Improvement and Validation of NESDIS Satellite Snowfall Rate Algorithm

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Wednesday, 7 January 2015
Jun Dong, University of Maryland, College Park, MD; and H. Meng, C. Kongoli, and R. R. Ferraro

Snowfall Rate (SFR) retrieval using satellite passive microwave data is a challenging research area. The current NOAA/NESDIS operational SFR retrieval algorithm (Meng et al., 2012) applies to measurements from AMSU-A, MHS, and ATMS sensors aboard five NOAA, NASA and EUMETSAT polar-orbiting satellites: NOAA-18, NOAA-19, MetOp-A, MetOp-B and NPP. This is a physically based algorithm with a constant adjusting factor for ice water content at cloud base. The adjusting factor is derived using matching satellite and StageIV, a radar and gauge combined precipitation data set. A statistical model was developed based on cloud microphysics to better fit satellite retrievals to StageIV data. The new algorithm was validated against StageIV and another radar precipitation product, NMQ. The result shows that the new algorithm performs considerably better than the previous version. The new algorithm has been implemented in a near real-time processing system and, by using direct broadcast (DB) satellite data, the new SFR retrieval system can provide snowfall rate estimates within 30 minutes of observations over CONUS.