{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T12:29:14Z","timestamp":1768739354852,"version":"3.49.0"},"reference-count":19,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["2020B1111200001"],"award-info":[{"award-number":["2020B1111200001"]}]},{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["SCSF202301"],"award-info":[{"award-number":["SCSF202301"]}]},{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["U2021Z05"],"award-info":[{"award-number":["U2021Z05"]}]},{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["2019YFC1510304"],"award-info":[{"award-number":["2019YFC1510304"]}]},{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["2021KJ019"],"award-info":[{"award-number":["2021KJ019"]}]},{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["2021Z003"],"award-info":[{"award-number":["2021Z003"]}]},{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["GRMC2022Z05"],"award-info":[{"award-number":["GRMC2022Z05"]}]},{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["GRMC2021XQ03"],"award-info":[{"award-number":["GRMC2021XQ03"]}]},{"name":"Key-Area Research and Development Program of Guang-dong Province","award":["2023LASW-B02"],"award-info":[{"award-number":["2023LASW-B02"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["2020B1111200001"],"award-info":[{"award-number":["2020B1111200001"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["SCSF202301"],"award-info":[{"award-number":["SCSF202301"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["U2021Z05"],"award-info":[{"award-number":["U2021Z05"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["2019YFC1510304"],"award-info":[{"award-number":["2019YFC1510304"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["2021KJ019"],"award-info":[{"award-number":["2021KJ019"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["2021Z003"],"award-info":[{"award-number":["2021Z003"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["GRMC2022Z05"],"award-info":[{"award-number":["GRMC2022Z05"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["GRMC2021XQ03"],"award-info":[{"award-number":["GRMC2021XQ03"]}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["2023LASW-B02"],"award-info":[{"award-number":["2023LASW-B02"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["2020B1111200001"],"award-info":[{"award-number":["2020B1111200001"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["SCSF202301"],"award-info":[{"award-number":["SCSF202301"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["U2021Z05"],"award-info":[{"award-number":["U2021Z05"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["2019YFC1510304"],"award-info":[{"award-number":["2019YFC1510304"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["2021KJ019"],"award-info":[{"award-number":["2021KJ019"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["2021Z003"],"award-info":[{"award-number":["2021Z003"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["GRMC2022Z05"],"award-info":[{"award-number":["GRMC2022Z05"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["GRMC2021XQ03"],"award-info":[{"award-number":["GRMC2021XQ03"]}]},{"name":"Joint Fund of Key Laboratory of Atmosphere Sounding, CMA and Research Centre on Meteorological Observation Engineering Technology, CMA","award":["2023LASW-B02"],"award-info":[{"award-number":["2023LASW-B02"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["2020B1111200001"],"award-info":[{"award-number":["2020B1111200001"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["SCSF202301"],"award-info":[{"award-number":["SCSF202301"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["U2021Z05"],"award-info":[{"award-number":["U2021Z05"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["2019YFC1510304"],"award-info":[{"award-number":["2019YFC1510304"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["2021KJ019"],"award-info":[{"award-number":["2021KJ019"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["2021Z003"],"award-info":[{"award-number":["2021Z003"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["GRMC2022Z05"],"award-info":[{"award-number":["GRMC2022Z05"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["GRMC2021XQ03"],"award-info":[{"award-number":["GRMC2021XQ03"]}]},{"name":"Key project of monitoring, early warning and prevention of major natural disasters of China","award":["2023LASW-B02"],"award-info":[{"award-number":["2023LASW-B02"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["2020B1111200001"],"award-info":[{"award-number":["2020B1111200001"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["SCSF202301"],"award-info":[{"award-number":["SCSF202301"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["U2021Z05"],"award-info":[{"award-number":["U2021Z05"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["2019YFC1510304"],"award-info":[{"award-number":["2019YFC1510304"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["2021KJ019"],"award-info":[{"award-number":["2021KJ019"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["2021Z003"],"award-info":[{"award-number":["2021Z003"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["GRMC2022Z05"],"award-info":[{"award-number":["GRMC2022Z05"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["GRMC2021XQ03"],"award-info":[{"award-number":["GRMC2021XQ03"]}]},{"name":"Science and Technology Development Fund of CAMS","award":["2023LASW-B02"],"award-info":[{"award-number":["2023LASW-B02"]}]},{"name":"Basic Research Fund of