FV3-LAM Convection-Allowing Model Forecasts and Ensemble Consensus Products for the 13th HMT Winter Weather Experiment

During the winter of 2022-2023, the Center for Analysis and Prediction of Storms (CAPS) ran an ensemble of convection-allowing model (CAM) forecasts as part of the 13^1h Annual NOAA Weather Prediction Center Hydrometeorology Testbed (WPC-HMT) Winter Weather Experiment (13^th WWE). For the 13^th WWE, the CAPS CAM ensemble comprised 15 members of the FV3 Limited Area Model (FV3-LAM) at ~3-km grid spacing over the contiguous United States (CONUS). The forecast ensemble and ensemble consensus products were produced for 30 days within the WWE period (from mid-November to mid-March). Specific forecast days of interest were determined in consultation with NWS forecasters via the HMT meteorologists at the Weather Prediction Center. Ensemble consensus products such as simple mean, probability matched (PM) mean and local-probability matched (LPM) mean were produced from the ensemble. Individual members and ensemble consensus products were evaluated for their ability to improve on winter weather forecasting, particularly snow accumulation.

Another objective of the experiment was to test possible combinations of physics packages for use in the future NOAA operational regional prediction system, such as the Rapid Refresh Forecast System (RRFS) CAM ensemble forecasting system, being developed to replace current suites of regional operational models.

The CAPS CAM ensemble was designed with a control member and 14 additional members with physics variations and/or variations in initial conditions and boundary conditions. The physics variations consisted of different combinations of microphysics, planetary boundary layer (PBL), surface layer physics, and land surface model (LSM).

Graphics products relevant to the forecasting of winter weather were produced, including 2-m temperature, liquid-equivalent precipitation and snowfall accumulation at 6- and 24-h intervals, precipitation type paintball plots, and precipitation type plots for each ensemble member. Ensemble consensus products and machine-learning product graphics were also generated. These graphics products are publicly viewable at https://caps.ou.edu/forecast/realtime/ .

The plot below shows the frequency bias and equitable threat score (ETS) for each of the 15 ensemble members (green, blue, and purple colors) for 24-h snowfall exceeding 3” for three separate periods (12-36h, 36-60h, and 60-84h). Also shown (in orange/brown) are statistics for three different computations of the ensemble mean over the same periods. The ETS scores show the value of computing ensemble means, as they outperform any single ensemble member. Other subjective and objective evaluations of the CAPS individual ensemble members and ensemble consensus products will be presented, as well as implications for developing a RRFS ensemble.