GPM Refereed Publications
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.
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.
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.
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.
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:.
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.
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.
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.
Tyagi, A., R. K. Tiwari, and N. James,
: Identification of the significant parameters in spatial prediction of landslide hazard. Bulletin of Engineering Geology and the Environment, 82(8), 307, doi:10.1007/s10064-023-03334-w.
Tyagi, A., R. K. Tiwari, and N. James,
: Prediction of the future landslide susceptibility scenario based on LULC and climate projections. Landslides, 20, 1837–1852, doi:10.1007/s10346-023-02088-6.
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.
Turk, J., Z. Haddad, P. E. Kirstetter, S. Ringerud, Y. You,
: An observationally based method for stratifying a priori passive microwave observations in a Bayesian‐based precipitation retrieval framework. Q. J. R. Meteor. Soc., 144, 145-164, doi:10.1002/qj.3203.
Turk, F. J., Z. S. Haddad, and Y. You,
: Principal Components of Multifrequency Microwave Land Surface Emissivities. Part I: Estimation under Clear and Precipitating Conditions. J. Hydrometeor., 15, 3-19, doi:10.1175/JHM-D-13-08.1.
Turk, F. J., Z. S. Haddad and Y. You,
: Estimating Nonraining Surface Parameters to Assist GPM Constellation Radiometer Precipitation Algorithms. J. Atmos. Oceanic Technol., 33, 1333–1353, doi:10.1175/JTECH-D-15-0229.1.
Turk, F. J., S. Hristova-Veleva, S. L. Durden, S. Tanelli, O. Sy, G. D. Emmitt, S. Greco, and S. Q. Zhang,
: Joint analysis of convective structure from the APR-2 precipitation radar and the DAWN Doppler wind lidar during the 2017 Convective Processes Experiment (CPEX). Atmos. Meas. Tech., 13(8), 4521–4537, doi:10.5194/amt-13-4521-2020.
Turk, F. J., S. E. Ringerud, Y. You, A. Camplani, D. Casella, G. Panegrossi, P. Sano, A. Ebtehaj, C. Guilloteau, N. Utsumi, C. Prigent, and C. Peters-Lidard,
: Adapting Passive Microwave-Based Precipitation Algorithms to Variable Microwave Land Surface Emissivity to Improve Precipitation Estimation from the GPM Constellation. J. Hydrometeor., 22(7), 1755-1781, doi:10.1175/JHM-D-20-0296.1.
Turk, F. J., S. E. Ringerud, A. Camplani, D. Casella, R. J. Chase, A. Ebtehaj, J. Gong, M. Kulie, G. Liu, L. Milani, G. Panegrossi, R. Padullés, J.-F. Rysman, P. Sanò, S. Vahedizade, and N. Wood,
: Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset. Remote Sens., 13(12), 2264, doi:10.3390/rs13122264.
Turk, F. J., R. Sikhakolli, P. Kirstetter, and S. L. Durden,
: Exploiting Over-Land OceanSat-2 Scatterometer Observations to Capture Short-Period Time-Integrated Precipitation. J. Hydrometeor., 16, 2519-2535, doi:10.1175/JHM-D-15-0046.1.
Turk, F. J., R. Padullés, E. Cardellach, C. O. Ao, K.-N. Wang, D. D. Morabito, M. de la Torre Juarez, M. Oyola, S. Hristova-Veleva, and J. D. Neelin,
: Interpretation of the Precipitation Structure Contained in Polarimetric Radio Occultation Profiles Using Passive Microwave Satellite Observations. J. Atmos. Oceanic Technol., 38(10), 1727–1745, doi:10.1175/JTECH-D-21-0044.1.
Turk, F. J., R. Padullés, D. D. Morabito, T. Emmenegger, and J. D. Neelin,
: Distinguishing Convective-Transition Moisture-Temperature Relationships with a Constellation of Polarimetric Radio Occultation Observations in and near Convection. Atmosphere, 13(2), 259, doi:10.3390/atmos13020259.