Identifying predictors of large hail, severe convective wind gusts, and tornadoes across Europe and North America: towards the development of global convective hazard models.

The development of additive logistic regression models (AR-CHaMo) for large hail, severe convective wind gusts, and F1 or stronger tornadoes for Europe and parts of North America allowed us to identify how the best predictors vary among different threats and different forecast domains. The best predictors were identified using the variance explained, based on the skill of logistic models for individual parameters as well as on investigating pairs of different parameters and their relation to hazard frequency.

For the models, we have chosen predictors that perform well over both domains and could thus be used to develop a global convective hazard model. In the case of large hail, CAPE was found to be a better predictor across Europe than across North America, where mid-tropospheric lapse rates discriminate better between environments with and without large hail. We found that CAPE below the -10 °C level was a skillful predictor in both domains. For severe convective wind gusts, it was found that they occurred with lower CAPE and lower amounts of absolute moisture in Europe than in North America. Height of the LCL or a parameter that predicts the cold pool strength worked better in Europe than in North America. Strong mean wind in the bottom troposphere was found among the best predictors of severe wind gusts in both domains. Regional differences among the best predictors were also found for F1 and stronger tornadoes, even though the amount of SRH in the lower troposphere is universally a skillful predictor.

We applied models using the best predictors of large hail across North America and Europe to the ERA-5 reanalysis to obtain a global model of large hail hazard. Then, we compare the model to existing hail climatologies worldwide and discuss its limitations and potential improvements.