Articles | Volume 20, issue 9
https://doi.org/10.5194/nhess-20-2463-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-2463-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
16 Sep 2020
Review article |
| 16 Sep 2020
| 16 Sep 2020
Review article: A comprehensive review of datasets and methodologies employed to produce thunderstorm climatologies
School of the Environment, Geography and Geosciences, University of Portsmouth, Burnaby Building, Burnaby Road, Portsmouth, PO1 3QL, United Kingdom
Malcolm Whitworth
School of the Environment, Geography and Geosciences, University of Portsmouth, Burnaby Building, Burnaby Road, Portsmouth, PO1 3QL, United Kingdom
Nick Pepin
School of the Environment, Geography and Geosciences, University of Portsmouth, Burnaby Building, Burnaby Road, Portsmouth, PO1 3QL, United Kingdom
Steve Dorling
School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, United Kingdom
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Total article views: 5,664 (including HTML, PDF, and XML)
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Total article views: 664 (including HTML, PDF, and XML)
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Cited
17 citations as recorded by crossref.
- A New Thunderstorm Identification Algorithm Based on Total Lightning Activity Y. Huang et al. https://doi.org/10.1029/2021EA002079
- A Multi-Year Investigation of Thunderstorm Activity at Istanbul International Airport Using Atmospheric Stability Indices O. Kolay et al. https://doi.org/10.3390/atmos16040470
- Application of lightning spatio-temporal localization method based on deep LSTM and interpolation R. Bao et al. https://doi.org/10.1016/j.measurement.2021.110549
- Statistical Characteristics of Thunderstorm Activity in the Middle Reaches of the Yangtze River Basin Based on a Five‐Year Cloud‐To‐Ground Lighting Data Set Q. Wei et al. https://doi.org/10.1029/2023GL106498
- An Assessment of Atmospheric Convection Trends Over India Using Radiosonde Derived CAPE and Other Proxies P. Ojha et al. https://doi.org/10.1002/joc.8917
- Unveiling India’s lightning patterns through satellite-based climatology and numerical modelling of severe weather events: A review U. Mondal et al. https://doi.org/10.1007/s12040-024-02491-0
- Environments conducive to cloud-to-ground and ignited lightning in a boreal forest of Northeast China Y. Jiang et al. https://doi.org/10.1016/j.atmosres.2026.108919
- Research on Lightning Prediction Based on GCN-LSTM Model Integrating Spatiotemporal Features W. Zhou et al. https://doi.org/10.3390/atmos16040447
- Atmospheric conditions conducive to thunderstorms with downbursts in Canada and a downburst precursor parameter M. Hadavi & D. Romanic https://doi.org/10.1016/j.atmosres.2024.107428
- Improving lightning identification from satellite imagery with a mult-task pseudo-labeling framework and masked attention J. Li et al. https://doi.org/10.1016/j.atmosres.2026.109041
- Assessing Spatial Accuracy of Lightning Forecasts Over India: Supporting Impact‐Based Forecasting for Vulnerable Regions H. Singh et al. https://doi.org/10.1002/met.70106
- CanCPLD: Convective Parameters and Lightning Data to Support Future Thunderstorm Projections in North America A. Cannon et al. https://doi.org/10.1038/s41597-025-05924-7
- The Urban Lightning Effect Revealed With Geostationary Lightning Mapper Observations J. Burke & M. Shepherd https://doi.org/10.1029/2022GL102272
- An Artificial Neural Network for Lightning Prediction Based on Atmospheric Electric Field Observations R. Bao et al. https://doi.org/10.3390/rs14174131
- Performance of a novel NWP–AI hybrid lightning early warning system over Indian Subcontinent R. Sarkar et al. https://doi.org/10.1007/s12040-024-02470-5
- A New Approach for the Analysis of Deep Convective Events: Thunderstorm Intensity Index D. Jelić et al. https://doi.org/10.3390/atmos12070908
- Variation of Lightning over the Bulgarian Black Sea Coast S. Petrova et al. https://doi.org/10.1051/epjconf/202636504005
17 citations as recorded by crossref.
- A New Thunderstorm Identification Algorithm Based on Total Lightning Activity Y. Huang et al. https://doi.org/10.1029/2021EA002079
- A Multi-Year Investigation of Thunderstorm Activity at Istanbul International Airport Using Atmospheric Stability Indices O. Kolay et al. https://doi.org/10.3390/atmos16040470
- Application of lightning spatio-temporal localization method based on deep LSTM and interpolation R. Bao et al. https://doi.org/10.1016/j.measurement.2021.110549
- Statistical Characteristics of Thunderstorm Activity in the Middle Reaches of the Yangtze River Basin Based on a Five‐Year Cloud‐To‐Ground Lighting Data Set Q. Wei et al. https://doi.org/10.1029/2023GL106498
- An Assessment of Atmospheric Convection Trends Over India Using Radiosonde Derived CAPE and Other Proxies P. Ojha et al. https://doi.org/10.1002/joc.8917
- Unveiling India’s lightning patterns through satellite-based climatology and numerical modelling of severe weather events: A review U. Mondal et al. https://doi.org/10.1007/s12040-024-02491-0
- Environments conducive to cloud-to-ground and ignited lightning in a boreal forest of Northeast China Y. Jiang et al. https://doi.org/10.1016/j.atmosres.2026.108919
- Research on Lightning Prediction Based on GCN-LSTM Model Integrating Spatiotemporal Features W. Zhou et al. https://doi.org/10.3390/atmos16040447
- Atmospheric conditions conducive to thunderstorms with downbursts in Canada and a downburst precursor parameter M. Hadavi & D. Romanic https://doi.org/10.1016/j.atmosres.2024.107428
- Improving lightning identification from satellite imagery with a mult-task pseudo-labeling framework and masked attention J. Li et al. https://doi.org/10.1016/j.atmosres.2026.109041
- Assessing Spatial Accuracy of Lightning Forecasts Over India: Supporting Impact‐Based Forecasting for Vulnerable Regions H. Singh et al. https://doi.org/10.1002/met.70106
- CanCPLD: Convective Parameters and Lightning Data to Support Future Thunderstorm Projections in North America A. Cannon et al. https://doi.org/10.1038/s41597-025-05924-7
- The Urban Lightning Effect Revealed With Geostationary Lightning Mapper Observations J. Burke & M. Shepherd https://doi.org/10.1029/2022GL102272
- An Artificial Neural Network for Lightning Prediction Based on Atmospheric Electric Field Observations R. Bao et al. https://doi.org/10.3390/rs14174131
- Performance of a novel NWP–AI hybrid lightning early warning system over Indian Subcontinent R. Sarkar et al. https://doi.org/10.1007/s12040-024-02470-5
- A New Approach for the Analysis of Deep Convective Events: Thunderstorm Intensity Index D. Jelić et al. https://doi.org/10.3390/atmos12070908
- Variation of Lightning over the Bulgarian Black Sea Coast S. Petrova et al. https://doi.org/10.1051/epjconf/202636504005
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
This review article outlines the state of thunderstorm climatologies, which are underrepresented in the literature. Thunderstorms overlap with lightning and intense precipitation events, both of which create important hazards. This article compiles and evaluates information on datasets, research approaches and methodologies used in quantifying thunderstorm distribution, providing an introduction to the topic and signposting new and established researchers to research articles and datasets.
This review article outlines the state of thunderstorm climatologies, which are underrepresented...
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