The Houston Derecho: A MPAS Versus HRRR Case Study as Part of The NOAA Hazardous Weather Testbed Annual Spring Forecast Experiment

Every year in May, NOAA’s Hazardous Weather Testbed (HWT) Experimental Forecast Program (EFP), under the guidance of the Storm Prediction Center (SPC) and National Severe Storms Laboratory (NSSL), conducts the Spring Forecasting Experiment (SFE). Scientists use the SFE to accelerate the transfer of promising new tools from research to operations, inspire new initiatives for research, and identify and document performance of forecast modeling systems.

While the SFE compared a total of five different models in 2024, this research project will focus on the currently-operational High Resolution Rapid Refresh (HRRR) model, and three different runs of models using the Model for Prediction Across Scales (MPAS) dynamic core, which is currently in development. The HRRR and the three MPAS variations differ in their initial conditions and microphysics and will be verified through comparing what the respective model forecasted versus what was observed.

More specifically, this research project will focus on the Houston Derecho event of May 16, 2024 as a case study. This served as a way to determine how well the models ran during one particularly extreme convective event and perhaps provide insight to how well they performed throughout the entire experiment.

Through this verification effort, promising results for the MPAS dynamic core were delivered. We concluded that (1) the MPAS dynamic core is on par with the HRRR dynamic core, (2) RRFS initial conditions appear on par with HRRR initial conditions, sometimes performing even better, and (3) using MPAS in a future version of the RRFS will likely prove beneficial for forecasting capabilities.

With plans for operational implementation of MPAS dynamic core in the near future, comparing it with the currently-operational HRRR using the WRF dynamic core will prove vital to ensuring it is ready. Overall, this is all in hopes of making forecasting more accurate, especially for convective weather.