{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T04:30:36Z","timestamp":1784089836845,"version":"3.55.0"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Commission","doi-asserted-by":"publisher","award":["JCYJ20180507183823045"],"award-info":[{"award-number":["JCYJ20180507183823045"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Commission","doi-asserted-by":"publisher","award":["JCYJ20200109113014456"],"award-info":[{"award-number":["JCYJ20200109113014456"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/jstars.2020.3040648","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T20:59:20Z","timestamp":1606424360000},"page":"843-857","source":"Crossref","is-referenced-by-count":90,"title":["PFST-LSTM: A SpatioTemporal LSTM Model With Pseudoflow Prediction for Precipitation Nowcasting"],"prefix":"10.1109","volume":"14","author":[{"given":"Chuyao","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1894-984X","authenticated-orcid":false,"given":"Xutao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1807-8581","authenticated-orcid":false,"given":"Yunming","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","first-page":"11\ufffd474","article-title":"Disentangling physical dynamics from unknown factors for unsupervised video prediction","author":"guen","year":"0"},{"key":"ref32","first-page":"3208","article-title":"PDE-Net: Learning PDES from data","author":"long","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","first-page":"613","article-title":"Generating videos with scene dynamics","author":"vondrick","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","first-page":"1","article-title":"Deep multi-scale video prediction beyond mean square error","author":"mathieu","year":"0"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1175\/2009WAF2222350.1"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/atmos8030048"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s00376-012-2026-7"},{"key":"ref12","article-title":"A critical review of recurrent neural networks for sequence learning","author":"lipton","year":"2015"},{"key":"ref13","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref15","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"shi","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2644865"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/atmos10050244"},{"key":"ref18","first-page":"879","article-title":"PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMS","author":"wang","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref19","article-title":"Delving deeper into convolution networks for learning video representation","author":"ballas","year":"2015"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_46"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/nature14956"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00937"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-11-00263.1"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2926776"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_44"},{"key":"ref5","article-title":"Application of spatiotemporal predictive learning in precipitation nowcasting","author":"wang","year":"2018"},{"key":"ref8","first-page":"1","article-title":"Towards the blending of NWP with nowcast–operation experience in B 08 FDP","volume":"30","author":"wong","year":"0","journal-title":"Proceedings of WMO Symposium on Nowcasting"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-73603-7_2"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2019.02.036"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-12-1387-2019"},{"key":"ref1","first-page":"5617","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","author":"shi","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","first-page":"1","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"chung","year":"0"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref21","first-page":"2017","article-title":"Spatial transformer networks","author":"jaderberg","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","first-page":"1","article-title":"Eidetic 3D LSTM: A model for video prediction and beyond","author":"wang","year":"0"},{"key":"ref23","first-page":"5123","article-title":"PredRNN++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning","author":"wang","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref26","first-page":"667","article-title":"Dynamic filter networks","author":"jia","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","first-page":"64","article-title":"Unsupervised learning for physical interaction through video prediction","author":"finn","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4609443\/9314330\/09272611.pdf?arnumber=9272611","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:02:04Z","timestamp":1642003324000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9272611\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/jstars.2020.3040648","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}