GPM Refereed Publications
Gautam, J., O. Sungmin, V. Degavath, and A. Mahesha,
: Evaluation of GPM IMERG satellite precipitation for rainfall–runoff modelling in Great Britain. Hydrological Sciences Journal, 69(14), 2010-2025, doi:10.1080/02626667.2024.2394172.
Gebregiorgis, A., P. E. Kirstetter, Y. Hong, J. J. Gourley, G. Huffman, W. Petersen, X. Xue, M. Schwaller,
: To what extent is the Day-1 IMERG satellite rainfall estimate improved as compared to TMPA-RT. J. Geophy. Res., 123, 1694-1707, doi:10.1002/2017JD027606.
Gebregiorgis, A., P. E. Kirstetter, Y. Hong, N. Carr, J. J. Gourley, Y. Zheng,
: Understanding multi-sensor satellite precipitation error structure in level-3 TRMM products. J. Hydrometeor., 18, 285-306, doi:10.1175/JHM-D-15-0207.1.
Geer, A.J., F. Baordo, N. Bormann, P. Chambon, S. J. English, M. Kazumori, H. Lawrence, P. Lean, K. Lonitz, and C. Lupu,
: The growing impact of satellite observations sensitive to humidity, cloud and precipitation. Q. J. R. Meteor. Soc., 143(709), 3189-3206, doi:10.1002/qj.3172.
Geer, A.J., K. Lonitz, P. Weston, M. Kazumori, K. Okamoto, Y. Zhu, E. H. Liu, A. Collard, W. Bell, S. Migliorini, and P. Chambon,
: All‐sky satellite data assimilation at operational weather forecasting centres. Q. J. R. Meteor. Soc., 144(713), 1191-1217, doi:10.1002/qj.3202.
Getirana, A., D. Kirschbaum, F. Mandarino, M. Ottoni, S. Khan, and K. Arsenault,
: Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil . Remote Sensing, 12(24), 4095, doi:10.3390/rs12244095.
Ghosh, A., A. K. Varma, S. Shah, B. S. Gohil, and P. K. Pal,
: Rain identification and measurement using Oceansat-II scatterometer observations. Remote Sensing of Environment, 142, 20-32, doi:10.1016/j.rse.2013.10.033.
Giangrande, S. E., S. Collis, A. K. Theisen, and A. Tokay,
: Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign. J. Appl. Meteor. Climatol., 53, 2130-2147, doi:10.1175/JAMC-D-13-0321.1.
Gilbert, E., D. Pishniak, J. A. Torres, A. Orr, M. Maclennan, N. Wever, and K. Verro,
: Extreme precipitation associated with atmospheric rivers over West Antarctic ice shelves: insights from kilometre-scale regional climate modelling. The Cryosphere, 19(2), 597–618, doi:10.5194/tc-19-597-2025.
Gilewski, P., and M. Nawalany,
: Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland. Water, 10, 1665, doi:10.3390/w10111665.
Glazer, R. H., E. Coppola, and F. Giorgi,
: Understanding Nocturnally Driven Extreme Precipitation Events over Lake Victoria in a Convection-Permitting Model. Mon. Wea. Rev., 153(6), 1085–1103, doi:10.1175/MWR-D-22-0339.1.
Godoy, M. R. V., Y. Markonis, J. R. Thomson, A. S. Ballarin, S. Perri, A. Molini, C. Miao, Q. Sun, M. Hanel, S. M. Papalexiou, C. Kummerow, and T. Oki,
: Which Precipitation Dataset to Choose for Hydrological Studies of the Terrestrial Water Cycle?. Bull. Amer. Meteor. Soc., 106(9), E2000–E2016, doi:10.1175/BAMS-D-24-0306.1.
Gohil, B. S., R. M. Gairola, A. K. Mathur, A. K. Varma, C. Mahesh, R. K. Gangwar and P. K. Pal,
: Algorithms for retrieving geophysical parameters from the MADRAS and SAPHIR sensors of the Megha-Tropiques satellite: Indian scenario. Quarterly Journal of Royal Meteorological Society, 139(673), 954-963, doi:10.1002/qj.2041.
Goldenstern, E., and C. D. Kummerow,
: Predicting Region-Dependent Biases in a GOES-16 Machine Learning Precipitation Retrieval. J. Appl. Meteor. Climatol., 62(7), 873–885, doi:10.1175/JAMC-D-22-0089.1.
Golian, S., M. Javadian, and A. Behrangi,
: On the use of satellite, gauge, and reanalysis precipitation products for drought studies. Environ. Res. Lett., 14, 075005, doi:10.1088/1748-9326/ab2203.
Golian, S., S. Moazami, P. E. Kirstetter, Y. Hong,
: Merging Multiple Satellite Rainfall Estimate Algorithms over a Complex Terrain. Water Resources Management, 29, 4885-4901, doi:10.1007/s11269-015-1096-6.
Gong, J. and D. L. Wu,
: Microphysical Properties of Frozen Particles Inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) Polarimetric Measurements . Atmos. Chem. and Phys. Discuss, 17, 2741–2757, doi:10.5194/acp-17-2741-2017.
Gong, J., X. Zeng, D. L. Wu, and X. Li,
: Diurnal Variation of Tropical Ice Cloud Microphysics: Evidence from Global Precipitation Measurement Microwave Imager Polarimetric Measurements. Geophys. Res. Letts., 45(2), 1185-1193, doi:10.1002/2017GL075519.
Gonzalez, J., V. Misra, and C. B. Jayansankar,
: The Impact of Cumulus Parameterization on Regional Climate Simulations of Central American Climate. J. Appl. Meteor. and Climatol., 64(11), 1629–1649, doi:10.1175/JAMC-D-24-0240.1.
Gonzalez, R., and C. D. Kummerow,
: An Evaluation of GPROF-Based Snowfall Retrievals and Their Training Data. J. Hydrometeorology, 26(5), 627–638, doi:10.1175/JHM-D-24-0106.1.