Predictability of Severe Convective Storm Environments in Global Ensemble Forecast System, Version 12, Reforecasts

Abstract Prediction of severe convective storms at time scales of 2–4 weeks is of interest to forecasters and stakeholders due to their impacts on life and property. Prediction of severe convective storms on this time scale is challenging, since the large-scale weather patterns that drive this activity begin to lose dynamic predictability beyond week 1. Previous work related to severe convective storms on the subseasonal time scale has mostly focused on observed relationships with teleconnections. The skill of numerical weather prediction forecasts of convective-related variables has been comparatively less explored. In this study over the United States, a forecast evaluation of variables relevant in the prediction of severe convective storms is conducted using Global Ensemble Forecast System, version 12, reforecasts at lead times of up to 4 weeks. We find that kinematic and thermodynamic fields are predicted with skill out to week 3 in some cases, while composite parameters struggle to achieve meaningful skill into week 2. Additionally, using a novel method of weekly summations of daily maximum composite parameters, we suggest that the aggregation of certain variables may assist in providing additional predictability beyond week 1. These results should serve as a reference for forecast skill for the relevant fields and help inform the development of convective forecasting tools at time scales beyond current operational products. Significance Statement Prediction of severe weather beyond 1 week in advance is of interest to stakeholders given their impacts on life and property. In this study, we evaluate 20 years of forecast data generated by a numerical model ensemble to determine whether variables relevant in severe weather forecasts can be predicted in weeks 2–4. The variables with the best skill measures generally represent large-scale weather patterns that are more predictable on longer time scales, although some manipulation of other severe weather parameters yielded additional results. We suggest that the results found in this study can inform future work that assesses the predictability of severe weather in weeks 2–4 using more complex methods such as machine learning.