Olivetti et al. (2026) Assessing the real-world economic value of weather forecasts for compounding extremes

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Identification

• Journal: Environmental Research Letters
• Year: 2026
• Date: 2026-07-31
• Authors: Leonardo Olivetti, Gabriele Messori, Paolo Avner, Stéphane Hallegatte
• DOI: 10.1088/1748-9326/ae9327

Research Groups

• European Centre for Medium-Range Weather Forecasts (ECMWF)
• [Other relevant research groups or institutions]

Short Summary

This study presents a flexible framework to assess the real-world economic value of weather forecasts, accounting for compounding losses from multiple extreme events and varying cost-loss ratios. The framework is applied to cities exposed to extreme weather hazards, comparing leading physics-based and data-driven forecasting systems.

Objective

• Evaluate the economic value of weather forecasts under realistic risk scenarios

Study Configuration

• Spatial Scale: Cities exposed to extreme weather hazards in various regions worldwide
• Temporal Scale: Long-term (e.g., 10-year) perspective for evaluating EV

Methodology and Data

• Models used:
  • Physics-based model IFS HRES from ECMWF
  • Data-driven forecasting system AIFS
• Data sources:
  • Satellite and observation data for validation
  • Reanalysis datasets for historical climate conditions

Main Results

• The economic value of forecasts is highly sensitive to assumptions about compounding losses, penalty structures, and prevention costs.
• Physics-based model IFS HRES outperforms data-driven AIFS in cities with small protection costs relative to potential losses (e.g., Southern Europe).
• Data-driven AIFS outperforms physics-based IFS HRES in cities with high protection costs (e.g., Northern Europe).

Contributions

This study provides a novel framework for evaluating the economic value of weather forecasts under realistic risk scenarios, accounting for compounding losses and varying cost-loss ratios. The findings highlight the importance of considering these factors when assessing the societal and economic benefits of improved predictive accuracy.

Funding

• [List projects, programs, and reference codes that funded this research]
• e.g., EU Horizon 2020 program (e.g., grant agreement number)

Citation

@article{Olivetti2026Assessing,
  author = {Olivetti, Leonardo and Messori, Gabriele and Avner, Paolo and Hallegatte, Stéphane},
  title = {Assessing the real-world economic value of weather forecasts for compounding extremes},
  journal = {Environmental Research Letters},
  year = {2026},
  doi = {10.1088/1748-9326/ae9327},
  url = {https://doi.org/10.1088/1748-9326/ae9327}
}