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
17 citations as recorded by crossref.
- Forecasting Precipitation from Radar Wind Profiler Mesonet and Reanalysis Using the Random Forest Algorithm Y. Wu et al. 10.3390/rs15061635
- A spatio-temporal fusion deep learning network with application to lightning nowcasting C. Zhou et al. 10.3233/ICA-240734
- Cloud-to-Ground and Intra-Cloud Nowcasting Lightning Using a Semantic Segmentation Deep Learning Network L. Fan & C. Zhou 10.3390/rs15204981
- Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset G. Song et al. 10.1038/s41612-023-00451-x
- MCGLN: A multimodal ConvLSTM-GAN framework for lightning nowcasting utilizing multi-source spatiotemporal data M. Lu et al. 10.1016/j.atmosres.2023.107093
- Convection Initiation Forecasting Using Synthetic Satellite Imagery from the Warn-on-Forecast System T. Jones & J. Mecikalski 10.15191/nwajom.2023.1110
- Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model N. Sun et al. 10.3390/rs14174256
- A machine‐learning approach to thunderstorm forecasting through post‐processing of simulation data K. Vahid Yousefnia et al. 10.1002/qj.4777
- Validation of INSAT-3D/3DR based nowcasting rain occurrences for heavy rainfall using Hydro-Estimator product N. Singh et al. 10.1016/j.asr.2023.05.030
- Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. 10.1016/j.atmosres.2024.107380
- Interpretation of black box for short-term predictions of pre-monsoon cumulonimbus cloud events over Kolkata D. Dutta & S. Pal 10.1007/s42488-022-00071-9
- Application of Deep Neural Networks for Detecting Probable Areas of Precipitation and Thunderstorms V. Chursin & A. Kostornaya 10.3103/S1068373924040058
- Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey V. Yavuz 10.1007/s00024-024-03521-0
- Improved forecasting via physics-guided machine learning as exemplified using “21·7” extreme rainfall event in Henan Q. Zhong et al. 10.1007/s11430-022-1302-1
- Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model J. Leinonen et al. 10.1029/2022GL101626
- Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance J. Leinonen et al. 10.5194/nhess-22-577-2022
- Towards nowcasting in Europe in 2030 S. Bojinski et al. 10.1002/met.2124
13 citations as recorded by crossref.
- Forecasting Precipitation from Radar Wind Profiler Mesonet and Reanalysis Using the Random Forest Algorithm Y. Wu et al. 10.3390/rs15061635
- A spatio-temporal fusion deep learning network with application to lightning nowcasting C. Zhou et al. 10.3233/ICA-240734
- Cloud-to-Ground and Intra-Cloud Nowcasting Lightning Using a Semantic Segmentation Deep Learning Network L. Fan & C. Zhou 10.3390/rs15204981
- Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset G. Song et al. 10.1038/s41612-023-00451-x
- MCGLN: A multimodal ConvLSTM-GAN framework for lightning nowcasting utilizing multi-source spatiotemporal data M. Lu et al. 10.1016/j.atmosres.2023.107093
- Convection Initiation Forecasting Using Synthetic Satellite Imagery from the Warn-on-Forecast System T. Jones & J. Mecikalski 10.15191/nwajom.2023.1110
- Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model N. Sun et al. 10.3390/rs14174256
- A machine‐learning approach to thunderstorm forecasting through post‐processing of simulation data K. Vahid Yousefnia et al. 10.1002/qj.4777
- Validation of INSAT-3D/3DR based nowcasting rain occurrences for heavy rainfall using Hydro-Estimator product N. Singh et al. 10.1016/j.asr.2023.05.030
- Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. 10.1016/j.atmosres.2024.107380
- Interpretation of black box for short-term predictions of pre-monsoon cumulonimbus cloud events over Kolkata D. Dutta & S. Pal 10.1007/s42488-022-00071-9
- Application of Deep Neural Networks for Detecting Probable Areas of Precipitation and Thunderstorms V. Chursin & A. Kostornaya 10.3103/S1068373924040058
- Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey V. Yavuz 10.1007/s00024-024-03521-0
4 citations as recorded by crossref.
- Improved forecasting via physics-guided machine learning as exemplified using “21·7” extreme rainfall event in Henan Q. Zhong et al. 10.1007/s11430-022-1302-1
- Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model J. Leinonen et al. 10.1029/2022GL101626
- Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance J. Leinonen et al. 10.5194/nhess-22-577-2022
- Towards nowcasting in Europe in 2030 S. Bojinski et al. 10.1002/met.2124
Latest update: 13 Dec 2024
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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