Abstract
The Warn-on-Forecast System (WoFS) is a regional, rapidly-updating, ensemble data assimilation and prediction system designed to provide short-term probabilistic guidance of severe and hazardous weather, including individual thunderstorms. As with most convection-allowing modeling systems, WoFS occasionally produces forecasts of thunderstorms with storm motion biases, which can be caused by multiple sources of error within the data assimilation and forecast system. The storm motion biases lead to storm displacement errors during the forecasts resulting in increasingly worse forecasts. In this study, we investigate storm displacement errors in WoFS forecasts from cases in 2020–2023 using an object-based technique in a novel way to define and match WoFS and MRMS reflectivity objects. The storm displacement mean absolute errors and location biases are grouped together by various attributes, including year, lead time, ensemble member, MRMS relative storm age, 850–300-hPa mean wind, and MRMS object mean intensity. Results from this investigation reveal storm displacement errors in WoFS forecasts generally have an eastward bias, grow the fastest within the first hour after forecast initialization, and are the smallest 1–3 hours after a thunderstorm has been assimilated. By understanding and characterizing the storm displacement errors, WoFS developers will be able to focus attention on possible error sources and preventative measures to further improve WoFS, and NWS forecasters will be able to mentally account for the storm displacements errors when issuing forecast and warning products.
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