Statistical methods in an era of AI in geosciences: forecasting, verification, and interpretability

Co-organized by AS5/HS13

Convener: Maxime TaillardatECSECS | Co-conveners: Philine BommerECSECS, Jieyu ChenECSECS, Sebastian Lerch, Romain PicECSECS, Sándor Baran, Stéphane Vannitsem

This session explores forecasting in geosciences using statistical methods. Ranging from linear regression to the most advanced machine learning (ML) or artificial intelligence (AI) algorithms, the session welcomes all contributions developing and/or using these tools for various applications such as AI-based numerical weather prediction and nowcasting, time series forecasting in geosciences, forecast blending, statistical post-processing, and downscaling.
This session also welcomes contributions advancing the assessment of AI-based forecasts. Aiming at a proper and in-depth assessment of the strengths and weaknesses of AI-based models, the session will report on benchmarking activities, new verification methodologies, diagnostics of forecast realism, and progress in the interpretability of AI-based models.

This session is designed to foster interdisciplinary discussions among geoscientists from meteorology, climate, hydrology, and other related communities, promoting the use of statistical methods in forecasting, verification, and beyond.