Year: 2024
Type: conference-paper
Abstract: Statistical interpolation methods used to adapt data do not adequately capture the spatiotemporal dependencies of meteorological data. This work proposes the use of Graph Convolutional Neural Networks (GCNs) for precipitation interpolation and forecasting. The experiments utilized radar data and in-situ station data. Comparing GCN interpolation with the statistical Inverse Distance Weighting (IDW) method, preliminary results indicate that GCNs exhibit better accuracy in extreme precipitation events, also showing promise in events of lesser magnitude.
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: bronze
OpenAlex ID: W4403389850