RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
arXiv:2605.24945v1 Announce Type: new Abstract: Accurate evaluation of weather forecasting models is critical for their reliable deployment in real-world applications. However, existing benchmarks predominantly rely on reanalysis products such as ERA5, which are generated through delayed data assimilation and do not reflect the constraints of real-time operational forecasting, thereby resulting in a systematic mismatch between benchmark performance and real-world forecasting. In this work, we introduce RealBench, a next-generation benchmark for AI weather forecasting that emphasizes realistic
The proliferation of AI in scientific domains, particularly for complex systems like weather modeling, necessitates robust and realistic evaluation methods to ensure practical reliability.
This benchmark directly addresses a critical gap in the development of AI weather forecasting, moving it from theoretical performance to operational utility, which has significant implications for disaster preparedness, agriculture, and energy.
The shift from reanalysis products to real-time operational conditions means that AI weather models will now be evaluated against metrics that truly reflect their real-world applicability and reliability challenges.
- · AI weather model developers
- · Emergency services
- · Agricultural sector
- · Energy producers
- · AI models that perform poorly in operational settings
- · Traditional numerical weather prediction models (potentially, if AI proves super
More accurate and reliable AI-driven weather predictions become available for operational use.
Improved forecasting leads to better resource allocation and disaster mitigation strategies globally.
Economic savings and increased resilience in sectors highly dependent on weather, potentially influencing geopolitical stability.
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Read at arXiv cs.LG