Addressing Forecast Challenges at the Satellite Proving Ground for Marine, Precipitation, and Satellite Analysis

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 National Weather Service (NWS) and National Environmental Satellite, Data, and Information (NESDIS) operations. The Satellite Proving Ground for Marine, Precipitation, and Satellite Analysis (MPS PG) has been an active participant in the large Satellite Proving Ground for about five years and consists of the NWS Ocean Prediction Center (OPC), Weather Prediction Center (WPC), Tropical Analysis and Forecast Branch (TAFB) of the National Hurricane Center, and the NESDIS Satellite Analysis Branch (SAB). The first four years have focused on introducing new GOES-R and JPSS proxy products to forecasters using current data from GOES, MTSAT, METEOSAT, MODIS (Aqua and Terra), and S-NPP. With the advent of Himawari-8 imagery into the operational satellite suite at these centers, the forecasters are being introduced to satellite products with GOES-R era spectral, spatial, and temporal resolutions. These new capabilities may offer new methods to analyze and forecast weather phenomena that in the past were difficult to diagnose and forecast.

This presentation seeks to highlight some of the research projects embedded in the MPS PG that are addressing forecast challenges such as extreme rainfall, explosive cyclogenesis and associated hurricane-force wind events, extratropical transition of hurricanes/typhoons, and severe maritime convection. The projects involve forecasters and meteorologist interns who have used products such as the GLD-360 Lightning Density as a proxy for the GOES-R Geostationary Lightning Mapper or Air Mass RGB to analyze these weather events and document how the products performed. This along with forecaster feedback will help pave the way for future satellite product integration in the forecasting process.