Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts
<p>Additive logistic regression models for lightning (AR<span class="inline-formula"><sub>lig</sub></span>) and large hail (AR<span class="inline-formula"><sub>hail</sub></span>) were developed using convective parameters from...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-11-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/23/3651/2023/nhess-23-3651-2023.pdf |
| _version_ | 1797448448152174592 |
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| author | F. Battaglioli F. Battaglioli P. Groenemeijer P. Groenemeijer I. Tsonevsky T. Púčik |
| author_facet | F. Battaglioli F. Battaglioli P. Groenemeijer P. Groenemeijer I. Tsonevsky T. Púčik |
| author_sort | F. Battaglioli |
| collection | DOAJ |
| description | <p>Additive logistic regression models for lightning (AR<span class="inline-formula"><sub>lig</sub></span>) and large hail (AR<span class="inline-formula"><sub>hail</sub></span>) 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<span class="inline-formula"><sub>hail</sub></span> has a very high predictive skill (AUC <span class="inline-formula">></span> 0.95) for a lead time up to 60 h. AR<span class="inline-formula"><sub>hail</sub></span> retains a high predictive skill even for extended forecasts (AUC <span class="inline-formula">=</span> 0.86 at 180 h lead time). Although AR<span class="inline-formula"><sub>lig</sub></span> exhibits a lower predictive skill than AR<span class="inline-formula"><sub>hail</sub></span>, lightning forecasts are also skilful both in the short term (AUC <span class="inline-formula">=</span> 0.92 at 60 h) and in the medium range (AUC <span class="inline-formula">=</span> 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<span class="inline-formula"><sub>hail</sub></span> 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.</p> |
| first_indexed | 2024-03-09T14:10:34Z |
| format | Article |
| id | doaj.art-218ea57387354dbdb2ed146fa9e45d8e |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-03-09T14:10:34Z |
| publishDate | 2023-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-218ea57387354dbdb2ed146fa9e45d8e2023-11-29T10:01:09ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812023-11-01233651366910.5194/nhess-23-3651-2023Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecastsF. Battaglioli0F. Battaglioli1P. Groenemeijer2P. Groenemeijer3I. Tsonevsky4T. Púčik5European Severe Storms Laboratory e.V. (ESSL), 82234 Wessling, GermanyInstitut für Meteorologie, Freie Univerisität Berlin, 12165 Berlin, GermanyEuropean Severe Storms Laboratory e.V. (ESSL), 82234 Wessling, GermanyEuropean Severe Storms Laboratory (ESSL) – Science and Training, 2700 Wiener Neustadt, AustriaEuropean Centre for Medium Range Weather Forecasts (ECMWF), Reading, RG2 9AX, United KingdomEuropean Severe Storms Laboratory (ESSL) – Science and Training, 2700 Wiener Neustadt, Austria<p>Additive logistic regression models for lightning (AR<span class="inline-formula"><sub>lig</sub></span>) and large hail (AR<span class="inline-formula"><sub>hail</sub></span>) 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<span class="inline-formula"><sub>hail</sub></span> has a very high predictive skill (AUC <span class="inline-formula">></span> 0.95) for a lead time up to 60 h. AR<span class="inline-formula"><sub>hail</sub></span> retains a high predictive skill even for extended forecasts (AUC <span class="inline-formula">=</span> 0.86 at 180 h lead time). Although AR<span class="inline-formula"><sub>lig</sub></span> exhibits a lower predictive skill than AR<span class="inline-formula"><sub>hail</sub></span>, lightning forecasts are also skilful both in the short term (AUC <span class="inline-formula">=</span> 0.92 at 60 h) and in the medium range (AUC <span class="inline-formula">=</span> 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<span class="inline-formula"><sub>hail</sub></span> 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.</p>https://nhess.copernicus.org/articles/23/3651/2023/nhess-23-3651-2023.pdf |
| spellingShingle | F. Battaglioli F. Battaglioli P. Groenemeijer P. Groenemeijer I. Tsonevsky T. Púčik Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts Natural Hazards and Earth System Sciences |
| title | Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts |
| title_full | Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts |
| title_fullStr | Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts |
| title_full_unstemmed | Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts |
| title_short | Forecasting large hail and lightning using additive logistic regression models and the ECMWF reforecasts |
| title_sort | forecasting large hail and lightning using additive logistic regression models and the ecmwf reforecasts |
| url | https://nhess.copernicus.org/articles/23/3651/2023/nhess-23-3651-2023.pdf |
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