GPM Refereed Publications
Kim, D., and C. J. Matyas,
: Classification of tropical cyclone rain patterns using convolutional autoencoder. Scientific Reports, 14(1), 791, doi:10.1038/s41598-023-50994-5.
Kim, D., D.-B. Shin, J. Kim, M.-J. Kang, H.-K. Lee, and H.-Y. Chun,
: Differences in Satellite-Based Latent Heating Profiles Between the 2015/2016 Disruption and Westerly Phase of the Quasi-Biennial Oscillation. JGR Atmospheres, 127(19), e2021JD036254, doi:10.1029/2021JD036254.
Kim, D., S. Lee, S. Cho, D. Kim, and M. Choi,
: Evaluating rainfall estimates derived from soil moisture using soil hydraulic properties over the Korean Peninsula. J. Hydrology, 663(Part B), 134267, doi:10.1016/j.jhydrol.2025.134267.
Kim, J.-H., M.-L. Ou, J.-D. Park, K. R. Morris, M. R. Schwaller, and D. B. Wolff,
: Global Precipitation Measurement (GPM) Ground Validation (GV) Prototype in the Korean Peninsula. J. Atmos. Oceanic Technol., 31, 1902-1921, doi:10.1175/JTECH-D-13-00193.1.
Kim, J.-W., J. Y. Yu, and B. Tian,
: Overemphasized role of preceding strong El Niño in generating multi-year La Niña events. Nature Comm., 14, 6790, doi:10.1038/s41467-023-42373-5.
Kim, M., J. Jin, A. El Akkraoui, W. McCarty, R. Todling, W. Gu, and R. Gelaro,
: The Framework for Assimilating All-Sky GPM Microwave Imager Brightness Temperature Data in the NASA GEOS Data Assimilation System. Mon. Wea. Rev., , , doi:10.1175/MWR-D-19-0100.1.
Kim, M.-J., J. Jin, A. El Akkaroui, W. McCarty, R. Todling, W. Gu, and R. Gelaro,
: All-sky Microwave Imager Data Assimilation at NASA GMAO. JCSDA Newsletter 2017 Summer Issue, , , doi:10-29T14:12:30+00:00Z.
Kim, S.-H., J. Shin, D.-W. Kim, Y.-H. Jo,
: Estimation of subsurface salinity and analysis of Changjiang diluted water volume in the East China Sea. Frontiers in Marine Science, 10, 1247462, doi:10.3389/fmars.2023.1247462.
Kim, Y., J.-H. Park, and J. Du,
: Monitoring Surface Water Content and Biogeochemical Responses In The Area Surrounding River Mouths Using Multi-Source Satellite Remote Sensing. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, , 3742-3744, doi:10.1109/IGARSS52108.2023.10283466.
King, F., C. Pettersen, C. G. Fletcher, and A. Geiss,
: Development of a Full-Scale Connected U-Net for Reflectivity Inpainting in Spaceborne Radar Blind Zones. Artificial Intelligence for the Earth Systems, 3(2), , doi:10.1175/AIES-D-23-0063.1.
King, F., C. Pettersen, et al.,,
: A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package. Earth and Space Science, 11(5), e2024EA003538, doi:10.1029/2024EA003538.
Kirchmeier-Young, M. C., F. W. Zwiers, N. P. Gillett, and A. J. Cannon,
: Attributing extreme fire risk in Western Canada to human emissions. Climatic Change, 144, 365-379, doi:10.1007/s10584-017-2030-0.
Kirschbaum, D., and T. Stanley,
: Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness. Earth's Future, 6(3), 505-523, doi:10.1002/2017ef000715.
Kirschbaum, D., G. Huffman, G. Skofronick-Jackson, S. Braun, E. Stocker, K. Garrett, E. Jones, R. Adler, H. Wu, A. McNally, and B. Zaitchik,
: NASA’s Remotely-sensed Precipitation: A Reservoir for Applications Users. Bull. Amer. Meteor. Soc., 98, 1169-1184, doi:10.1175/BAMS-D-15-00296.1.
Kirschbaum, D., S. Kapnick, T. Stanley, and S. Pascale,
: Changes in Extreme Precipitation and Landslides Over High Mountain Asia. Geophysical Research Letters, 47(4), e2019GL085347, doi:10.1029/2019GL085347.
Kirstetter, P. E., N. Karbalaee, K. Hsu, and Y. Hong,
: Probabilistic Precipitation Rate Estimates with Space-based Infrared Sensors. Q. J. R. Meteor. Soc., 144, 191-205, doi:10.1002/qj.3243.
Kirstetter, P. E., Y. Hong, J. J. Gourley, M. Schwaller, W. Petersen, and J. Zhang,
: Comparison of TRMM 2A25 Products Version 6 and Version 7 with NOAA/NSSL Ground Radar-based National Mosaic QPE. J. Hydrometeor., 14(2), 661-669, doi:10.1175/JHM-D-12-030.1.
Kirstetter, P.-E., Y. Hong, J.J. Gourley, Q. Cao, M. Schwaller, and W. Petersen,
: A research framework to bridge from the Global Precipitation Measurement mission core satellite to the constellation sensors using ground radar-based National Mosaic QPE. In L. Venkataraman, Remote Sensing of the Terrestrial Water Cycle. AGU books Geophysical Monograph Series, Chapman monograph on remote sensing. John Wiley & Sons Inc., 206, 61-79, doi:ISBN: 1118872037.
Kirstetter, P.E., J.J. Gourley, Y. Hong, J. Zhang, S. Moazamigoodarzi, C. Langston, and A. Arthur,
: Probabilistic Precipitation Rate Estimates with Ground-based Radar Networks. Water Resources Research, 51, 1422-1442, doi:10.1002/2014WR015672.
Kirstetter, P.E., N. Viltard and M. Gosset,
: An error model for instantaneous satellite rainfall estimates: evaluation of BRAIN-TMI over West Africa. Q. J. R. Meteor. Soc., 139, 894-911, doi:10.1002/qj.1964.