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GPM Refereed Publications

Rysman, J.-F., G. Panegrossi, P. Sanò, A. Marra, S. Dietrich, L. Milani, and M. Kulie, : SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager. Remote Sens., 10(8), 1278, doi:10.3390/rs10081278.
Ryu, Y.-H., J. A. Smith, M. L. Baeck, L. Cunha, E. Bou-Zeid, and W. F. Krajewski, : The regional water cycle and heavy spring rainfall in Iowa: Observational and modeling analyses from the IFloodS campaign. J. Hydrometeor., 17(12), 2763–2784, doi:10.1175/JHM-D-15-0174.1.
Sam, S., and M. Özger, : A multi-method and multi-duration trend analysis of temperature and precipitation in Istanbul, Turkey, by using meteorological records, MERRA-2 reanalysis, and IMERG estimations. HydroResearch, 8, 209-222, doi:10.1016/j.hydres.2024.11.005 .
Sambath V., N. Dubois-Quilici, N. Viltard, A. Martini, and C. Mallet, : Unsupervised Domain Adaptation to Mitigate Out-of-Distribution Problem of Spatial Radiometer Images: Application to Quantitative Precipitation Estimation. IEEE Transactions on Geoscience and Remote Sensing, 62, 5301414, doi:10.1109/TGRS.2024.3403373.
Sanò P., G. Panegrossi, D. Casella, A. C. Marra, F. Di Paola, and S. Dietrich, : The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars. Atmos. Meas. Tech., 9, 5441-5460, doi:10.5194/amt-9-5441-2016.
Sano, P., G. Panegrossi, D. Casella, A. C. Marra, L. P. D'Adderio, J.-F. Rysman, and S. Dietrich, : The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer. Remote Sensing, 10(7), 1122, doi:10.3390/rs10071122.
Sapan, E. G. A., W. D. Susanti, B. H. Santosa, F. A. Wardhani, N. Widiatmoko, M. R. Yuvhenmindo, I. Ridwansyah, E. Triwisesa, and A. E. Pravitasari, : Flash Floods Impact in the Upper Citarum Watershed: A Hydrological and Hydraulic Simulation Approach. IOP Conference Series: Earth and Environmental Science, 1443(1), 012019, doi:10.1088/1755-1315/1443/1/012019.
Sapucci, C. R., V. C. Mayta, and L. da Silva Dias, : South American Intraseasonal Oscillation: EOF and Neural Network Approaches. JGR Atmospheres, 130(5), e2024JD041988, doi:10.1029/2024JD041988.
Sapucci, C. R., V. C. Mayta, and P. L. Silva Dias, : Predictability of Precipitation and Intraseasonal Variability: Insights From ECMWF Model Skill Over Brazil. Int'l Journal of Climatoloty, 45(7), e8820, doi:10.1002/joc.8820.
Sapucci, C. R., V. C. Mayta, and P. L. Silva Dias, : Predictability of Precipitation and Intraseasonal Variability: Insights From ECMWF Model Skill Over Brazil. Int'l Journal of Climatol., 45(7), e8820, doi:10.1002/joc.8820.
Sarkar, A. and J. Panda, : Comprehending dust aerosol impacts on cloud and rainfall distribution during a ‘dust-rain’ storm through WRF-Chem simulations. Natural Hazards, 121(14), 16481–16514, doi:10.1007/s11069-025-07438-2.
Sarkar, A., and J. Panda, : Comprehending dust aerosol impacts on cloud and rainfall distribution during a ‘dust-rain’ storm through WRF-Chem simulations. Natural Hazards, 121, 16481–16514, doi:10.1007/s11069-025-07438-2.
Sawada, M., and K. Ueno, : Heavy Winter Precipitation Events with Extratropical Cyclone Diagnosed by GPM Products and Trajectory Analysis. J. Meteor. Soc. Japan, 99(2), 473-496, doi:10.2151/jmsj.2021-024.
Scarino, B., K. Itterly, K. Bedka, C. R. Homeyer, J. Allen, S. Bang, and D. Cecil, : Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network. Artif. Intell. Earth Syst., 2(4), , doi:10.1175/AIES-D-22-0042.1.
Schiro, K. A., H. Su, F. Ahmed, N. Dai, C. E. Singer, P. Gentine, G. S. Elsaesser, J. H. Jiang, Y.-S. Choi, and J. D. Neelin, : Model spread in tropical low cloud feedback tied to overturning circulation response to warming. Nature Communications, 13(1), 7119, doi:10.1038/s41467-022-34787-4.
Schreck, C. J., M. A. Janiga, and S. Baxter, : Sources of Tropical Subseasonal Skill in the CFSv2. Mon. Wea. Rev., 148, 1553–1565, doi:10.1175/MWR-D-19-0289.1.
Schulte, R. M., and C. Kummerow, : Can DSD Assumptions Explain the Differences in Satellite Estimates of Warm Rain?. J. of Atmos. and Oceanic Tech., 39(12), 1889–1901, doi:10.1175/JTECH-D-22-0036.1.
Schulte, R., C. D. Kummerow, C. Klepp, and G. G. Mace, : How Accurately Can Warm Rain Realistically Be Retrieved with Satellite Sensors? Part I: DSD Uncertainties. J. Appl. Meteorol. Climatol., 61(9), 1087-1105, doi:10.1175/JAMC-D-21-0158.1.
Schulte, R., C. D. Kummerow, S. M. Saleebe and G. G. Mace, : How Accurately Can Warm Rain Realistically Be Retrieved with Satellite Sensors? Part II: Horizontal and Vertical Heterogeneities. J. Appl. Meteor. Climatol., 62(2), 155-170, doi:10.1175/JAMC-D-22-0051.1.
Schumacher, C., and A. Funk, : Assessing Convective-Stratiform Precipitation Regimes in the Tropics and Extratropics With the GPM Satellite Radar. Geophys. Res. Lett., 50(14), e2023GL102786, doi:10.1029/2023GL102786.