GNSS-Based Machine Learning Storm Nowcasting
Nowcasting of severe weather events and summer storms, in particular, are intensively studied as they have great potential for large economic and societal losses. Use of Global Navigation Satellite Systems (GNSS) observations for weather nowcasting has been investigated in various regions. However,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/16/2536 |
| _version_ | 1797559837964369920 |
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| author | Marcelina Łoś Kamil Smolak Guergana Guerova Witold Rohm |
| author_facet | Marcelina Łoś Kamil Smolak Guergana Guerova Witold Rohm |
| author_sort | Marcelina Łoś |
| collection | DOAJ |
| description | Nowcasting of severe weather events and summer storms, in particular, are intensively studied as they have great potential for large economic and societal losses. Use of Global Navigation Satellite Systems (GNSS) observations for weather nowcasting has been investigated in various regions. However, combining the vertically integrated water vapour (IWV) with vertical profiles of wet refractivity derived from GNSS tomography has not been exploited for short-range forecasts of storms. In this study, we introduce a methodology to use the synergy of IWV and tomography-based vertical profiles to predict 0–2 h of storms using a machine learning approach for Poland. Moreover, we present an analysis of the importance of features that take part in the prediction process. The accuracy of the model reached over 87%, and the precision of prediction was about 30%. The results show that wet refractivity below 6 km and IWV on the west of the storm are among the significant parameters with potential for predicting storm location. The analysis of IWV demonstrated a correlation between IWV changes and storm occurrence. |
| first_indexed | 2024-03-10T17:50:57Z |
| format | Article |
| id | doaj.art-7b880960a4924d22a354add8ccbda920 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T17:50:57Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7b880960a4924d22a354add8ccbda9202023-11-20T09:21:36ZengMDPI AGRemote Sensing2072-42922020-08-011216253610.3390/rs12162536GNSS-Based Machine Learning Storm NowcastingMarcelina Łoś0Kamil Smolak1Guergana Guerova2Witold Rohm3Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wrocław, PolandInstitute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wrocław, PolandDepartment Meteorology and Geophysics, Sofia University “St. Kliment Ohridski” Physics Faculty, 1164 Sofia, BulgariaInstitute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wrocław, PolandNowcasting of severe weather events and summer storms, in particular, are intensively studied as they have great potential for large economic and societal losses. Use of Global Navigation Satellite Systems (GNSS) observations for weather nowcasting has been investigated in various regions. However, combining the vertically integrated water vapour (IWV) with vertical profiles of wet refractivity derived from GNSS tomography has not been exploited for short-range forecasts of storms. In this study, we introduce a methodology to use the synergy of IWV and tomography-based vertical profiles to predict 0–2 h of storms using a machine learning approach for Poland. Moreover, we present an analysis of the importance of features that take part in the prediction process. The accuracy of the model reached over 87%, and the precision of prediction was about 30%. The results show that wet refractivity below 6 km and IWV on the west of the storm are among the significant parameters with potential for predicting storm location. The analysis of IWV demonstrated a correlation between IWV changes and storm occurrence.https://www.mdpi.com/2072-4292/12/16/2536storm nowcastingGNSS meteorologyGNSS tomographymachine learningrandom forest |
| spellingShingle | Marcelina Łoś Kamil Smolak Guergana Guerova Witold Rohm GNSS-Based Machine Learning Storm Nowcasting Remote Sensing storm nowcasting GNSS meteorology GNSS tomography machine learning random forest |
| title | GNSS-Based Machine Learning Storm Nowcasting |
| title_full | GNSS-Based Machine Learning Storm Nowcasting |
| title_fullStr | GNSS-Based Machine Learning Storm Nowcasting |
| title_full_unstemmed | GNSS-Based Machine Learning Storm Nowcasting |
| title_short | GNSS-Based Machine Learning Storm Nowcasting |
| title_sort | gnss based machine learning storm nowcasting |
| topic | storm nowcasting GNSS meteorology GNSS tomography machine learning random forest |
| url | https://www.mdpi.com/2072-4292/12/16/2536 |
| work_keys_str_mv | AT marcelinałos gnssbasedmachinelearningstormnowcasting AT kamilsmolak gnssbasedmachinelearningstormnowcasting AT guerganaguerova gnssbasedmachinelearningstormnowcasting AT witoldrohm gnssbasedmachinelearningstormnowcasting |