{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:47:57Z","timestamp":1774964877355,"version":"3.50.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2022ZD0119500"],"award-info":[{"award-number":["2022ZD0119500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Program","doi-asserted-by":"publisher","award":["KCXFZ20211020163403005"],"award-info":[{"award-number":["KCXFZ20211020163403005"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302127"],"award-info":[{"award-number":["62302127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376072"],"award-info":[{"award-number":["62376072"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272130"],"award-info":[{"award-number":["62272130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tgrs.2023.3330303","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T19:20:43Z","timestamp":1699298443000},"page":"1-14","source":"Crossref","is-referenced-by-count":8,"title":["A Practical Online Incremental Learning Framework for Precipitation Nowcasting"],"prefix":"10.1109","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4848-609X","authenticated-orcid":false,"given":"Chuyao","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1805-7705","authenticated-orcid":false,"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4546-6870","authenticated-orcid":false,"given":"Huiwei","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6178-6797","authenticated-orcid":false,"given":"Baoquan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1894-984X","authenticated-orcid":false,"given":"Xutao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2585-0290","authenticated-orcid":false,"given":"Tingting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering and Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1807-8581","authenticated-orcid":false,"given":"Yunming","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Harbin Institute of Technology, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3217639"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3100847"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3198222"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3264545"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03854-z"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06184-4"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.021"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3057446"},{"key":"ref9","first-page":"1","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Shi"},{"key":"ref10","first-page":"1","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Shi"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107900"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3390\/atmos8030048"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2003.11.034"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2005.09.014"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s00376-012-2026-7"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s13143-010-1008-x"},{"key":"ref17","first-page":"24","article-title":"Towards the blending of NWP with nowcast-operation experience in B08FDP","volume-title":"Proc. WMO Symp. Nowcasting","volume":"30","author":"Wong"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.124140"},{"key":"ref19","first-page":"1","article-title":"PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Wang"},{"key":"ref20","first-page":"1","article-title":"Eidetic 3D LSTM: A model for video prediction and beyond","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00937"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6819"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.07.061"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-15-5407-2022"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12184"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2019.02.036"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-13-2631-2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2021.01.036"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"ref30","first-page":"1","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Gulrajani"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2017.8128174"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2926776"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.3023950"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-16-2737-2023"},{"key":"ref35","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zenke"},{"key":"ref36","first-page":"1","article-title":"Online structured Laplace approximations for overcoming catastrophic forgetting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Ritter"},{"key":"ref37","first-page":"1","article-title":"Overcoming catastrophic interference using conceptor-aided backpropagation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"He"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3055564"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00528"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref42","first-page":"1","article-title":"A neural Dirichlet process mixture model for task-free continual learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lee"},{"key":"ref43","article-title":"PathNet: Evolution channels gradient descent in super neural networks","author":"Fernando","year":"2017","journal-title":"arXiv:1701.08734"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref45","first-page":"1","article-title":"Experience replay for continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Rolnick"},{"key":"ref46","first-page":"15920","article-title":"Dark experience for general continual learning: A strong, simple baseline","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Buzzega"},{"key":"ref47","first-page":"11849","article-title":"Online continual learning with maximal interfered retrieval","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Aljundi"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17159"},{"key":"ref49","first-page":"1","article-title":"Gradient based sample selection for online continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Aljundi"},{"key":"ref50","first-page":"1","article-title":"Gradient-based editing of memory examples for online task-free continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Jin"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3219605"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02322"},{"key":"ref53","first-page":"1","article-title":"New insights on reducing abrupt representation change in online continual learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Caccia"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00729"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3040648"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10354519\/10309842.pdf?arnumber=10309842","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T01:23:08Z","timestamp":1703035388000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10309842\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2023.3330303","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}