15 May 2023
 | 15 May 2023
Status: this preprint is currently under review for the journal NHESS.

Forecasting Large Hail and Lightning using Additive Logistic Regression Models and the ECMWF Reforecasts

Francesco Battaglioli, Pieter Groenemeijer, Ivan Tsonevsky, and Tomàš Púčik

Abstract. Additive Logistic Regression models for lightning and large hail (ARhail) were developed using convective parameters from the ERA5 reanalysis, hail reports from the European Severe Weather Database (ESWD), and lightning observations from the Met Office Arrival Time Difference network (ATDnet). The model yields the probability of large hail in a given timeframe over a particular grid point and can accurately reproduce the climatological distribution and the seasonal cycle of observed hail events in Europe. To explore the value of this approach to medium-range forecasting, a similar four-dimensional model was developed using predictor parameters retrieved from ECMWF reforecasts: Most Unstable CAPE, 925–500 hPa bulk shear, Mixed Layer Mixing Ratio, and the Wet Bulb Zero Height. This model was applied to the ECMWF reforecasts to compute probabilistic large hail forecasts for all available 11 ensemble members, from 2008 to 2019 and for lead times up to 228 hours. First, we compared the hail ensemble forecasts for different lead times with observed hail occurrence from the ESWD focusing on a recent hail outbreak. Secondly, we evaluated the model’s predictive skill as a function of forecast lead time using the Area under the ROC Curve (AUC) as a validation score. This analysis showed that ARhail has a very high predictive skill (AUC > 0.95) for a lead time up to 60 hours. ARhail retains a high predictive skill even for extended forecasts (AUC = 0.86 at 180 hours lead time). Finally, the performance of the four-dimensional model was compared with that of composite parameters such as the Significant Hail Parameter (SHP) or the product of CAPE and the 925–500 hPa bulk shear (CAPESHEAR). Results show that ARhail outperformed CAPESHEAR at all lead times and SHP at short to medium lead times. This suggests that the combination of Additive Logistic Regression models and ECMWF ensemble forecasts can create highly skilful medium-range hail forecasts for Europe.

Francesco Battaglioli et al.

Status: open (until 26 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Francesco Battaglioli et al.

Data sets

European Severe Weather Database (ESWD) European Severe Storms Laboratory

Reforecasts European Centre for Medium Range Weather Forecasts (ECMWF)

ERA5 European Centre for Medium Range Weather Forecasts (ECMWF)!/dataset/reanalysis-era5-single-levels?tab=overview

Francesco Battaglioli et al.


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Short summary
Probabilistic models for lightning and large hail were developed across Europe using lightning observations and hail reports. These models accurately predict the occurrence of lightning and large hail several days in advance. In addition, the hail model was shown to perform significantly better than the state-of-the-art forecasting methods. These results suggest that the models developed in this study may help improving forecasting of convective hazards and eventually limit the associated risks.