Weather forecasting based on AI models is now part of our operational and research landscape. AI-based weather forecasts show improved skill with verification measures such as the root mean squared error when compared with NWP models. However, proper and in-depth assessment of strengths, weaknesses, and properties of these models is still ongoing. This session aims to gather contributions advancing the assessment of AI-based weather forecasts.

This session welcomes contributions on the following topics with applications to AI weather models:

Benchmarking activities (e.g. datasets, intercomparison projects, comparison with NWP forecasts)
Verification methodology (e.g. spatial verification methods, scoring rules, or innovative approaches)
Diagnostics of forecast realism and potential forecast artifacts
Forecasting extreme events, predictability, and other properties (e.g. fairness)
Interpretability of AI weather models, e.g. XAI methods.

Contributions covering theoretical, methodological, applied, or operational aspects are equally welcome.