The Testing of Deterministic and Probabilistic Precipitation Type Algorithms During the 2017-2018 HMT-WPC Winter Weather Experiment

In an effort to support improvements in both the Weather Prediction Center (WPC) and National Weather Service (NWS) Weather Forecast Offices (WFOs) winter weather forecasts, the Hydrometeorology Testbed at WPC (HMT-WPC) conducted the 8th Annual Winter Weather Experiment (WWE) over the winter season from November 14th, 2017 to March 9th, 2018. The experiment brings together members of the operational forecasting, research, and academic communities to address winter weather forecast challenges. The combination of remote and residence execution of the WWE over the winter season served to enhance collaboration among NCEP centers, WFOs, and NOAA research labs on winter weather forecasting.

The 2017-18 WWE provided an opportunity for participants to evaluate a suite of both deterministic and probabilistic precipitation type algorithms in a real-time setting to assign the best precipitation type for the thermal environment. Experimental data sets were used side by side with current operational methods. The science objectives were to explore the testing of multiple microphysical and probabilistic precipitation type methodologies to determine which methods best enhance the winter forecast process.

This talk will highlight the results of testing the environmental assessment, microphysical post-processing, and model explicit approaches to precipitation type prediction among high-resolution and global models, WPC methodologies, and the National Blend of Models.