{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:17:35Z","timestamp":1780633055902,"version":"3.54.1"},"reference-count":52,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T00:00:00Z","timestamp":1596672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["UMO-2015\/17\/B\/ST10\/03827"],"award-info":[{"award-number":["UMO-2015\/17\/B\/ST10\/03827"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u20132 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.<\/jats:p>","DOI":"10.3390\/rs12162536","type":"journal-article","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T09:30:54Z","timestamp":1596792654000},"page":"2536","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["GNSS-Based Machine Learning Storm Nowcasting"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9057-6051","authenticated-orcid":false,"given":"Marcelina","family":"\u0141o\u015b","sequence":"first","affiliation":[{"name":"Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9113-6090","authenticated-orcid":false,"given":"Kamil","family":"Smolak","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5638-3387","authenticated-orcid":false,"given":"Guergana","family":"Guerova","sequence":"additional","affiliation":[{"name":"Department Meteorology and Geophysics, Sofia University \u201cSt. Kliment Ohridski\u201d Physics Faculty, 1164 Sofia, Bulgaria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Witold","family":"Rohm","sequence":"additional","affiliation":[{"name":"Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka 53, 50-357 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,6]]},"reference":[{"key":"ref_1","unstructured":"Wang, Y., Coning, E., Harou, A., Jacobs, W., Joe, P., Nikitina, L., Roberts, R., Wang, J., Wilson, J., and Atencia, A. (2017). Guidelines for Nowcasting Techniques, World Meteorological Organization (WMO). Number 1198."},{"key":"ref_2","first-page":"802","article-title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","volume":"2015","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1029\/RG027i003p00345","article-title":"Nowcasting of precipitation systems","volume":"27","author":"Browning","year":"1989","journal-title":"Rev. Geophys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5385","DOI":"10.5194\/amt-9-5385-2016","article-title":"Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe","volume":"9","author":"Guerova","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2989","DOI":"10.5194\/amt-9-2989-2016","article-title":"Benchmark campaign and case study episode in central Europe for development and assessment of advanced GNSS tropospheric models and products","volume":"9","author":"Dick","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_6","unstructured":"V\u00e1clavovic, P., Dou\u0161a, J., and Teferle, F. (2020). GNSS Real-Time PPP Demonstration Campaign, Springer."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.5194\/nhess-15-2605-2015","article-title":"On the inclusion of GPS precipitable water vapour in the nowcasting of rainfall","volume":"15","author":"Benevides","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12465","DOI":"10.1038\/s41598-017-12593-z","article-title":"Establishing a method of short-term rainfall forecasting based on GNSS-derived PWV and its application","volume":"7","author":"Yao","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Benevides, P., Catalao, J., and Nico, G. (2019). Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors. Remote Sens., 11.","DOI":"10.3390\/rs11080966"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1175\/1520-0477(1998)079<2079:NTASR>2.0.CO;2","article-title":"Nowcasting Thunderstorms: A Status Report","volume":"79","author":"Wilson","year":"1998","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cooper, M.A., and Holle, R.L. (2019). Reducing Lightning Injuries Worldwide, Springer. Number June.","DOI":"10.1007\/978-3-319-77563-0"},{"key":"ref_12","first-page":"1","article-title":"Nowcasting the lightning activity in Peninsular Malaysia using the GPS PWV during the 2009 inter-monsoons","volume":"57","author":"Suparta","year":"2014","journal-title":"Ann. Geophys."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guerova, G., Dimitrova, T., and Georgiev, S. (2019). Thunderstorm Classification Functions Based on Instability Indices and GNSS IWV for the Sofia Plain. Remote Sens., 11.","DOI":"10.3390\/rs11242988"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Brenot, H., Rohm, W., Ka\u010dma\u0159\u00edk, M., M\u00f6ller, G., S\u00e1, A., Tonda\u015b, D., Rapant, L., Biondi, R., Manning, T., and Champollion, C. (2020). Cross-Comparison and methodological improvement in GPS tomography. Remote Sens., 12.","DOI":"10.3390\/rs12010030"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Flores, A., Ruffini, G., and Rius, A. (2000). 4D tropospheric tomography using GPS slant wet delays. Ann. Geophys.","DOI":"10.1007\/s00585-000-0223-7"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rohm, W., Zhang, K., and Bosy, J. (2014). Limited constraint, robust Kalman filtering for GNSS troposphere tomography. Atmos. Meas. Tech.","DOI":"10.5194\/amtd-6-9133-2013"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kak, A.C., Slaney, M., and Wang, G. (2002). Principles of Computerized Tomographic Imaging. Med. Phys.","DOI":"10.1137\/1.9780898719277"},{"key":"ref_18","unstructured":"Manning, T., Rohm, W., Zhang, K., Hurter, F., and Wang, C. (July, January 28). Determining the 4D dynamics of wet refractivity using GPS tomography in the Australian region. Proceedings of the IAG General Assembly, Melbourne, Australia."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, K., Manning, T., Wu, S., Rohm, W., Silcock, D., and Choy, S. (2015). Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/JSTARS.2015.2406313"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, P., Ye, S.R., Liu, Y.Y., Zhang, J.J., and Xia, P.F. (2014). Near real-time water vapor tomography using ground-based GPS and meteorological data: Long-term experiment in Hong Kong. Ann. Geophys.","DOI":"10.5194\/angeo-32-911-2014"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Trzcina, E., and Rohm, W. (2019). Estimation of 3D wet refractivity by tomography, combining GNSS and NWP data: First results from assimilation of wet refractivity into NWP. Q. J. R. Meteorol. Soc.","DOI":"10.1002\/qj.3475"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hanna, N., Trzcina, E., M\u00f6ller, G., Rohm, W., and Weber, R. (2019). Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator. Atmos. Meas. Tech.","DOI":"10.5194\/amt-12-4829-2019"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4038","DOI":"10.1002\/2016JD025783","article-title":"A machine learning nowcasting method based on real-time reanalysis data","volume":"122","author":"Han","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, W., Han, L., Sun, J., Guo, H., and Dai, J. (2019, January 9\u201312). Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005568"},{"key":"ref_25","unstructured":"Veillette, M.S., Iskenderian, H., Lamey, P.M., and Bickmeier, L.J. (2013, January 9). Convective Initiation Forecasts Through the Use of Machine Learning Methods. Proceedings of the 16th Conference on Aviation, Range, and Aerospace Meteorology, Austin, TX, USA."},{"key":"ref_26","first-page":"91","article-title":"Storms prediction: Logistic regression vs random forest for unbalanced data","volume":"1","author":"Ruiz","year":"2007","journal-title":"Case Stud. Bus. Ind. Gov. Stat."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"708805","DOI":"10.1117\/12.795737","article-title":"Combining observations and model data for short-term storm forecasting","volume":"Volume 7088","author":"Feltz","year":"2008","journal-title":"Remote Sensing Applications for Aviation Weather Hazard Detection and Decision Support"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1175\/WAF-D-15-0113.1","article-title":"Probabilistic Forecasts of Mesoscale Convective System Initiation Using the Random Forest Data Mining Technique","volume":"31","author":"Ahijevych","year":"2016","journal-title":"Weather Forecast."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3","DOI":"10.26491\/mhwm\/65146","article-title":"An assessment of the quality of near-real time GNSS observations as a potential data source for meteorology","volume":"5","author":"Dymarska","year":"2017","journal-title":"Meteorol. Hydrol. Water Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1127\/0941-2948\/2013\/0444","article-title":"Application and evaluation of the WRF model for high-resolution forecasting of rainfall\u2014A case study of SW Poland","volume":"22","author":"Kryza","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Emardson, T.R. (2000). The systematic behavior of water vapor estimates using four years of GPS observations. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/36.823927"},{"key":"ref_32","unstructured":"Dach, R., Lutz, S., Walser, P., and Fridez, P. (2015). Bernese GNSS software version 5.2."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF02521844","article-title":"Contributions to the theory of atmospheric refraction","volume":"105","author":"Saastamoinen","year":"1972","journal-title":"Bull. G\u00e9od\u00e9sique"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"L07304","DOI":"10.1029\/2005GL025546","article-title":"Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data","volume":"33","author":"Boehm","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20489","DOI":"10.1029\/97JB01739","article-title":"Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data","volume":"102","author":"Chen","year":"1997","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107849","DOI":"10.1016\/j.measurement.2020.107849","article-title":"Ultra-fast near real-time estimation of troposphere parameters and coordinates from GPS data","volume":"162","author":"Rohm","year":"2020","journal-title":"Measurement"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1016\/j.asr.2010.10.028","article-title":"Near real-time estimation of tropospheric water vapour content from ground based GNSS data and its potential contribution to weather now-casting in Austria","volume":"47","author":"Weber","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bosy, J., Kaplon, J., Rohm, W., Sierny, J., and Hadas, T. (2012). Near real-time estimation of water vapour in the troposphere using ground GNSS and the meteorological data. Ann. Geophys., 30.","DOI":"10.5194\/angeo-30-1379-2012"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.atmosres.2014.12.011","article-title":"Multi-observation meteorological and GNSS data comparison with Numerical Weather Prediction model","volume":"156","author":"Wilgan","year":"2015","journal-title":"Atmos. Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Boehm, J., Kouba, J., and Schuh, H. (2009). Forecast Vienna mapping functions 1 for real-time analysis of space geodetic observations. J. Geod.","DOI":"10.1007\/s00190-008-0216-y"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shoji, Y. (2013). Retrieval of water vapor inhomogeneity using the japanese nationwide GPS array and its potential for prediction of convective precipitation. J. Meteorol. Soc. Jpn.","DOI":"10.2151\/jmsj.2013-103"},{"key":"ref_42","unstructured":"Wanke, E., Andersen, R., and Volgnandt, T. (2020, April 18). A World-Wide Low-Cost Community-Based Time-of-Arrival Lightning Detection and Lightning Location Network 2014. pp. 1\u201395. Available online: www.blitzortung.org\/Documents\/TOA_Blitzortung_RED.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_45","unstructured":"Geron, A. (2017). Hands-on Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media."},{"key":"ref_46","unstructured":"More, A. (2016). Survey of resampling techniques for improving classification performance in unbalanced datasets. arXiv."},{"key":"ref_47","unstructured":"Chen, C., Liaw, A., and Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data, University of California, Berkeley."},{"key":"ref_48","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A Review on Evaluation Metrics for Data Classification Evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_50","first-page":"37","article-title":"Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J. Mach. Learn. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"RG4003","DOI":"10.1029\/2004RG000150","article-title":"Mesoscale convective systems","volume":"42","author":"Houze","year":"2004","journal-title":"Rev. Geophys."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Guerova, G., Dimitrova, T., Vassileva, K., Slavchev, M., Stoev, K., and Georgiev, S. (2020). BalkanMed real time severe weather service: progress and prospects in Bulgaria. Adv. Space Res.","DOI":"10.1016\/j.asr.2020.07.005"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/16\/2536\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:57:27Z","timestamp":1760176647000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/16\/2536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,6]]},"references-count":52,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12162536"],"URL":"https:\/\/doi.org\/10.3390\/rs12162536","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,6]]}}}