Assessing the real-world economic value of weather forecasts under compounding extremes: a decision-specific framework

This study proposes a flexible framework to assess the economic value of weather forecasts, with penalty functions that explicitly account for compounding losses as well as declining user trust in cases of repeated false alarms. In addition, the framework allows for varying cost—loss ratios to represent heterogeneous prevention costs and vulnerability structures. The framework is applied to cities exposed to weather-related natural hazards, comparing the relative economic value of leading physics-based and data-driven forecasting systems from the European Centre for Medium-Range Weather Forecasts.

The value of forecasts is highly sensitive to assumptions about compounding losses, penalty structures, and prevention costs, which often substantially alter conclusions drawn from meteorological skill alone. For instance, in some cities in Southern Europe, the higher sensitivity of the physics-based Integrated Forecast System high-resolution model (IFS HRES) makes it better suited when protection costs are small relative to potential losses, while the higher specificity of the data-driven Artificial Intelligence Forecasting System (AIFS) makes it better when protection costs are higher. These findings underscore the importance of evaluating economic value under realistic risk scenarios to ensure that improvements in predictive accuracy translate into meaningful societal and economic benefits.