{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T05:26:38Z","timestamp":1774070798139,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,4]],"date-time":"2021-04-04T00:00:00Z","timestamp":1617494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the China Natural Science Funds","award":["41904033, 41730109"],"award-info":[{"award-number":["41904033, 41730109"]}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)","award":["XDA17010304"],"award-info":[{"award-number":["XDA17010304"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0501405"],"award-info":[{"award-number":["2016YFB0501405"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019MD005"],"award-info":[{"award-number":["ZR2019MD005"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["KYCX20_1835"],"award-info":[{"award-number":["KYCX20_1835"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the \u201cthreshold-based\u201d models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010\u20132017 at the co-located HKSC\u2013KP (King\u2019s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.<\/jats:p>","DOI":"10.3390\/rs13071390","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T11:48:29Z","timestamp":1617623309000},"page":"1390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A New Method for Determining an Optimal Diurnal Threshold of GNSS Precipitable Water Vapor for Precipitation Forecasting"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5441-2818","authenticated-orcid":false,"given":"Haobo","family":"Li","sequence":"first","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, No.1 Daxue Road, Xuzhou 221116, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China"},{"name":"Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research Center, RMIT University, 124 La Trobe St, Melbourne, VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1720-6630","authenticated-orcid":false,"given":"Xiaoming","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Suqin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, No.1 Daxue Road, Xuzhou 221116, China"}]},{"given":"Kefei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, No.1 Daxue Road, Xuzhou 221116, China"}]},{"given":"Erjiang","family":"Fu","sequence":"additional","affiliation":[{"name":"Bei-stars Geospatial Information Innovation Institute, No. 1 Xinbei Road, Pukou District, Nanjing 210000, China"}]},{"given":"Ying","family":"Xu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Cong","family":"Qiu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Jinglei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3952-4621","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center of Beidou Navigation and Remote Sensing, Suzhou University of Science and Technology, Suzhou 215009, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15787","DOI":"10.1029\/92JD01517","article-title":"GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System","volume":"97","author":"Bevis","year":"1992","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104624","DOI":"10.1016\/j.atmosres.2019.104624","article-title":"Radio occultation and ground-based GNSS products for observing, understanding and predicting extreme events: A review","volume":"230","author":"Bonafoni","year":"2019","journal-title":"Atmos. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1029\/2018GL080412","article-title":"Sensing Heavy Precipitation With GNSS Polarimetric Radio Occultations","volume":"46","author":"Cardellach","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gao, F., Xu, T., Wang, N., Jiang, C., Du, Y., Nie, W., and Xu, G. (2018). Spatiotemporal evaluation of GNSS-R based on future fully operational global multi-GNSS and Eight-LEO constellations. Remote Sens., 10.","DOI":"10.3390\/rs10010067"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1016\/S1364-6826(00)00249-2","article-title":"Ground-based GPS water vapour estimation: Potential for meteorological forecasting","volume":"63","author":"Baker","year":"2001","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S1474-7065(02)00009-8","article-title":"Climate monitoring using GPS","volume":"27","author":"Gradinarsky","year":"2002","journal-title":"Phys. Chem. Earth"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1109\/JSTARS.2015.2406313","article-title":"Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements","volume":"8","author":"Zhang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sen."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Liu, Y., Yao, W., and Yao, Y. (2021). Hourly rainfall forecast model using supervised learning algorithm. IEEE Tran. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2021.3054582"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.atmosres.2013.11.026","article-title":"Ground-based GNSS ZTD\/IWV estimation system for numerical weather prediction in challenging weather conditions","volume":"138","author":"Rohm","year":"2013","journal-title":"Atmos. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.06.029","article-title":"The correlation between GNSS-derived precipitable water vapor and sea surface temperature and its responses to El Ni\u00f1o\u2013Southern Oscillation","volume":"216","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"D214678","article-title":"On the determination of atmospheric water vapor from GPS measurements","volume":"108","author":"Hagemann","year":"2003","journal-title":"J. Geophys. Res."},{"key":"ref_12","first-page":"D09110","article-title":"Seasonal variability of GPS-derived zenith tropospheric delay (1994\u20132006) and climate implications","volume":"112","author":"Jin","year":"2007","journal-title":"J. Geophys. Res."},{"key":"ref_13","first-page":"D19101","article-title":"Long-term trends in the atmospheric water vapor content estimated from ground-based GPS data","volume":"113","author":"Nilsson","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1002\/2015JD024181","article-title":"Water vapor-weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend","volume":"121","author":"Wang","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","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_16","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.jastp.2017.11.013","article-title":"GPS-based PWV for precipitation forecasting and its application to a typhoon event","volume":"167","author":"Zhao","year":"2018","journal-title":"J. Atmos. Sol. Terr. Phy."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, H., Wang, X., Wu, S., Zhang, K., Chen, X., Qiu, C., Zhang, S., Zhang, J., Xie, M., and Li, L. (2020). Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV. Remote Sens., 12.","DOI":"10.3390\/rs12244101"},{"key":"ref_18","unstructured":"Sangiorgio, M., Barindelli, S., Biondi, R., Solazzo, E., Realini, E., Venuti, G., and Guariso, G. (2019, January 25\u201327). Improved extreme rainfall events forecasting using neural networks and water vapor measures. Proceedings of the 6th International conference on Time Series and Forecasting (ITISE-2019), Granada, Spain."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56452-5","article-title":"Short-term rainfall forecast model based on the improved Bp\u2013nn algorithm","volume":"9","author":"Liu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"9323","DOI":"10.1109\/TGRS.2019.2926110","article-title":"A data-driven approach for accurate rainfall prediction","volume":"57","author":"Manandhar","year":"2019","journal-title":"IEEE Tran. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1175\/1520-0493(1993)121<1215:AOPWMI>2.0.CO;2","article-title":"Assimilation of precipitable water measurements into a mesoscale numerical model","volume":"121","author":"Kuo","year":"1993","journal-title":"Mon. Weather Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.1175\/MWR3436.1","article-title":"Short-range forecast impact from assimilation of GPS-IPW observations into the Rapid Update Cycle","volume":"135","author":"Smith","year":"2007","journal-title":"Mon. Weather Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"345","DOI":"10.5194\/amt-12-345-2019","article-title":"4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF","volume":"12","author":"Rohm","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1175\/MWR-D-15-0242.1","article-title":"A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh","volume":"144","author":"Benjamin","year":"2016","journal-title":"Mon. Weather Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1007\/s11069-019-03779-x","article-title":"Determination of extreme precipitation threshold and analysis of its effective patterns (case study: West of Iran)","volume":"99","author":"Shaffie","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_27","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. Hazard Earth Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.asr.2017.11.002","article-title":"Determining the precipitable water vapor thresholds under different rainfall strengths in Taiwan","volume":"61","author":"Yeh","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4891","DOI":"10.1109\/TGRS.2020.2968124","article-title":"An Improved Rainfall Forecasting Model Based on GNSS Observations","volume":"58","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5186","DOI":"10.1016\/j.csda.2007.11.008","article-title":"An adjusted boxplot for skewed distributions","volume":"52","author":"Hubert","year":"2008","journal-title":"Comput. Statist. Data Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3452","DOI":"10.1109\/TGRS.2014.2377041","article-title":"Real-time GPS precise point positioning-based precipitable water vapor estimation for rainfall monitoring and forecasting","volume":"53","author":"Shi","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1175\/2008JAMC1920.1","article-title":"Integrated water vapor field and multiscale variations over China from GPS measurements","volume":"47","author":"Jin","year":"2008","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Manandhar, S., Lee, Y.H., and Meng, Y.S. (2019). GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions. Remote Sens., 11.","DOI":"10.3390\/rs11222643"},{"key":"ref_34","first-page":"143","article-title":"Review of Weather Prediction Verifying Techniques","volume":"18","author":"Ding","year":"1995","journal-title":"J. Nanjing Inst. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"B\u00f6hm, J., Werl, B., and Schuh, H. (2006). Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data. J. Geophys. Res. Solid Earth, 111.","DOI":"10.1029\/2005JB003629"},{"key":"ref_36","first-page":"247","article-title":"Atmospheric Correction for the Troposphere and the Stratosphere in Radio Ranging of Satellites","volume":"15","author":"Saastamoinen","year":"1972","journal-title":"Geophys. Monogr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1175\/1520-0450(1994)033<0379:GMMZWD>2.0.CO;2","article-title":"GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water","volume":"33","author":"Bevis","year":"1994","journal-title":"J. Appl. Meteorol."},{"key":"ref_38","first-page":"3","article-title":"Inversing the content of vapor in atmosphere by GPS observations","volume":"28","author":"Chen","year":"2005","journal-title":"Mod. Surv. Mapp."},{"key":"ref_39","first-page":"755","article-title":"Objective evaluator of techniques for predicting severe weather events","volume":"56","author":"Donaldson","year":"1975","journal-title":"Bull. Amer. Meteorol. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1175\/2009WAF2222269.1","article-title":"Intercomparison of Spatial Forecast Verification Methods","volume":"24","author":"Gilleland","year":"2009","journal-title":"Weather Forecast."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1175\/1520-0434(1990)005<0576:OSMOSI>2.0.CO;2","article-title":"On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables","volume":"5","author":"Keller","year":"1990","journal-title":"Weather Forecast."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Manandhar, S., Dev, S., Lee, Y., Winkler, S., and Meng, Y. (2018, January 22\u201327). Systematic study of weather variables for rainfall detection. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517667"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S1474-7065(02)00010-4","article-title":"Monitoring of integrated water vapour from ground-based GPS observations and their assimilation in a limited-area NWP model","volume":"27","author":"Tomassini","year":"2002","journal-title":"Phys. Chem. Earth"},{"key":"ref_44","unstructured":"WMO (2017). Guidelines for Nowcasting Techniques, World Meteorological Organization. Available online: https:\/\/library.wmo.int\/doc_num.php?explnum_id=3795."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1390\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:50Z","timestamp":1760362430000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1390"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,4]]},"references-count":44,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071390"],"URL":"https:\/\/doi.org\/10.3390\/rs13071390","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,4]]}}}