{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:30:12Z","timestamp":1785501012979,"version":"3.56.0"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021ZD0110905"],"award-info":[{"award-number":["2021ZD0110905"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Programs for Science and Technology Development of Heilongjiang Province","award":["2021ZXJ05A03"],"award-info":[{"award-number":["2021ZXJ05A03"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106061"],"award-info":[{"award-number":["62106061"]}],"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":["61972114"],"award-info":[{"award-number":["61972114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["FRFCU5710010521"],"award-info":[{"award-number":["FRFCU5710010521"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"National Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["YQ2019F007"],"award-info":[{"award-number":["YQ2019F007"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tgrs.2023.3264545","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T17:44:51Z","timestamp":1680716691000},"page":"1-14","source":"Crossref","is-referenced-by-count":61,"title":["MM-RNN: A Multimodal RNN for Precipitation Nowcasting"],"prefix":"10.1109","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0346-5757","authenticated-orcid":false,"given":"Zhifeng","family":"Ma","sequence":"first","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-2115","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s11434-008-0494-z"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3390\/s21061981"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1126\/science.282.5389.728"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1002\/met.2097"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6484812"},{"key":"ref59","first-page":"1","article-title":"Sequence to sequence learning with neural networks","volume":"27","author":"sutskever","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","first-page":"145","article-title":"A description of the advanced research WRF model version 4","volume":"145","author":"skamarock","year":"2019"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1029\/2021GL095302"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.3390\/rs13163278"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS47720.2021.9555094"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3158888"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3390\/atmos12121596"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/ecas2021-10340"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-32483-x"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3040648"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3194522"},{"key":"ref19","first-page":"1","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume":"28","author":"shi","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref18","first-page":"281","article-title":"On the integration of optical flow and action recognition","author":"sevilla-lara","year":"2018","journal-title":"Proc German Conf Pattern Recognit"},{"key":"ref51","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref50","first-page":"25390","article-title":"Earthformer: Exploring space-time transformers for Earth system forecasting","author":"gao","year":"2022","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20711"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3159530"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref47","first-page":"1","article-title":"An image is worth 16&#x00D7;16 words: Transformers for image recognition at scale","author":"dosovitskiy","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/138"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01084"},{"key":"ref44","first-page":"1","article-title":"High fidelity video prediction with large stochastic recurrent neural networks","volume":"32","author":"villegas","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref43","first-page":"1","article-title":"Stochastic adversarial video prediction","author":"lee","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref49","first-page":"1","article-title":"Rainformer: Features extraction balanced network for radar-based precipitation nowcasting","volume":"19","author":"bai","year":"2022","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.09.060"},{"key":"ref7","article-title":"MetNet: A neural weather model for precipitation forecasting","author":"kaae s\u00f8nderby","year":"2020","journal-title":"arXiv 2003 12140"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-021-00161-5"},{"key":"ref4","first-page":"1","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","volume":"30","author":"shi","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/408"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/rs11192303"},{"key":"ref5","article-title":"FDNet: A deep learning approach with two parallel cross encoding pathways for precipitation nowcasting","author":"yan","year":"2021","journal-title":"arXiv 2105 02585"},{"key":"ref40","first-page":"1","article-title":"Very short-term rainfall prediction using ground radar observations and conditional generative adversarial networks","volume":"60","author":"kim","year":"2021","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref35","first-page":"1","article-title":"Eidetic 3D LSTM: A model for video prediction and beyond","author":"wang","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3165153"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6819"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018263"},{"key":"ref31","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref30","article-title":"FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators","author":"pathak","year":"2022","journal-title":"arXiv 2202 11214"},{"key":"ref33","article-title":"Machine learning for precipitation nowcasting from radar images","author":"agrawal","year":"2019","journal-title":"arXiv 1912 12132"},{"key":"ref32","first-page":"1","article-title":"Convective precipitation nowcasting using U-Net model","volume":"60","author":"han","year":"2021","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03854-z"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3177625"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3198222"},{"key":"ref24","first-page":"1","article-title":"PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs","volume":"30","author":"wang","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00937"},{"key":"ref25","first-page":"5123","article-title":"PredRNN++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning","author":"wang","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2926776"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2103946"},{"key":"ref63","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3045007"},{"key":"ref66","first-page":"11365","article-title":"Do RNN and LSTM have long memory?","author":"zhao","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref21","first-page":"1","article-title":"Advancing radar nowcasting through deep transfer learning","volume":"60","author":"han","year":"2021","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref28","article-title":"MS-RNN: A flexible multi-scale framework for spatiotemporal predictive learning","author":"ma","year":"2022","journal-title":"arXiv 2206 03010"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01518"},{"key":"ref29","first-page":"1","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2013","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref60","first-page":"1174","article-title":"Stochastic video generation with a learned prior","author":"denton","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1080\/02693799008941549"},{"key":"ref61","author":"larvor","year":"2020","journal-title":"MeteoNet An Open Reference Weather Dataset by Meteo-France"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10006360\/10092888.pdf?arnumber=10092888","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T18:50:15Z","timestamp":1683571815000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10092888\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":66,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2023.3264545","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}