An Operational Satellite Snowfall Rate Product at NOAA/NESDIS

A passive microwave-based overland snowfall rate (SFR) product has been produced operationally at the National Oceanic and Atmospheric Administration since 2012 (Meng et al., 2017; Ferraro et al., 2018). The product utilizes measurements from passive microwave sounders or imager/sounders ATMS, AMSU/MHS, GMI, and SSMIS. These instruments are aboard NOAA-20, S-NPP, POES, Metop, GPM, and DMSP polar-orbiting satellites, respectively. The SFR algorithm includes two main components: snowfall detection (SD) and snowfall rate estimation. Both components rely on the high frequencies above 88 GHz due to their sensitivity to solid precipitation. The SD is a statistical algorithm that optimally combines the outputs from a satellite module and from a NWP-model module to provide the probability of snowfall (Kongoli et al., 2015, 2018). The snowfall rate component is a physical algorithm that retrieves cloud properties from a 1DVAR (Yan et al., 2008). Snowfall rate is further derived based on these properties. Both the snowfall detection and rate estimation algorithms have been validated against gauge observations and radar snowfall rate estimates with satisfactory results. Currently, SFR is being generated from a suite of nine satellites and provides a relatively high temporal coverage over global land. The product enables 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. The SFR product has also undergone official assessment at NWS Forecast Offices and demonstrated its utility in weather forecasting.

This presentation will include a description of the SFR algorithm, some recent development on NOAA-20, GPM, and DMSP SFR algorithms, product calibration, validation, and applications.