{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:37:48Z","timestamp":1768822668702,"version":"3.49.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62441232"],"award-info":[{"award-number":["62441232"]}],"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":["62472014"],"award-info":[{"award-number":["62472014"]}],"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":["62572017"],"award-info":[{"award-number":["62572017"]}],"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":["U19B2039"],"award-info":[{"award-number":["U19B2039"]}],"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":["62502517"],"award-info":[{"award-number":["62502517"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1600903"],"award-info":[{"award-number":["2018YFB1600903"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China Meteorological Administration (CMA) Key Innovation Team","award":["CMA2022ZD10"],"award-info":[{"award-number":["CMA2022ZD10"]}]},{"name":"Weather Modification Centre (WMC) Key Innovation Team","award":["WMC2023IT03"],"award-info":[{"award-number":["WMC2023IT03"]}]},{"name":"Scientific and Technological Project of China Meteorological Administration","award":["CMAJBGS202505"],"award-info":[{"award-number":["CMAJBGS202505"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tgrs.2025.3640219","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:37:15Z","timestamp":1764873435000},"page":"1-14","source":"Crossref","is-referenced-by-count":0,"title":["Toward Nonuniformly Distributed Weather Forecasting: Adaptive Filtered Hypergraph Convolution Network"],"prefix":"10.1109","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4535-8538","authenticated-orcid":false,"given":"Jingcheng","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, the Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6650-6790","authenticated-orcid":false,"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, the Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"China Meteorological Administration Weather Modification Centre, Beijing, China"}]},{"given":"Guodong","family":"Jing","sequence":"additional","affiliation":[{"name":"China Meteorological Administration Weather Modification Centre, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0440-438X","authenticated-orcid":false,"given":"Yongli","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, the Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-1823","authenticated-orcid":false,"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, the Faculty of Information Technology, Beijing University of Technology, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1115255"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14956"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06185-3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330704"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2949180"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3186016"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2010.07.001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICACSIS.2015.7415154"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-020-00898-1"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1029\/2020MS002203"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783275"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.118777"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441731"},{"key":"ref15","article-title":"Inductive spatiotemporal graph convolutional networks for short-term quantitative precipitation forecasting","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16529"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20711"},{"key":"ref18","article-title":"FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere","author":"Ling","year":"2024","journal-title":"arXiv:2411.10191"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1126\/science.adi2336"},{"key":"ref20","article-title":"Spectral networks and locally connected networks on graphs","author":"Bruna","year":"2013","journal-title":"arXiv:1312.6203"},{"key":"ref21","first-page":"1","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Defferrard"},{"key":"ref22","first-page":"10936","article-title":"Graph convolutional network for recommendation with low-pass collaborative filters","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Yu"},{"key":"ref23","article-title":"Revisiting over-smoothing in deep GCNs","author":"Yang","year":"2020","journal-title":"arXiv:2003.13663"},{"key":"ref24","article-title":"Mitigating degree biases in message passing mechanism by utilizing community structures","author":"Hoang","year":"2023","journal-title":"arXiv:2312.16788"},{"key":"ref25","first-page":"2268","article-title":"Not too little, not too much: A theoretical analysis of graph (over) smoothing","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Keriven"},{"key":"ref26","article-title":"A survey on oversmoothing in graph neural networks","author":"Rusch","year":"2023","journal-title":"arXiv:2303.10993"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-9637-7_33"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0205"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206795"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2199502"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/366"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3206025.3206062"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298759"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2015.7177477"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2867718"},{"key":"ref36","first-page":"1509","article-title":"HyperGCN: A new method for training graph convolutional networks on hypergraphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yadati"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00057"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3072743"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3168879"},{"key":"ref40","volume-title":"Introduction to Graph and Hypergraph Theory","author":"Voloshin","year":"2009"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3337052"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1609.02907"},{"key":"ref43","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref44","article-title":"Weather2K: A multivariate spatio-temporal benchmark dataset for meteorological forecasting based on real-time observation data from ground weather stations","author":"Zhu","year":"2023","journal-title":"arXiv:2302.10493"},{"issue":"1","key":"ref45","first-page":"5","article-title":"Weather forecasting using ANFIS and ARIMA MODELS","volume":"51","author":"Tekta\u015f","year":"2010","journal-title":"Environ. Res."},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.07.069"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref48","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2017","journal-title":"arXiv:1709.04875"},{"key":"ref49","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2017","journal-title":"arXiv:1707.01926"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119580"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7191549"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/36\/10807682\/11278451.pdf?arnumber=11278451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T10:24:05Z","timestamp":1766053445000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11278451\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2025.3640219","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}