{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:27:49Z","timestamp":1765268869450,"version":"3.37.3"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771479","61971429","61921001"],"award-info":[{"award-number":["61771479","61971429","61921001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["QL20210001"],"award-info":[{"award-number":["QL20210001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/jstars.2022.3189037","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T19:26:04Z","timestamp":1657221964000},"page":"5596-5606","source":"Crossref","is-referenced-by-count":10,"title":["A Spatio-Temporal Neural Network for Fine-Scale Wind Field Nowcasting Based on Lidar Observation"],"prefix":"10.1109","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1163-5533","authenticated-orcid":false,"given":"Hang","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System and the College of Electronic Science and Technology, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9419-4798","authenticated-orcid":false,"given":"Chun","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System and the College of Electronic Science and Technology, National University of Defense Technology, Changsha, China"}]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Naval Research Academy, Shanghai, China"}]},{"given":"Xuesong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System and the College of Electronic Science and Technology, National University of Defense Technology, Changsha, China"}]},{"given":"Pak-Wai","family":"Chan","sequence":"additional","affiliation":[{"name":"Hong Kong Observatory, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4842-0843","authenticated-orcid":false,"given":"Kai-Kwong","family":"Hon","sequence":"additional","affiliation":[{"name":"Hong Kong Observatory, Hong Kong, China"}]},{"given":"Dingfu","family":"Zhou","sequence":"additional","affiliation":[{"name":"South-West Institute of Technical Physics, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5334-7663","authenticated-orcid":false,"given":"Jianbing","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System and the College of Electronic Science and Technology, National University of Defense Technology, Changsha, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau2027"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/b978-0-12-821353-7.00014-4"},{"issue":"2","key":"ref3","first-page":"16","article-title":"Ground-based wind shear detection systems have become vital to safe operations","volume":"62","author":"Keohan","year":"2007","journal-title":"Icao J."},{"issue":"3\/4","key":"ref4","first-page":"1","article-title":"Data show that US wake-turbulence accidents are most frequent at low altitude and during approach and landing","volume":"21","author":"Veillette","year":"2002","journal-title":"Flight Saf. Dig."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.crhy.2005.06.002"},{"issue":"2","key":"ref6","first-page":"47","article-title":"Wind shear system cost benefit analysis","volume":"18","author":"Hallowell","year":"2010","journal-title":"Lincoln Lab. J."},{"key":"ref7","first-page":"81","article-title":"Monitoring wind, turbulence and aircraft wake vortices by high resolution RADAR and LIDAR remote sensors in all weather conditions","volume-title":"Proc. URSI Sci. Days","author":"Barbaresco","year":"2015"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2019.2954107"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1175\/2007JTECHA963.1"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/rs11182115"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5194\/amt-8-2251-2015"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1364\/OE.382968"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1364\/OE.26.016377"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s00024-018-2058-8"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2008.2001758"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-15-00295.1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1979)018<0532:OTAOSD>2.0.CO;2"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s00703-004-0093-8"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s00703-005-0170-7"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1175\/JTECH-D-16-0199.1"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1002\/asl2.564"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0426(1995)012<0230:ADAOTV>2.0.CO;2"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.4271\/861847"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2019.04.154"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2004.07.003"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/RADAR.2014.7060421"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s10236-003-0036-9"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1029\/2008GL033975"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/nature14956"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2004.01.070"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-6105(00)00081-7"},{"issue":"4","key":"ref32","first-page":"397","article-title":"Implementation and applications of chaotic oscillatory based neural network for wind prediction problems","volume":"24","author":"Kwong","year":"2011","journal-title":"Atmosfera"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.05.044"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/PES.2006.1709479"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2019.04.157"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0912-1"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2018.2844102"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/SMC52423.2021.9659311"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CIEEC50170.2021.9510691"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2019.2897136"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3390\/atmos8030048"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-11-00263.1"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2005.846018"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005568"},{"key":"ref45","first-page":"1","article-title":"Machine learning for precipitation nowcasting from radar images","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","author":"Agrawal","year":"2019"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1029\/2021GL095302"},{"key":"ref47","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume-title":"Proc. 28th Int. Conf. Neural Inf. Process. Syst.","author":"Shi","year":"2015"},{"key":"ref48","first-page":"5618","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shi","year":"2017"},{"article-title":"MetNet: A neural weather model for precipitation forecasting","year":"2020","author":"Snderby","key":"ref49"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03854-z"},{"article-title":"Precipitation nowcasting with star-bridge networks","year":"2019","author":"Cao","key":"ref51"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3128522"},{"key":"ref53","first-page":"123","article-title":"Three-dimensional high-resolution national radar mosaic","volume-title":"Proc. Conf. Aviation, Range, Aerosp. Meteorol.","author":"Zhang","year":"2004"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.3390\/atmos11030267"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3390\/atmos10050244"},{"key":"ref56","first-page":"1","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume-title":"Proc. ICML Workshop Deep Learn. Audio, Speech Lang. Process.","volume":"28","author":"Maas","year":"2013"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3155662"},{"key":"ref58","first-page":"658","article-title":"Generating images with perceptual similarity metrics based on deep networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dosovitskiy","year":"2016"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4609443\/9656571\/09817617.pdf?arnumber=9817617","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T04:51:30Z","timestamp":1706763090000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9817617\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":58,"URL":"https:\/\/doi.org\/10.1109\/jstars.2022.3189037","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"type":"print","value":"1939-1404"},{"type":"electronic","value":"2151-1535"}],"subject":[],"published":{"date-parts":[[2022]]}}}