Articles | Volume 23, issue 12
https://doi.org/10.5194/nhess-23-3651-2023
https://doi.org/10.5194/nhess-23-3651-2023
Research article
 | 
29 Nov 2023
Research article |  | 29 Nov 2023

Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts

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

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Cited articles

Adams-Selin, R. and Ziegler, C. L.: Forecasting Hail Using a One-Dimensional Hail Growth Model within WRF, Mon. Weather Rev., 144, 4919–4939, https://doi.org/10.1175/MWR-D-16-0027.1, 2016. 
Allen, J., Giammanco, I., Kumjian, M., Jurgen Punge, H., Zhang Q., Groenemeijer, P., Kunz, M., and Ortega, K.: Understanding Hail in the Earth System, Rev. Geophys., 58, e2019RG000665, https://doi.org/10.1029/2019RG000665, 2020. 
Anderson, G. and Klugmann, D.: A European lightning density analysis using 5 years of ATDnet data, Nat. Hazards Earth Syst. Sci., 14, 815–829, https://doi.org/10.5194/nhess-14-815-2014, 2014. 
Bang, S. and Cecil, D.: Constructing a Multifrequency Passive Microwave Hail Retrieval and Climatology in the GPM Domain, J. Appl. Meteorol. Clim., 58, 1889–1904, https://doi.org/10.1175/JAMC-D-19-0042.1, 2019. 
Battaglioli, F., Groenemeijer, P., Púčik, T., Taszarek, M., Ulbrich, U., and Rust, H.: Modeled Multidecadal Trends of Lightning and (Very) Large Hail in Europe and North America (1950–2021), J. Appl. Meteorol. Clim., 62, 1627–1653, https://doi.org/10.1175/JAMC-D-22-0195.1, 2023. 
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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 improve forecasting of convective hazards and eventually limit the associated risks.
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