CAMS","award":["2020B1111200001"],"award-info":[{"award-number":["2020B1111200001"]}]},{"name":"Basic Research Fund of CAMS","award":["SCSF202301"],"award-info":[{"award-number":["SCSF202301"]}]},{"name":"Basic Research Fund of CAMS","award":["U2021Z05"],"award-info":[{"award-number":["U2021Z05"]}]},{"name":"Basic Research Fund of CAMS","award":["2019YFC1510304"],"award-info":[{"award-number":["2019YFC1510304"]}]},{"name":"Basic Research Fund of CAMS","award":["2021KJ019"],"award-info":[{"award-number":["2021KJ019"]}]},{"name":"Basic Research Fund of CAMS","award":["2021Z003"],"award-info":[{"award-number":["2021Z003"]}]},{"name":"Basic Research Fund of CAMS","award":["GRMC2022Z05"],"award-info":[{"award-number":["GRMC2022Z05"]}]},{"name":"Basic Research Fund of CAMS","award":["GRMC2021XQ03"],"award-info":[{"award-number":["GRMC2021XQ03"]}]},{"name":"Basic Research Fund of CAMS","award":["2023LASW-B02"],"award-info":[{"award-number":["2023LASW-B02"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["2020B1111200001"],"award-info":[{"award-number":["2020B1111200001"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["SCSF202301"],"award-info":[{"award-number":["SCSF202301"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["U2021Z05"],"award-info":[{"award-number":["U2021Z05"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["2019YFC1510304"],"award-info":[{"award-number":["2019YFC1510304"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["2021KJ019"],"award-info":[{"award-number":["2021KJ019"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["2021Z003"],"award-info":[{"award-number":["2021Z003"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["GRMC2022Z05"],"award-info":[{"award-number":["GRMC2022Z05"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["GRMC2021XQ03"],"award-info":[{"award-number":["GRMC2021XQ03"]}]},{"name":"Science and Technology Research Project of Guangdong Province Meteorological Bureau","award":["2023LASW-B02"],"award-info":[{"award-number":["2023LASW-B02"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["2020B1111200001"],"award-info":[{"award-number":["2020B1111200001"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["SCSF202301"],"award-info":[{"award-number":["SCSF202301"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["U2021Z05"],"award-info":[{"award-number":["U2021Z05"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["2019YFC1510304"],"award-info":[{"award-number":["2019YFC1510304"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["2021KJ019"],"award-info":[{"award-number":["2021KJ019"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["2021Z003"],"award-info":[{"award-number":["2021Z003"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["GRMC2022Z05"],"award-info":[{"award-number":["GRMC2022Z05"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["GRMC2021XQ03"],"award-info":[{"award-number":["GRMC2021XQ03"]}]},{"name":"Open Grants of the State Key Laboratory of Severe Weather","award":["2023LASW-B02"],"award-info":[{"award-number":["2023LASW-B02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gust fronts (GFs) belong to the boundary layer convergence system. A strong GF can cause serious wind disasters, so its automatic monitoring and identification are very helpful but difficult in daily meteorological operations. By collecting convective weather processes in Hubei, Jiangsu, and other regions of China, 1422 GFs from 106 S-band new-generation weather radar (CINRAD\/SA) volume scan data are labeled as positive samples by means of human\u2013computer interaction, and the same number of negative samples are randomly tagged from no GF radar data. A deep learning dataset including 2844 labels with a positive and negative sample ratio of 1:1 is constructed, and 80%, 10%, and 10% of the dataset are separated as training, validation, and test sets, respectively. Then, the training dataset is expanded to 273,120 samples by data augmentation technology. Since the height of a GF is generally less than 1.5 km, three deep-learning-based models are trained for GF automatic recognition according to the distance from the radars. Three models (M1, M2, M3) are trained with the data at a 0.5\u00b0 elevation angle from 65 to 180 km away from the radars, at 0.5\u00b0 and 1.5\u00b0 angles from 40 to 65 km, and at 0.5\u00b0, 1.5\u00b0, and 2.4\u00b0 angles within 40 km, respectively. The precision, confusion matrix, and its derived indicators including receiver operating characteristic curve (ROC) and the area under ROC (AUC) are used to evaluate the three models by the test set. The results show that the identification precisions of the models are 97.66% (M1), 90% (M2), and 90.43% (M3), respectively. All the hit rates are over 89%, the false positive rates are less than 11%, and the critical success indexes (CSIs) surpass 82%. In addition, all the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.93. These results suggest that the three models can effectively achieve the automatic discrimination of GFs. Finally, the models are demonstrated by three GF events detected with Qingpu, Nantong, and Cangzhou radars.