Whose weather is it? A fairness framework for data-driven weather forecasting
From Earthly Machine Learning by Amirpasha
April 14, 2026 · 22 min · Season 2 · Episode 5
About this episode
The episode discusses a fairness framework for data-driven weather forecasting, highlighting disparities in AI weather model predictions across different regions.
Citation : Olivetti, L., & Messori, G. (2025). Whose weather is it? A fairness framework for data-driven weather forecasting. Environmental Research Letters, 20 , 121006. https://doi.org/10.1088/1748-9326/ae21f5 Main Takeaways: AI Weather Models Aren't Fair to Everyone : The latest generation of AI-powered weather forecasts improves predictions globally — but not equally. Using ECMWF's AIFS model as a case study, the authors show that wealthier and more densely populated areas consistently receive a higher share of forecast improvements compared to poorer and more rural regions, violating basic fairness criteria borrowed from the algorithmic fairness literature. Two Measurable Fairness Tests — Both Failed : The paper proposes two concrete criteria: statistical parity (improvement rates should be similar across income groups) and conditional independence (a region's GDP or population density should not predict whether it benefits from the new model). Across nearly all tested variables and forecast lead times, AIFS fails both tests at the 0.01 significance level — meaning the disparity is not a statistical fluke. Extreme Weather Is Where the Gap Hurts Most : For standard…
People in this episode
Host: Amirpasha
Topics covered
- AI in weather forecasting
- fairness in data
- climate equity
- extreme weather
- statistical analysis
Keywords
- weather forecasting
- AI models
- fairness criteria
- statistical parity
- conditional independence
- extreme weather
- vulnerable populations
Mentioned in this episode
Organizations: ECMWF
Books & works: Environmental Research Letters
More episodes of Earthly Machine Learning
- Aligning artificial intelligence with climate change mitigation · May 9, 2026 · 19 min
- Machine learning for the physics of climate · May 3, 2026 · 20 min
- Atmospheric Transport Modeling of CO2 With Neural Networks · April 27, 2026 · 21 min
- On the foundations of Earth foundation models · April 20, 2026 · 18 min
- Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders · March 7, 2026 · 18 min
- Green and intelligent: the role of AI in the climate transition · February 28, 2026 · 18 min
Explore listener stats, chart rankings, contacts and more on the Earthly Machine Learning podcast page.