Assessing The Real-World Economic Value of Weather Forecasts under Compounding Extremes: A Decision-Specific Framework

Leonardo Olivetti, Gabriele Messori, Paolo Avner and Stephane Hallegatte

No 11407, Policy Research Working Paper Series from The World Bank

Abstract: Assessing the real-world economic value of weather forecasts remains challenging, particularly in the context of high-impact extreme events. Although meteorological skill has improved substantially in recent years—driven by steady advances in physics-based models and impressive breakthroughs in artificial intelligence-based forecasting—operational evaluations still focus primarily on standard skill metrics, with limited consideration of how improvements in meteorological skill translate into economic value. 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.

Date: 2026-06-02
References: Add references at CitEc
Citations:

Downloads: (external link)
https://documents.worldbank.org/curated/en/0998360 ... e9f-95c258fb251c.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (https://documents.worldbank.org/curated/en/099836006022622606/pdf/IDU-3d41fc50-bb68-481a-ae9f-95c258fb251c.pdf [302 Found]--> http://documents1.worldbank.org/curated/en/099836006022622606/pdf/IDU-3d41fc50-bb68-481a-ae9f-95c258fb251c.pdf [301 Moved Permanently]--> https://documents1.worldbank.org/curated/en/099836006022622606/pdf/IDU-3d41fc50-bb68-481a-ae9f-95c258fb251c.pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wbk:wbrwps:11407

Access Statistics for this paper

More papers in Policy Research Working Paper Series from The World Bank 1818 H Street, N.W., Washington, DC 20433. Contact information at EDIRC.
Bibliographic data for series maintained by Roula I. Yazigi ().