2B.1 An Operational Passive Microwave Snowfall Rate Product and Its Application in Hydrology

Monday, 8 January 2018: 10:30 AM
Room 18B (ACC) (Austin, Texas)
Huan Meng, NOAA/NESDIS/Center for Satellite Applications and Research, College Park, MD; and J. Dong, C. Kongoli, R. R. Ferraro, B. Yan, L. Zhao, P. Xie, and R. Joyce

A passive microwave-based over land snowfall rate (SFR) product has been produced operationally at the National Oceanic and Atmospheric Administration since 2012 (Meng et al., 2017). The product utilizes measurements from Advance Technology Microwave Sounder (ATMS) aboard S-NPP, and from Advanced Microwave Sounding Unit (AMSU) and Microwave Humidity Sounder (MHS) pair aboard four NOAA and EUMETSAT polar-orbiting satellites. The algorithm includes two main components: snowfall detection (SD) and snowfall rate estimation (SFR). Both components rely on the high frequencies at and above 88/89 GHz due to their sensitivity to solid precipitation. The SD is a statistical algorithm composed of three modules: a satellite-based module coupling principal components of high frequency measurements and logistic regression model (Kongoli et al., 2015), a NWP model data-based logistic regression model, and a set of filters from NWP model data. The first two modules are optimally combined to provide the probability of snowfall (Kongoli et al., 2017). The snowfall rate component is a physical algorithm that retrieves cloud properties following a variational method (Yan et al., 2008). Snowfall rate is further derived based on these properties (Meng et al, 2016). The SFR algorithm has benefited greatly from continuous development and improvement since it was transitioned to operation. The product has been validated extensively against gauge observations and radar snowfall rate estimates with satisfactory results. Current development includes the extension of SFR retrievals to utilizing measurements from GMI aboard NASA GPM and from SSMIS aboard DMSP F16, F17, and F18. The SFR product enables global blended precipitation analyses (such as NOAA CMORPH) to include satellite-based winter precipitation estimates. Traditionally, such data sets either lack snowfall estimates or rely on other data sources such as gauge and model.

This presentation will include a description of the SFR algorithm and some recent development, product validation against ground observations and radar product, and its application in hydrology.

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