{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T19:14:43Z","timestamp":1776539683930,"version":"3.51.2"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,23]],"date-time":"2019-04-23T00:00:00Z","timestamp":1555977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/GEO\/50019\/2019."],"award-info":[{"award-number":["UID\/GEO\/50019\/2019."]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms.<\/jats:p>","DOI":"10.3390\/rs11080966","type":"journal-article","created":{"date-parts":[[2019,4,24]],"date-time":"2019-04-24T03:14:28Z","timestamp":1556075668000},"page":"966","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5858-6815","authenticated-orcid":false,"given":"Pedro","family":"Benevides","sequence":"first","affiliation":[{"name":"Dire\u00e7\u00e3o-Geral do Territ\u00f3rio (DGT), 1099-052 Lisbon, Portugal"}]},{"given":"Joao","family":"Catalao","sequence":"additional","affiliation":[{"name":"Instituto Dom Luiz (IDL), Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7621-5014","authenticated-orcid":false,"given":"Giovanni","family":"Nico","sequence":"additional","affiliation":[{"name":"Istituto per le Applicazioni del Calcolo (IAC), Consiglio Nazionale delle Ricerche (CNR), 70126 Bari, Italy"},{"name":"Department of Cartography and Geoinformatics, Institute of Earth Sciences, State University (SPSU), 199034 Saint Petersburg, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yan, X., Ducrocq, V., Poli, P., Hakam, M., Jaubert, G., and Walpersdorf, A. (2009). Impact of GPS zenith delay assimilation on convective-scale prediction of Mediterranean heavy rainfall. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD011036"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3341","DOI":"10.1002\/2017JD027472","article-title":"Assimilating InSAR maps of water vapor to improve heavy rainfall forecasts: A case study with two successive storms","volume":"123","author":"Mateus","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5425","DOI":"10.5194\/acp-13-5425-2013","article-title":"Preliminary signs of the initiation of deep convection by GNSS","volume":"13","author":"Brenot","year":"2013","journal-title":"Atmos. Chem. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6710","DOI":"10.1109\/TGRS.2015.2446758","article-title":"Uncertainty Assessment of the Estimated Atmospheric Delay Obtained by a Numerical Weather Model (NMW)","volume":"53","author":"Mateus","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","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_6","first-page":"A0217","article-title":"Nowcasting the lightning activity in Peninsular Malaysia using the GPS PWV during the 2009 intermonsoons","volume":"57","author":"Suparta","year":"2014","journal-title":"Ann. Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s40623-018-0795-7","article-title":"Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers","volume":"70","author":"Barindelli","year":"2018","journal-title":"Earth Planets Space"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1002\/qj.2087","article-title":"Assimilation of GNSS ZTD and radar radial velocity for the benefit of very-short-range regional weather forecasts","volume":"139","year":"2013","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1109\/TGRS.2016.2631449","article-title":"Analysis of Galileo and GPS Integration for GNSS Tomography","volume":"55","author":"Benevides","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1016\/j.asr.2014.02.021","article-title":"Real-time zenith tropospheric delays in support of numerical weather prediction applications","volume":"53","author":"Dousa","year":"2014","journal-title":"Adv. Space Res."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"4835","DOI":"10.1109\/TGRS.2018.2839899","article-title":"GPS-Derived PWV for Rainfall Nowcasting in Tropical Region","volume":"56","author":"Manandhar","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0022-1694(92)90046-X","article-title":"Rainfall forecasting in space and time using a neural network","volume":"137","author":"French","year":"1992","journal-title":"J. Hydrol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1175\/1520-0434(1999)014<0338:PFUANN>2.0.CO;2","article-title":"Precipitation forecasting using a neural network","volume":"14","author":"Hall","year":"1999","journal-title":"Weather Forecast."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jhydrol.2004.06.028","article-title":"Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region","volume":"301","author":"Ramirez","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"97","DOI":"10.5194\/adgeo-7-97-2006","article-title":"Artificial neural-network technique for precipitation nowcasting from satellite imagery","volume":"7","author":"Rivolta","year":"2006","journal-title":"Adv. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.5194\/hess-13-1413-2009","article-title":"An artificial neural network model for rainfall forecasting in Bangkok, Thailand","volume":"13","author":"Hung","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jhydrol.2010.05.040","article-title":"Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques","volume":"389","author":"Wu","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_20","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015, January 7\u201312). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1016\/j.eswa.2014.09.029","article-title":"Modeling of zenith path delay over Antarctica using an adaptive neuro fuzzy inference system technique","volume":"42","author":"Suparta","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s00190-018-1114-6","article-title":"A neural network model for predicting weighted mean temperature","volume":"92","author":"Ding","year":"2018","journal-title":"J. Geod."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1175\/JAMC-D-15-0161.1","article-title":"Modeling of precipitable water vapor using an adaptive neuro-fuzzy inference system in the absence of the GPS network","volume":"55","author":"Suparta","year":"2016","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.jastp.2018.06.011","article-title":"A GNSS-based weather forecasting approach using Nonlinear Auto Regressive Approach with Exogenous Input (NARX)","volume":"178","author":"Rahimi","year":"2018","journal-title":"J. Atmos. Sol.-Terr. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/72.548162","article-title":"Learning long-term dependencies in NARX recurrent neural networks","volume":"7","author":"Lin","year":"1996","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_26","unstructured":"Finkensieper, S., Meirink, J.F., van Zadelhoff, G.J., Hanschmann, T., Benas, N., Stengel, M., Fuchs, P., Hollmann, R., and Werscheck, M. (2019, April 22). CLAAS-2: CM SAF CLoud Property dAtAset Using SEVIRI. Available online: https:\/\/bit.ly\/2GrdHU2."},{"key":"ref_27","unstructured":"Herring, T., King, R.W., and McClusky, S.C. (2010). GAMIT Reference Manual\u2014GPS Analysis at MIT\u2014Release 10.4, Department of Earth, Atmospheric and Planetary Sciences, MIT."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s10291-018-0755-5","article-title":"4D wet refractivity estimation in the atmosphere using GNSS tomography initialized by radiosonde and AIRS measurements: Results from a 1-week intensive campaign","volume":"22","author":"Benevides","year":"2018","journal-title":"GPS Solut."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15787","DOI":"10.1029\/92JD01517","article-title":"GPS meteorology: Remote sensing of the atmospheric water vapor using the Global Positioning System","volume":"97","author":"Bevis","year":"1992","journal-title":"J. Geophys. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1109\/LGRS.2014.2318993","article-title":"Maps of PWV temporal changes by SAR interferometry: A study on the properties of atmosphere\u2019s temperature profiles","volume":"11","author":"Mateus","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.engappai.2012.01.023","article-title":"A hybrid algorithm for artificial neural network training","volume":"26","author":"Yaghini","year":"2013","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/8\/966\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:46:24Z","timestamp":1760186784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/8\/966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,23]]},"references-count":31,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11080966"],"URL":"https:\/\/doi.org\/10.3390\/rs11080966","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,23]]}}}