{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:08:48Z","timestamp":1774570128628,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key-Area Research and Development Program of Guangdong Province","award":["2020B0101130021"],"award-info":[{"award-number":["2020B0101130021"]}]},{"name":"the National Key R&amp;D Program of China","award":["2018YFC1507401"],"award-info":[{"award-number":["2018YFC1507401"]}]},{"name":"the National Key R&amp;D Program of China","award":["2019YFC1510203"],"award-info":[{"award-number":["2019YFC1510203"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971340"],"award-info":[{"award-number":["41971340"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Heavy rain associated with landfalling typhoons often leads to disasters in South China, which can be reduced by improving the accuracy of radar quantitative precipitation estimation (QPE). At present, raindrop size distribution (DSD)-based nonlinear fitting (QPEDSD) and traditional neural networks are the main radar QPE algorithms. The former is not sufficient to represent the spatiotemporal variability of DSDs through the generalized Z\u2013R or polarimetric radar rainfall relations that are established using statistical methods since such parametric methods do not consider the spatial distribution of radar observables, and the latter is limited by the number of network layers and availability of data for training the model. In this paper, we propose an alternative approach to dual-polarization radar QPE based on deep learning (QPENet). Three datasets of \u201cdual-polarization radar observations\u2014surface rainfall (DPO\u2014SR)\u201d were constructed using radar observations and corresponding measurements from automatic weather stations (AWS) and used for QPENetV1, QPENetV2, and QPENetV3. In particular, 13 \u00d7 13, 25 \u00d7 25, and 41 \u00d7 41 radar range bins surrounding each AWS location were used in constructing the datasets for QPENetV1, QPENetV2, and QPENetV3, respectively. For training the QPENet models, the radar data and AWS measurements from eleven landfalling typhoons in South China during 2017\u20132019 were used. For demonstration, an independent typhoon event was randomly selected (i.e., Merbok) to implement the three trained models to produce rainfall estimates. The evaluation results and comparison with traditional QPEDSD algorithms show that the QPENet model has a better performance than the traditional parametric relations. Only when the hourly rainfall intensity is less than 5 mm (R &lt; 5 mm\u00b7h\u22121), the QPEDSD model shows a comparable performance to QPENet. Comparing the three versions of the QPENet model, QPENetV2 has the best overall performance. Only when the hourly rainfall intensity is less than 5 mm (R &lt; 5 mm\u00b7h\u22121), QPENetV3 performs the best.<\/jats:p>","DOI":"10.3390\/rs13163157","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T08:57:14Z","timestamp":1628585834000},"page":"3157","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Deep Learning for Polarimetric Radar Quantitative Precipitation Estimation during Landfalling Typhoons in South China"],"prefix":"10.3390","volume":"13","author":[{"given":"Yonghua","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"Guangdong Meteorological Public Service Center, Guangzhou 510641, China"},{"name":"Institute of Artificial Intelligence for Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7295-1716","authenticated-orcid":false,"given":"Shuoben","family":"Bi","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Liping","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9795-3064","authenticated-orcid":false,"given":"Haonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong Meteorological Public Service Center, Guangzhou 510641, China"}]},{"given":"Ping","family":"Shen","sequence":"additional","affiliation":[{"name":"Guangdong Emergency Early Warning Release Center, Guangzhou 510641, China"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong Technology Support Center of Flood Control, Guangzhou 510635, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4829-0276","authenticated-orcid":false,"given":"Yaqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence for Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"given":"Shun","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1007\/s11069-013-0755-2","article-title":"Composite risk assessment of typhoon-induced disaster for China\u2019s coastal area","volume":"69","author":"Yin","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bringi, V.N., and Chandrasekar, V. (2001). Polarimetric Doppler Weather Radar: Principles and Applications, Cambridge University Press.","DOI":"10.1017\/CBO9780511541094"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10669","DOI":"10.1029\/2019GL084771","article-title":"Rainfall Estimation From Ground Radar and TRMM Precipitation Radar Using Hybrid Deep Neural Networks","volume":"46","author":"Chen","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"141","DOI":"10.2151\/jmsj.2018-016","article-title":"High Resolution Radar Quantitative Precipitation Estimation in the San Francisco Bay Area: Rainfall Monitoring for the Urban Environment","volume":"96","author":"Cifelli","year":"2018","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.atmosres.2017.12.017","article-title":"Radar-derived quantitative precipitation estimation in complex terrain over the eastern Tibetan Plateau","volume":"203","author":"Gou","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xia, Q., Zhang, W., Chen, H., Lee, W.