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-Calibrating Multivariate Precipitation Retrieval (SCaMPR; Kuligowski 2002 ; Kuligowski et al. 2016 ) algorithm is the operational precipitation retrieval algorithm for GOES-16 / GOES-17 . SCaMPR is trained with passive microwave (PMW) precipitation rate estimates from the Climate Prediction Center (CPC) combined microwave (MWCOMB) dataset ( Joyce et al. 2004 ). While visible/infrared (VIS/IR) sensors on board GEO platforms are primarily sensitive to the cloud-top properties indirectly related to surface
-Calibrating Multivariate Precipitation Retrieval (SCaMPR; Kuligowski 2002 ; Kuligowski et al. 2016 ) algorithm is the operational precipitation retrieval algorithm for GOES-16 / GOES-17 . SCaMPR is trained with passive microwave (PMW) precipitation rate estimates from the Climate Prediction Center (CPC) combined microwave (MWCOMB) dataset ( Joyce et al. 2004 ). While visible/infrared (VIS/IR) sensors on board GEO platforms are primarily sensitive to the cloud-top properties indirectly related to surface
Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class1. Introduction and motivation Variability in precipitation typology affects vertical water and energy fluxes though the associated precipitation structure, dynamics, microphysical processes, and latent heat release. The distribution of convective and stratiform precipitation impacts Earth’s radiative properties and atmospheric circulation. While the differences in microphysical processes and dynamics in convective and stratiform systems are well documented in the literature (e.g., Houze 1997
1. Introduction and motivation Variability in precipitation typology affects vertical water and energy fluxes though the associated precipitation structure, dynamics, microphysical processes, and latent heat release. The distribution of convective and stratiform precipitation impacts Earth’s radiative properties and atmospheric circulation. While the differences in microphysical processes and dynamics in convective and stratiform systems are well documented in the literature (e.g., Houze 1997
://doi.org/10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2 . 10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2 Letu , H. , and Coauthors , 2020 : High-resolution retrieval of cloud microphysical properties and surface solar radiation using Himawari-8/AHI next-generation geostationary satellite . Remote Sens. Environ. , 239 , 111583, https://doi.org/10.1016/j.rse.2019.111583 . 10.1016/j.rse.2019.111583 Levizzani , V. , F. Porcú , F. S. Marzano , A. Mugnai , E. A. Smith , and F. Prodi
://doi.org/10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2 . 10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2 Letu , H. , and Coauthors , 2020 : High-resolution retrieval of cloud microphysical properties and surface solar radiation using Himawari-8/AHI next-generation geostationary satellite . Remote Sens. Environ. , 239 , 111583, https://doi.org/10.1016/j.rse.2019.111583 . 10.1016/j.rse.2019.111583 Levizzani , V. , F. Porcú , F. S. Marzano , A. Mugnai , E. A. Smith , and F. Prodi
