GNSS-Based Machine Learning Storm Nowcasting
Marcelina Łoś , Kamil Smolak , Guergana Guerova , Witold Rohm
AbstractNowcasting 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.
|Journal series||Remote Sensing, ISSN 2072-4292, (N/A 100 pkt)|
|Publication size in sheets||0.3|
|Keywords in English||storm nowcasting; GNSS meteorology; GNSS tomography; machine learning; random forest|
|License||Journal (articles only); published final; ; after publication|
|Score||= 100.0, 20-04-2021, ArticleFromJournal|
|Publication indicators||= 1; = 1; : 2017 = 1.559; : 2018 = 4.118 (2) - 2018=4.74 (5)|
|Citation count*||2 (2021-05-11)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.