<\/jats:p>","DOI":"10.3390\/rs16030439","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:22:32Z","timestamp":1705994552000},"page":"439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Radar Echo Recognition of Gust Front Based on Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Hanyuan","family":"Tian","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China"},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, Haikou 570100, China"},{"name":"State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4991-3647","authenticated-orcid":false,"given":"Zhiqun","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, Haikou 570100, China"},{"name":"State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"given":"Fuzeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Peilong","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Fen","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China"}]},{"given":"Liang","family":"Leng","sequence":"additional","affiliation":[{"name":"CMA Basin Heavy Rainfall Key Laboratory, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"ref_1","unstructured":"Zhang, P.C., Du, B.Y., and Dai, T.P. (2001). Radar Meteorology, China Meteorological Press. (In Chinese)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1175\/1520-0477(1977)058<0920:TDAPJD>2.0.CO;2","article-title":"The Dulles Airport Pressure Jump Detector Array for Gust Front Detection","volume":"58","author":"Bedard","year":"1977","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1175\/1520-0493(1987)115<0905:GFCADB>2.0.CO;2","article-title":"Gust Front Characteristics as Detected by Doppler radar","volume":"115","author":"Klingle","year":"1987","journal-title":"Mon. Weather Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1175\/1520-0493(1995)123<2913:CIAWAS>2.0.CO;2","article-title":"Convection Initiation Associated with a Sea-Breeze Front, a Gust Front, and Their Collision","volume":"123","author":"Kingsmill","year":"1995","journal-title":"Mon. Weather Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2019MS001910","DOI":"10.1029\/2019MS001910","article-title":"Particle-Based Tracking of Cold Pool Gust Fronts","volume":"12","author":"Henneberg","year":"2020","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1175\/1520-0493(1982)110<0707:MAOTGF>2.0.CO;2","article-title":"Multiscale Aspects of Thunderstorm Gust Fronts and Their Effects on Subsequent Storm Development","volume":"110","author":"Weaver","year":"1982","journal-title":"Mon. Weather Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1175\/1520-0426(1986)003<0036:ADOGF>2.0.CO;2","article-title":"Automatic Detection of Gust Fronts","volume":"3","author":"Uyeda","year":"1986","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1175\/1520-0426(1993)010<0693:TGFDAW>2.0.CO;2","article-title":"The Gust-Front Detection and Wind-Shift Algorithms for the Terminal Doppler Weather radar System","volume":"10","author":"Hermes","year":"1993","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_9","first-page":"182","article-title":"Machine-intelligent approach to automated gust-front detection for Doppler weather radars. Sensing, Imaging, and Vision for Control and Guidance of Aerospace Vehicles","volume":"2220","author":"Troxel","year":"1994","journal-title":"Int. Soc. Opt. Photonics"},{"key":"ref_10","unstructured":"Kwon, S.M. (1994). Pixel-Level Data Fusion Techniques Applied to the Detection of Gust Fonts. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_11","first-page":"117","article-title":"Automatic Identification and Alert of Gust Fronts","volume":"24","author":"Zheng","year":"2013","journal-title":"J. Appl. Meteor. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s13351-014-3240-2","article-title":"Gust Front Statistical Characteristics and Automatic Identification Algorithm for CINRAD","volume":"28","author":"Zheng","year":"2014","journal-title":"J. Meteorol. Res."},{"key":"ref_13","first-page":"44","article-title":"Improvement of the MIGFA Technique for Identifying Gust Front and Its Verification","volume":"42","author":"Xu","year":"2016","journal-title":"Meteorol. Mon."},{"key":"ref_14","unstructured":"Leng, L., Xiao, Y.J., and Wu, T. (2016). Automatic Recognition of Gust Fronts Based on Mathematical Morphology. Meteorol. Sci. Technol., 44, (In Chinese)."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1109\/TGRS.2016.2628520","article-title":"Neuro-Fuzzy Gust Front Detection Algorithm With S-Band Polarimetric radar","volume":"55","author":"Hwang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1007\/s13351-018-7089-7","article-title":"An Algorithm for Automated Identification of Gust Fronts from Doppler radar Data","volume":"32","author":"Yuan","year":"2018","journal-title":"J. Meteorol. Res."},{"key":"ref_17","first-page":"81","article-title":"Gust front detection algorithm based on deep convolutional neural network","volume":"39","author":"Xu","year":"2020","journal-title":"Torrential Rain Disasters"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xie, P., Hu, Z., Yuan, S., Zheng, J., Tian, H., and Xu, F. (2023). radar Echo Recognition of Squall Line Based on Deep Learning. Remote Sens., 15.","DOI":"10.3390\/rs15194726"},{"key":"ref_19","first-page":"9","article-title":"Analysis on the Birth and Disappearance History and Weather Characteristics of a Rare Gust Front in Heilongjiang Province","volume":"38","author":"Wang","year":"2021","journal-title":"Heilongjiang Meteorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/439\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:47:50Z","timestamp":1760104070000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/3\/439"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,23]]},"references-count":19,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16030439"],"URL":"https:\/\/doi.org\/10.3390\/rs16030439","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,23]]}}}