Analyzing Significant Precipitation Events Using GOES-R and JPSS Products (134)

Michael Folmer, UMD/ESSIC/CICS, College Park, MD

Andrew Orrison, NOAA/NWS/NCEP/WPC

Sarah Perfater, WPC/HMT

Benjamin Albright, WPC/HMT





The GOES-R and JPSS Proving Ground Programs were conceived to demonstrate and familiarize forecasters with the next generation geostationary and polar-orbiting satellite products and capabilities that will be incorporated into NOAA operations. These satellite product demonstrations at the Weather Prediction Center (WPC) and Satellite Analysis Branch (SAB) of the National Environmental Satellite, Data, and Information Service (NESDIS) have concentrated on heavy precipitation challenges that impact daily operations. Since the launch of Himawari-8, GOES-16, and recently, NOAA-20, many new forecasts products are available to forecasters at WPC and SAB to enhance the analysis and prediction of significant heavy precipitation events. The CIRA Layered Precipitable Water product has been used in both operations and the Flash Flood and Intense Rainfall (FFAIR) Experiment to identify moisture plumes and/or atmospheric rivers in different layers that could deliver excessive rainfall. The Quantitative Precipitation Estimate (QPE) and Rainfall Rate product as part of the GOES-R suite of products is still in the process of being introduced to operations, but can be used to estimate rainfall in poorly-observed regions. The NESDIS Snowfall Rate product has been used to improve diagnosis of heavy snow aloft that will eventually impact downstream areas. It has also been featured as a method of verification for snowfall rate forecasts in the Winter Weather Experiment at WPC. Additional products will be assessed as they become available in operations, including convective products to short term, intense rainfall and training cells (Overshooting Top Algorithm or multispectral imagery). This presentation will highlight some of the first uses of these new and in some cases, improved satellite products to address these forecast challenges.