{"type":"ScholarlyArticle","title":"WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction","authors":[{"name":"Binqing Wu","slug":"binqing-wu","given":"Binqing","family":"Wu","affiliation":"DAMO Academy , Alibaba Group; College of Computer Science and Technology , Zhejiang University"},{"name":"Weiqi Chen","slug":"weiqi-chen","given":"Weiqi","family":"Chen","affiliation":"DAMO Academy , Alibaba Group"},{"name":"Wengwei Wang","slug":"wengwei-wang","given":"Wengwei","family":"Wang","affiliation":"DAMO Academy , Alibaba Group"},{"name":"Bingqing Peng","slug":"bingqing-peng","given":"Bingqing","family":"Peng","affiliation":"DAMO Academy , Alibaba Group"},{"name":"Liang Sun","slug":"liang-sun","given":"Liang","family":"Sun","affiliation":"DAMO Academy , Alibaba Group"},{"name":"Ling Chen","slug":"ling-chen","given":"Ling","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University"}],"abstract":"Due to insufficient local area information, numerical weather prediction (NWP) may yield biases for specific areas. Previous studies correct biases mainly by employing handcrafted features or applying data-driven methods intuitively, overlooking the complicated dependencies between weather factors and between areas. To address this issue, we propose WeatherGNN, a local NWP bias-correction method that utilizes Graph Neural Networks (GNNs) to exploit meteorological dependencies and spatial dependencies under the guidance of domain knowledge. Specifically, we introduce a factor GNN to capture area-specific meteorological dependencies adaptively based on spatial heterogeneity and a fast hierarchical GNN to capture dynamic spatial dependencies efficiently guided by Tobler's first and second laws of geography. Our experimental results on two real-world datasets demonstrate that WeatherGNN achieves the state-of-the-art performance, outperforming the best baseline with an average of 4.75 % on RMSE.","doi":"10.24963/ijcai.2024/269","url":"https://ijcai.org/proceedings/2024/269","pdf":"https://ijcai.org/proceedings/2024/0269.pdf","markdown":"https://ijcai.org/proceedings/2024/269.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/269","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":269,"pages":{"first":2433,"last":2441},"keywords":["Data Mining: DM: Mining spatial and/or temporal data","Data Mining: DM: Applications"],"videos":[],"references":[{"position":1,"raw":"[Anselin, 2013] Luc Anselin. 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