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Open AccessArticle

GNSS-Based Machine Learning Storm Nowcasting

1
Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wrocław, Poland
2
Department Meteorology and Geophysics, Sofia University “St. Kliment Ohridski” Physics Faculty, 1164 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2536; https://doi.org/10.3390/rs12162536
Received: 20 June 2020 / Revised: 22 July 2020 / Accepted: 4 August 2020 / Published: 6 August 2020
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. View Full-Text
Keywords: storm nowcasting; GNSS meteorology; GNSS tomography; machine learning; random forest storm nowcasting; GNSS meteorology; GNSS tomography; machine learning; random forest
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MDPI and ACS Style

Łoś, M.; Smolak, K.; Guerova, G.; Rohm, W. GNSS-Based Machine Learning Storm Nowcasting. Remote Sens. 2020, 12, 2536.

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