Feature-Relative Forecast Evaluation for Convective Systems

Traditionally, spatial forecast verification has been performed in an Eulerian sense, as features move through a fixed geographical region, which helps to identify errors in the forecast for specific geographic areas. However, forecasters and model developers alike may gain valuable insight by knowing the spatial error patterns relative to a specific weather feature, such as convective systems. In this work, forecasts taken from data assimilation experiments will be analyzed to determine the forecast errors relative to convective weather systems from recent field programs such as MPEX and VORTEX-SE. This approach will attempt to measure the performance of convective mode/morphology predictions using feature-relative grids which follow specific convective systems. Error fields of environmental conditions surrounding convective systems can be produced and tested for statistical significance. Preliminary results from this ongoing work will be presented at the conference.