-C., Han, L., Ma, Y., and Liu, X. (2020). Quantification of Precipitation Using Polarimetric Radar Measurements during Several Typhoon Events in Southern China. Remote Sens., 12.","DOI":"10.3390\/rs12122058"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1175\/BAMS-86-6-809","article-title":"The joint polarization experiment: Polarimetric rainfall measurements and hydrometeor classification","volume":"86","author":"Ryzhkov","year":"2005","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/36.551944","article-title":"Development of neural network based algorithm for rainfall estimation based on radar measurements","volume":"35","author":"Xiao","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, H., Chandrasekar, V., and Cifelli, R. (2019, January 9\u201315). A Deep Learning Approach to Dual-Polarization Radar Rainfall Estimation. Proceedings of the 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), New Delhi, India.","DOI":"10.23919\/URSIAP-RASC.2019.8738337"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/LGRS.2004.842338","article-title":"Operational Feasibility of Neural-Network-Based Radar Rainfall Estimation","volume":"2","author":"Xu","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1175\/2009JAMC2172.1","article-title":"Rainfall estimation from polarimetric S-band radar measurements: Validation of a neutral netwrok approach","volume":"48","author":"Vulpiani","year":"2009","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1175\/JAS-D-17-0242.1","article-title":"Primary Modes of Global Drop Size Distributions","volume":"75","author":"Dolan","year":"2018","journal-title":"J. Atmos. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wen, G., Chen, H., Zhang, G., and Sun, J. (2018). An Inverse Model for Raindrop Size Distribution Retrieval with Polarimetric Variables. Remote Sens., 10.","DOI":"10.3390\/rs10081179"},{"key":"ref_14","first-page":"1231","article-title":"Real-time correction of weather radar data for the effects of bright band, range and orographic growth in widespread precipitation","volume":"120","author":"Kitchen","year":"1994","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1175\/1520-0450(2000)039<1923:RRRAKE>2.0.CO;2","article-title":"Reflectivity, Rain Rate, and Kinetic Energy Flux Relationships Based on Raindrop Spectra","volume":"39","author":"Steiner","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1002\/2014WR015672","article-title":"Probabilistic precipitation rate estimates with ground-based radar networks","volume":"51","author":"Kirstetter","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1175\/JHM-D-16-0124.1","article-title":"An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign","volume":"18","author":"Chen","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Han, L., Zhao, Y., Chen, H., and Chandrasekar, V. (2021). Advancing Radar Nowcasting Through Deep Transfer Learning. IEEE Trans. Geosci. Remote Sens., in press.","DOI":"10.1109\/TGRS.2021.3056470"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TGRS.2019.2942280","article-title":"A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations","volume":"58","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","unstructured":"Tan, H., Chandra, C.V., and Chen, H. (2021, June 01). A Deep Neural Network Model for Rainfall Estimation Using Polarimetric WSR-88DP Radar Observations. Available online: https:\/\/agu.confex.com\/agu\/fm16\/meetingapp.cgi\/Paper\/196830."},{"key":"ref_23","unstructured":"Tan, H., Chandrasekar, V., and Chen, H. (2017, January 19\u201326). A Machine Learning Model for Radar Rainfall Estimation Based on Gauge Observations. Proceedings of the 2017 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Montreal, QC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, H., Chandrasekar, V., Cifelli, R., Xie, P., and Tan, H. (2017, January 19\u201326). A data fusion system for accurate precipitation estimation using satellite and ground radar observations: Urban scale application in Dallas-Fort Worth Metroplex. Proceedings of the 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), Montreal, QC, Canada.","DOI":"10.23919\/URSIGASS.2017.8105093"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chandrasekar, V., Tan, H., and Chen, H. (2017, January 19\u201326). A machine learning system for rainfall estimation from spaceborne and ground radars. Proceedings of the 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), Montreal, QC, Canada.","DOI":"10.23919\/URSIGASS.2017.8105098"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Moraux, A., Dewitte, S., Cornelis, B., and Munteanu, A. (2019). Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements. Remote Sens., 11.","DOI":"10.3390\/rs11212463"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1007\/s00376-017-6241-0","article-title":"Statistics-based optimization of the polarimetric radar hydrometeor classification algorithm and its application for a squall line in South China","volume":"35","author":"Wu","year":"2018","journal-title":"Adv. Atmos. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1175\/2009JTECHA1358.1","article-title":"Algorithm for Estimation of the Specific Differential Phase","volume":"26","author":"Wang","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_29","first-page":"731","article-title":"Operational application and evaluation of the quantitative precipitation estimates algorithm based on the multi\u2013radar mosaic","volume":"72","author":"Gou","year":"2014","journal-title":"Acta Meteorol. Sin."},{"key":"ref_30","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deep-er with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, Y., Minh Nguyen, D., Deligiannis, N., Ding, W., and Munteanu, A. (2017). Hourglass-shapenetwork based semantic segmentation for high resolution aerial imagery. Remote Sens., 9.","DOI":"10.3390\/rs9060522"},{"key":"ref_34","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, L., Bi, S., Wu, Z., Shen, P., Ao, Z., Chen, C., and Zhang, Y. (2019). Analysis of Dual-Polarimetric Radar Variables and Quantitative Precipitation Estimators for Landfall Typhoons and Squall Lines Based on Disdrometer Data in Southern China. Atmosphere, 10.","DOI":"10.3390\/atmos10010030"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, L., Wen, H., Wu, C., and Zhang, Y. (2018). Evaluation of the Polarimetric-Radar Quantitative Precipitation Estimates of an Extremely Heavy Rainfall Event and Nine Common Rainfall Events in Guangzhou. Atmosphere, 9.","DOI":"10.3390\/atmos9090330"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3157\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:43:19Z","timestamp":1760164999000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,10]]},"references-count":36,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163157"],"URL":"https:\/\/doi.org\/10.3390\/rs13163157","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,10]]}}}