We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts.
- Authors
Battaglioli, Francesco; Groenemeijer, Pieter; Tsonevsky, Ivan; Púčik, Tomàš
- Abstract
Additive logistic regression models for lightning (AR lig) and large hail (AR hail) 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 models yield the probability of lightning and large hail in a given timeframe over a particular grid point. To explore the value of this approach to medium-range forecasting, the models were applied to the European Centre for Medium Range Weather Forecasts (ECMWF) reforecasts to reconstruct probabilistic lightning and large hail forecasts for 11 ensemble members, from 2008 to 2019 and for lead times up to 228 h. The lightning and large hail models were based on different predictor parameters: most unstable convective available potential energy (CAPE), 925–500 hPa bulk shear, mixed layer mixing ratio, wet bulb zero height (for large hail), most unstable lifted index, mean relative humidity between 850 and 500 hPa, 1 hourly accumulated convective precipitation and specific humidity at 925 hPa (for lightning). First, we compared the lightning and hail ensemble forecasts for different lead times with observed lightning and hail focusing on a recent hail outbreak. Second, we evaluated the predictive skill of the model as a function of forecast lead time using the area under the ROC curve (AUC) as a validation score. This analysis showed that AR hail has a very high predictive skill (AUC > 0.95) for a lead time up to 60 h. AR hail retains a high predictive skill even for extended forecasts (AUC = 0.86 at 180 h lead time). Although AR lig exhibits a lower predictive skill than AR hail , lightning forecasts are also skilful both in the short term (AUC = 0.92 at 60 h) and in the medium range (AUC = 0.82 at 180 h). Finally, we compared the performance of the 4-dimensional hail model 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 AR hail outperforms CAPESHEAR at all lead times and SHP at short-to-medium lead times. These findings suggests that the combination of additive logistic regression models and ECMWF ensemble forecasts can create highly skilful medium-range hail and lightning forecasts for Europe.
- Subjects
GREAT Britain. Meteorological Office; HAIL; HAILSTORMS; REGRESSION analysis; LOGISTIC regression analysis; LIGHTNING; LEAD time (Supply chain management); FORECASTING
- Publication
Natural Hazards & Earth System Sciences, 2023, Vol 23, Issue 12, p3651
- ISSN
1561-8633
- Publication type
Article
- DOI
10.5194/nhess-23-3651-2023