Articles | Volume 22, issue 2
https://doi.org/10.5194/nhess-22-577-2022
© Author(s) 2022. 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-22-577-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Feb 2022
Research article |
| 25 Feb 2022
| 25 Feb 2022
Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance
Jussi Leinonen
CORRESPONDING AUTHOR
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Ulrich Hamann
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Urs Germann
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
John R. Mecikalski
Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama, USA
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Cited
28 citations as recorded by crossref.
- Forecasting Precipitation from Radar Wind Profiler Mesonet and Reanalysis Using the Random Forest Algorithm Y. Wu et al. https://doi.org/10.3390/rs15061635
- Multi-Objective Optimization for Lightning Protection in Distribution Networks: A Novel Approach Based on Design of Experiments N. Ravichandran et al. https://doi.org/10.1109/ACCESS.2025.3546490
- Lightning nowcasting based on high-density area and extrapolation utilizing long-range lightning location data Y. Liu et al. https://doi.org/10.1016/j.atmosres.2025.108070
- An AI Training Dataset for Thunderstorm Monitoring and Forecasting over China N. Liu et al. https://doi.org/10.3390/rs18050724
- Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model N. Sun et al. https://doi.org/10.3390/rs14174256
- A machine‐learning approach to thunderstorm forecasting through post‐processing of simulation data K. Vahid Yousefnia et al. https://doi.org/10.1002/qj.4777
- ST-GLNet: A Spatiotemporal Graph Learning Network for Accurate Lightning Prediction Using Multimodal Meteorological Data J. Wang et al. https://doi.org/10.1109/TGRS.2026.3689475
- Validation of INSAT-3D/3DR based nowcasting rain occurrences for heavy rainfall using Hydro-Estimator product N. Singh et al. https://doi.org/10.1016/j.asr.2023.05.030
- Improving prediction of short-duration heavy rainfall in Guangxi, China during the pre-summer rainy season based on Fengyun-4A lightning frequency and a machine learning algorithm W. Huang et al. https://doi.org/10.3389/feart.2025.1669417
- Predicting thunderstorm risk probability at very short time range using deep learning M. Bosc et al. https://doi.org/10.5194/nhess-26-1603-2026
- Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. https://doi.org/10.1016/j.atmosres.2024.107380
- Application of Deep Neural Networks for Detecting Probable Areas of Precipitation and Thunderstorms V. Chursin & A. Kostornaya https://doi.org/10.3103/S1068373924040058
- A Gaussian Process Regression Method to Nowcast Cloud-to-Ground Lightning From Remote Sensing and Numerical Weather Modeling Data A. La Fata et al. https://doi.org/10.1109/JSTARS.2024.3501976
- Contribution Disparity and Key Factor Screening of Lightning Identification and Nowcasting with Multi-Source Data X. Wang et al. https://doi.org/10.3390/rs18142383
- A spatio-temporal fusion deep learning network with application to lightning nowcasting C. Zhou et al. https://doi.org/10.3233/ICA-240734
- Cloud-to-Ground and Intra-Cloud Nowcasting Lightning Using a Semantic Segmentation Deep Learning Network L. Fan & C. Zhou https://doi.org/10.3390/rs15204981
- Thundercloud assessment for the years 1990–2019 over the Baghdad airport station I. Al- Khulaifawi & A. Mutar https://doi.org/10.1007/s00704-026-06187-x
- Insights into thunderstorm characteristics from geostationary lightning jump and dive observations F. Erdmann & D. Poelman https://doi.org/10.5194/nhess-25-1751-2025
- Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset G. Song et al. https://doi.org/10.1038/s41612-023-00451-x
- MCGLN: A multimodal ConvLSTM-GAN framework for lightning nowcasting utilizing multi-source spatiotemporal data M. Lu et al. https://doi.org/10.1016/j.atmosres.2023.107093
- Convection Initiation Forecasting Using Synthetic Satellite Imagery from the Warn-on-Forecast System T. Jones & J. Mecikalski https://doi.org/10.15191/nwajom.2023.1110
- Advanced rainfall nowcasting using 3D convolutional LSTM networks on satellite data A. Upadhyay et al. https://doi.org/10.1016/j.jcmds.2025.100125
- Land surface information from satellites boost near-surface temperature forecast skill M. Ruiz-Vásquez et al. https://doi.org/10.1038/s43247-026-03298-1
- A machine learning model for the prediction of hail-affected area in Germany S. Li et al. https://doi.org/10.3389/feart.2025.1527391
- Lightning Prediction under Uncertainty: DeepLight with Hazy Loss M. Arifin et al. https://doi.org/10.1016/j.eswa.2026.131586
- Multimodal Remote Sensing of Thunderstorm Charge Motion: A Radar Echo and Electric Field Fusion Approach X. Yang et al. https://doi.org/10.1109/TGRS.2025.3627303
- Interpretation of black box for short-term predictions of pre-monsoon cumulonimbus cloud events over Kolkata D. Dutta & S. Pal https://doi.org/10.1007/s42488-022-00071-9
- Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey V. Yavuz https://doi.org/10.1007/s00024-024-03521-0
28 citations as recorded by crossref.
