.E. Kirstetter, R. Kuligowski, M. Searls,
: Towards improved precipitation estimation with the GOES-16 advanced baseline imager: Algorithm and evaluation. Quart. Journal of the Royal Meteoro. Soc., 148(748), 3406-3427, doi:10.1002/qj.4368.
Usman, M., and K. Heki,
: Satellite gravimetry observations on the state of groundwater level variability in the Arabian Peninsula Region and the associated socio-economic sustainability challenges. Groundwater for Sustainable Development, 26, 101270, doi:10.1016/j.gsd.2024.101270.
Utsumi, N., and H. Kim,
: Warm Season Satellite Precipitation Biases for Different Cloud Types Over Western North Pacific. IEEE Geoscience and Remote Sensing Letters, 15, 808–812, doi:10.1109/LGRS.2018.2815590.
Utsumi, N., F. J. Turk, Z. S. Haddad, P.-E. Kirstetter, and H. Kim,
: Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals. J. Hydrometeor., 22(1), 95–112, doi:10.1175/JHM-D-20-0160.1.