EGU22-7214, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Forecasting Large Hail Using Logistic Models and the ECMWF Ensemble Prediction System

Francesco Battaglioli1, Pieter Groenemeijer1, and Ivan Tsonesvky2
Francesco Battaglioli et al.
  • 1European Severe Storms Laboratory, Wessling, Germany (
  • 2European Center for Medium Range Weather Forecasts, Reading, UK (

An additive logistic regression model for large hail was developed based on convective parameters from ERA5 reanalysis, severe weather reports from the European Severe Weather Database (ESWD), and lightning observations from the Met Office Arrival Time Difference network (ATDnet). This model was shown to accurately reproduce the spatial distribution and the seasonal cycle of observed hail events in Europe. A spatial map of the modelled mean distribution for hail > 2 cm will be presented.

To explore the value of this approach to medium-range forecasting, a similar statistical model was developed using four predictor parameters available from the ECMWF Ensemble Prediction System (EPS) reforecasts: Mixed Layer CAPE, Deep Layer Shear, Mixed Layer Mixing Ratio and the Wet Bulb Zero Height. Probabilistic large hail predictions were created for all available 11-member ensemble forecasts (2008 to 2019), for lead times from 12 to 228 hours.

First, we evaluated the model’s predictive skill depending on the forecast lead time using the Area Under the ROC Curve (AUC) as a validation score. For forecasts up to two to three days, the model highlights a very high predictive skill (AUC > 0.95). Furthermore, the model retains a high predictive skill even for extended forecasts (AUC = 0.85 at 180 hours lead time) showing that it can identify regions with hail potential well in advance. Second, we compared the forecast spatial probabilities at various lead times with observed hail occurrence focusing on a few recent hail outbreaks. Finally, our four-dimensional model was compared with logistic models based on composite parameters such as the Significant Hail Parameter (SHP) and the product of CAPE and Deep Layer Shear (CAPESHEAR). The four-dimensional model outperformed these composite-based ones at lead times up to four days. The high AUC scores show that this model could improve short-medium range hail forecasts. Preliminary application of this approach to other convective hazards such as convective wind gusts will be presented as well.

How to cite: Battaglioli, F., Groenemeijer, P., and Tsonesvky, I.: Forecasting Large Hail Using Logistic Models and the ECMWF Ensemble Prediction System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7214,, 2022.