- Forecasting Precipitation from Radar Wind Profiler Mesonet and Reanalysis Using the Random Forest Algorithm Y. Wu et al. https://doi.org/10.3390/rs15061635
- Multi-Objective Optimization for Lightning Protection in Distribution Networks: A Novel Approach Based on Design of Experiments N. Ravichandran et al. https://doi.org/10.1109/ACCESS.2025.3546490
- Lightning nowcasting based on high-density area and extrapolation utilizing long-range lightning location data Y. Liu et al. https://doi.org/10.1016/j.atmosres.2025.108070
- An AI Training Dataset for Thunderstorm Monitoring and Forecasting over China N. Liu et al. https://doi.org/10.3390/rs18050724
- Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model N. Sun et al. https://doi.org/10.3390/rs14174256
- A machine‐learning approach to thunderstorm forecasting through post‐processing of simulation data K. Vahid Yousefnia et al. https://doi.org/10.1002/qj.4777
- ST-GLNet: A Spatiotemporal Graph Learning Network for Accurate Lightning Prediction Using Multimodal Meteorological Data J. Wang et al. https://doi.org/10.1109/TGRS.2026.3689475
- Validation of INSAT-3D/3DR based nowcasting rain occurrences for heavy rainfall using Hydro-Estimator product N. Singh et al. https://doi.org/10.1016/j.asr.2023.05.030
- Improving prediction of short-duration heavy rainfall in Guangxi, China during the pre-summer rainy season based on Fengyun-4A lightning frequency and a machine learning algorithm W. Huang et al. https://doi.org/10.3389/feart.2025.1669417
- Predicting thunderstorm risk probability at very short time range using deep learning M. Bosc et al. https://doi.org/10.5194/nhess-26-1603-2026
- Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. https://doi.org/10.1016/j.atmosres.2024.107380
- Application of Deep Neural Networks for Detecting Probable Areas of Precipitation and Thunderstorms V. Chursin & A. Kostornaya https://doi.org/10.3103/S1068373924040058
- A Gaussian Process Regression Method to Nowcast Cloud-to-Ground Lightning From Remote Sensing and Numerical Weather Modeling Data A. La Fata et al. https://doi.org/10.1109/JSTARS.2024.3501976
- Contribution Disparity and Key Factor Screening of Lightning Identification and Nowcasting with Multi-Source Data X. Wang et al. https://doi.org/10.3390/rs18142383
- A spatio-temporal fusion deep learning network with application to lightning nowcasting C. Zhou et al. https://doi.org/10.3233/ICA-240734
- Cloud-to-Ground and Intra-Cloud Nowcasting Lightning Using a Semantic Segmentation Deep Learning Network L. Fan & C. Zhou https://doi.org/10.3390/rs15204981
- Thundercloud assessment for the years 1990–2019 over the Baghdad airport station I. Al- Khulaifawi & A. Mutar https://doi.org/10.1007/s00704-026-06187-x
- Insights into thunderstorm characteristics from geostationary lightning jump and dive observations F. Erdmann & D. Poelman https://doi.org/10.5194/nhess-25-1751-2025
- Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset G. Song et al. https://doi.org/10.1038/s41612-023-00451-x
- MCGLN: A multimodal ConvLSTM-GAN framework for lightning nowcasting utilizing multi-source spatiotemporal data M. Lu et al. https://doi.org/10.1016/j.atmosres.2023.107093
- Convection Initiation Forecasting Using Synthetic Satellite Imagery from the Warn-on-Forecast System T. Jones & J. Mecikalski https://doi.org/10.15191/nwajom.2023.1110
- Advanced rainfall nowcasting using 3D convolutional LSTM networks on satellite data A. Upadhyay et al. https://doi.org/10.1016/j.jcmds.2025.100125
- Land surface information from satellites boost near-surface temperature forecast skill M. Ruiz-Vásquez et al. https://doi.org/10.1038/s43247-026-03298-1
- A machine learning model for the prediction of hail-affected area in Germany S. Li et al. https://doi.org/10.3389/feart.2025.1527391
- Lightning Prediction under Uncertainty: DeepLight with Hazy Loss M. Arifin et al. https://doi.org/10.1016/j.eswa.2026.131586
- Multimodal Remote Sensing of Thunderstorm Charge Motion: A Radar Echo and Electric Field Fusion Approach X. Yang et al. https://doi.org/10.1109/TGRS.2025.3627303
- Interpretation of black box for short-term predictions of pre-monsoon cumulonimbus cloud events over Kolkata D. Dutta & S. Pal https://doi.org/10.1007/s42488-022-00071-9
- Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey V. Yavuz https://doi.org/10.1007/s00024-024-03521-0
Saved (final revised paper)
Latest update: 11 Aug 2026
Short summary
We evaluate the usefulness of different data sources and variables to the short-term prediction (
nowcasting) of severe thunderstorms using machine learning. Machine-learning models are trained with data from weather radars, satellite images, lightning detection and weather forecasts and with terrain elevation data. We analyze the benefits provided by each of the data sources to predicting hazards (heavy precipitation, lightning and hail) caused by the thunderstorms.
We evaluate the usefulness of different data sources and variables to the short-term prediction...
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