Spatio-temporal graph neural networks to improve precipitation forecasts from numerical models

• Published in: Soft computing (Berlin, Germany) Vol. 29; no. 9-10; pp. 4481 - 4494
• Main Authors: Yousaf, Umair, De Rango, Alessio, Furnari, Luca, D’Ambrosio, Donato, Senatore, Alfonso, Mendicino, Giuseppe
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Nature B.V 01.05.2025
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Climate change; Error correction; Graph neural networks; Machine learning; Meteorological data; Neural networks; Numerical models; Numerical weather forecasting; Precipitation; Spatial resolution; Weather forecasting; Weather stations; United States > US; China

Weather forecasting has always been a fascinating challenge due to the randomness, difficulty and multiple intersections in the spatiotemporal distribution of several atmospheric processes. The classical approach for weather forecasting is based on numerical weather prediction (NWP). However, this a...