578 Utilizing a Remotely-Sensed Snowfall Rate Algorithm to Verify Experimental Snowfall Rate Forecasts in the WPC Hydrometeorology Testbed

Tuesday, 24 January 2017
4E (Washington State Convention Center )
Sarah Perfater, IMSG, College Park, MD; and M. J. Folmer, B. Albright, and H. Meng

The 2016 Winter Weather Experiment conducted in NOAA’s Weather Prediction Center Hyrdometeorology Testbed (WPC-HMT) featured both deterministic and probabilistic experimental high-resolution short-range guidance used to create daily probabilistic snowfall rate forecasts aiming to test the skill of predicting the areal and temporal scales of impactful heavy periods of snowfall within a pseudo-operational environment.  Verification of these snowfall rates is a challenge with the ground-truth offered by METARs, archived radar reflectivity, NWS local storm reports, and Stage IV QPE coupled with thermal profiles which identify precipitation type.

A 16-km snowfall rate product (SFR) has been developed at NESDIS/STAR with funding through the Joint Polar Satellite System (JPSS) Proving Ground and Product System Development and Implementation (PSDI).  The algorithm uses data from polar-orbiting microwave sensors: four Advanced Microwave Sounding Unit-A (AMSU-A)/Microwave Humidity Sounder (MHS) pairs and one Advanced Technology Microwave Sounder (ATMS) aboard NOAA POES, EUMETSAT Metop, and S-NPP satellites, respectively.  The SFR is able to provide quantitative snowfall information to complement snowfall observations or estimates from other sources (stations, radar, GOES imagery data etc.), and to fill observational gaps in mountains and remote regions where radar and weather stations are sparse or radar blockage and overshooting are common.

This poster will show how the SFR can be used to enhance verification for experimental probabilistic snowfall rate forecasts created using experimental snowfall rate guidance in the WPC-HMT.

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