{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sun X"],"funding":["National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["6658"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12276244"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(1)"],"pubmed_abstract":["Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrate global observations, data assimilation (DA), and physics-based models. However, further advances are increasingly constrained by high computational costs, the underutilization of vast observational datasets, and challenges in obtaining finer resolution. Recent advances in machine learning present a promising alternative, but still depend on the initial conditions generated by NWP systems. Here, we introduce FuXi Weather, a machine learning-based global forecasting system that assimilates multi-satellite data and is capable of cycling DA and forecasting. FuXi Weather generates reliable 10-day forecasts at 0.25° resolution using fewer observations than conventional NWP systems. It demonstrat"],"journal":["Nature communications"],"pubmed_title":["A data-to-forecast machine learning system for global weather."],"pmcid":["PMC12276244"],"funding_grant_id":["42341205"],"pubmed_authors":["Li H","Wu L","Huang Y","Feng J","Qi Y","Han W","Zhong X","Sun X","Xu X","Neelin JD","Chen D"],"additional_accession":[]},"is_claimable":false,"name":"A data-to-forecast machine learning system for global weather.","description":"Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrate global observations, data assimilation (DA), and physics-based models. However, further advances are increasingly constrained by high computational costs, the underutilization of vast observational datasets, and challenges in obtaining finer resolution. Recent advances in machine learning present a promising alternative, but still depend on the initial conditions generated by NWP systems. Here, we introduce FuXi Weather, a machine learning-based global forecasting system that assimilates multi-satellite data and is capable of cycling DA and forecasting. FuXi Weather generates reliable 10-day forecasts at 0.25° resolution using fewer observations than conventional NWP systems. It demonstrat","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-03-31T10:24:59.917Z","creation":"2025-08-23T03:09:08.57Z"},"accession":"S-EPMC12276244","cross_references":{"pubmed":["40683876"],"doi":["10.1038/s41467-025-62024-1"]}}