Severe Weather Signals in Convective- Allowing Forecasts: Comparing MPAS and FV3 for the 2023 and 2023 Convective Seasons.

Severe weather events have shown increased frequency, yet current weather models struggle to provide an accurate and timely forecast for severe weather events over all locations. As such, this research aims to improve the accuracy and timeliness of severe weather predictions by comparing two weather models: the Model for Prediction Across Scales (MPAS) and the Finite Volume Cubed (FV3). MPAS, with five ensemble members, and FV3, with ten ensemble members, were analyzed using updraft helicity (UH) and wind speed thresholds. The models' outputs were compared to SPC’s storm reports for the 2023 and 2024 convective seasons. By calculating the critical success index (CSI), equitable threat score (ETS), bias, and Heidke skill score (HSS), the study evaluates the models' forecast accuracy. Contingency tables were constructed based on hits, misses, false alarms, and correct nulls, providing a detailed comparison between observed and forecasted reports. This approach ensures a more accurate assessment of model performance, enhancing severe weather forecasts and leading to better preparedness and response strategies. Future research will include reducing the number of FV3 members to enable a more equitable analysis between the models.