An Active–Passive Microwave Land Surface Database From GPM

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Munchak, S. Joseph, Ringerud, Sarah, Brucker, Ludovic, You, Yalei, de Gelis, Iris, Prigent, Catherine
• Other Authors: LERMA Cergy (LERMA), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères (LERMA (UMR_8112)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; Sea ice
• Online Access: https://hal.sorbonne-universite.fr/hal-03045732; https://hal.sorbonne-universite.fr/hal-03045732/document; https://hal.sorbonne-universite.fr/hal-03045732/file/manuscript.pdf; https://doi.org/10.1109/TGRS.2020.2975477

International audience A microwave emissivity retrieval is applied to five years of Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations over land and sea ice. The emissivities are co-located with GPMs Dual-frequency Precipitation Radar (DPR) surface backscatter measurements in clear-sky conditions. The emissivity-backscatter database is used to characterize surfaces within the GPM orbit for precipitation retrieval algorithms and other applications. The full 10-166 GHz emissivity vector is retrieved using optimal estimation. Since GMI includes water vapor sounding channels, retrieval of the atmospheric and surface state are performed simultaneously. Using the MERRA2 reanalysis as the a priori atmospheric state and with proper characterization of its error, we are able to effectively screen for cloud-and precipitation-affected emissivities. Comparisons with co-located CloudSat data show that this GMI-based screen is able to detect precipitation that DPR alone does not; however, about 10% of precipitation occurrence from CloudSat is still undetected by GMI. The unsupervised Kohonen classification technique was then applied to multi-year monthly 0.25 • gridded mean retrieved emissivities and backscatter distinctly for snow-free, snow-covered, and sea ice surfaces in order to classify surfaces based on both active and passive microwave characteristics. The classes correspond to vegetation coverage and type, inundation zones, soil composition, and terrain roughness. Snow and sea ice surfaces show clear seasonal cycles representing the increase in snow and ice spatial extent and reduction in the spring. Applications toward GPM precipitation retrieval algorithms and sensitivity to accumulated rain and snowfall are also explored.