{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:18:08Z","timestamp":1763644688660,"version":"3.41.0"},"reference-count":59,"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":["62125102"],"award-info":[{"award-number":["62125102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2022ZD0160401"],"award-info":[{"award-number":["2022ZD0160401"]}]},{"name":"Beijing Natural Science Foundation","award":["JL23005"],"award-info":[{"award-number":["JL23005"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2022ZD0160101"],"award-info":[{"award-number":["2022ZD0160101"]}]},{"DOI":"10.13039\/501100002885","name":"Youth Innovation Team of China Meteorological Administration","doi-asserted-by":"publisher","award":["CMA2024QN02"],"award-info":[{"award-number":["CMA2024QN02"]}],"id":[{"id":"10.13039\/501100002885","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tgrs.2025.3578701","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T17:44:19Z","timestamp":1749663859000},"page":"1-14","source":"Crossref","is-referenced-by-count":1,"title":["Topographic Informed Kolmogorov-Arnold Neural Interpolator for Downscaling and Correcting Meteorological Fields From In Situ Observations"],"prefix":"10.1109","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2323-6078","authenticated-orcid":false,"given":"Zili","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Aerospace Intelligent Science and Technology, School of Astronautics, and the Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beihang University, Beijing, China"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"given":"Lei","family":"Bai","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"given":"Wenyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1774-552X","authenticated-orcid":false,"given":"Zhengxia","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Aerospace Intelligent Science and Technology, School of Astronautics, and the Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4772-3172","authenticated-orcid":false,"given":"Zhenwei","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Aerospace Intelligent Science and Technology, School of Astronautics, and the Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beihang University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06185-3"},{"key":"ref2","article-title":"FengWu: Pushing the skillful global medium-range weather forecast beyond 10 days lead","author":"Chen","year":"2023","journal-title":"arXiv:2304.02948"},{"key":"ref3","article-title":"FengWu-GHR: Learning the kilometer-scale medium-range global weather forecasting","author":"Han","year":"2024","journal-title":"arXiv:2402.00059"},{"key":"ref4","first-page":"54255","article-title":"Towards a self-contained data-driven global weather forecasting framework","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Xiao"},{"key":"ref5","first-page":"19798","article-title":"DiffDA: A diffusion model for weather-scale data assimilation","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Huang"},{"key":"ref6","article-title":"ADAF: An artificial intelligence data assimilation framework for weather forecasting","author":"Xiang","year":"2024","journal-title":"arXiv:2411.16807"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03854-z"},{"key":"ref8","first-page":"15809","article-title":"CasCast: Skillful high-resolution precipitation nowcasting via cascaded modelling","volume-title":"Proc. 41st Int. Conf. Mach. Learn. (ICML)","author":"Gong"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/nature14956"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/qj.3803"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1002\/wcc.535"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-025-08680-1"},{"key":"ref13","article-title":"Verification against in-situ observations for data-driven weather prediction","author":"Ramavajjala","year":"2023","journal-title":"arXiv:2305.00048"},{"key":"ref14","article-title":"Local off-grid weather forecasting with multi-modal Earth observation data","author":"Yang","year":"2024","journal-title":"arXiv:2410.12938"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00667-9"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2023.12.011"},{"key":"ref17","article-title":"Satellite observations guided diffusion model for accurate meteorological states at arbitrary resolution","author":"Tu","year":"2025","journal-title":"arXiv:2502.07814"},{"key":"ref18","article-title":"OMG-HD: A high-resolution AI weather model for end-to-end forecasts from observations","author":"Zhao","year":"2024","journal-title":"arXiv:2412.18239"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3447073"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10862-8"},{"key":"ref21","article-title":"KAN: Kolmogorov\u2013Arnold networks","author":"Liu","year":"2024","journal-title":"arXiv:2404.19756"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1175\/WAF-D-21-0151.1"},{"key":"ref23","article-title":"How far are today\u2019s time-series models from real-world weather forecasting applications?","author":"Han","year":"2024","journal-title":"arXiv:2406.14399"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2022.3145854"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3218921"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3522293"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2024.3404604"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3348464"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2025.3560455"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098004"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1002\/met.1961"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1175\/JAMC-D-20-0057.1"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1002\/qj.4596"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3032790"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1029\/2022MS003120"},{"key":"ref36","first-page":"1","article-title":"Precipitation downscaling with spatiotemporal video diffusion","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Srivastava"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1038\/s41612-024-00679-1"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/s43247-025-02042-5"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1029\/2024JH000260"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-025-00980-5"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3496895"},{"key":"ref42","article-title":"WSSM: Geographic-enhanced hierarchical state-space model for global station weather forecast","author":"Yang","year":"2025","journal-title":"arXiv:2501.11238"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2010.10.024"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1038\/s41612-023-00486-0"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/269"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3743128"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2024.117397"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i5.32491"},{"key":"ref49","article-title":"HyperKAN: Kolmogorov-arnold networks make hyperspectral image classificators smarter","author":"Lobanov","year":"2024","journal-title":"arXiv:2407.05278"},{"key":"ref50","article-title":"Kolmogorov\u2013Arnold networks for time series: Bridging predictive power and interpretability","author":"Xu","year":"2024","journal-title":"arXiv:2406.02496"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4825654"},{"key":"ref52","article-title":"KAN 2.0: Kolmogorov\u2013Arnold networks meet science","author":"Liu","year":"2024","journal-title":"arXiv:2408.10205"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14505"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"ref56","article-title":"TimeKAN: KAN-based frequency decomposition learning architecture for long-term time series forecasting","author":"Huang","year":"2025","journal-title":"arXiv:2502.06910"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3390\/geosciences8020063"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-025-08897-0"},{"key":"ref59","article-title":"GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations","author":"Alexe","year":"2024","journal-title":"arXiv:2412.15687"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/36\/10807682\/11030658.pdf?arnumber=11030658","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T17:39:00Z","timestamp":1750354740000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11030658\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2025.3578701","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"type":"print","value":"0196-2892"},{"type":"electronic","value":"1558-0644"}],"subject":[],"published":{"date-parts":[[2025]]}}}