Challenges of developing AR-CHaMo for severe convective wind gust forecasting

ESSL is developing additive logistic regression models (AR-CHaMo) for forecasting large hail, tornadoes, and severe convective wind gusts. Large hail and severe convective wind gust forecasting models applied to the ECMWF ensemble were tested during the 2024 summer severe convective season. The wind model was developed using a subset of severe wind reports from the United States and Europe from all seasons. We found that the severe wind gust model was less skillful than the large hail model. The main issues were too low probabilities in high CAPE and low to moderate vertical wind shear, and too high probabilities in situations characterized by weak elevated instability and strong mean flow in the lower troposphere. 

Using the above-mentioned issues, we outline the main challenges associated with the severe wind development. The first challenge is dealing with the various quality of severe wind reports related to the observed wind intensity in the European and the United States severe weather databases. The second challenge is a large spread of situations that can result in severe convective wind gusts: from disorganised storms producing downbursts, through longer-lived convective systems in high CAPE and moderate to strong shear, to strongly forced narrow convective rainbands forming in marginal CAPE and very strong mean tropospheric flow. We propose a way to deal with these challenges and how to improve the performance of the severe convective wind gust forecasting model. At the same time, we invite a discussion on the topic from the present severe weather research community.