Whose weather is it? A fairness framework for data-driven weather forecasting

Abstract As data-driven weather forecasting models increasingly come in operational use, questions of fairness and equitable access to forecast improvements are gaining urgency. This paper introduces a conceptual framework for evaluating outcome-based fairness in global data-driven forecasts, drawing on principles from the algorithmic fairness literature. Specifically, we focus on two criteria: statistical parity (i.e. comparable improvements across protected groups) and conditional independence (i.e. no dependence of improvements on protected variables). Using ECMWF’s AIFS model as a case study and IFS HRES as a baseline, we assess whether forecast improvements are equitably distributed across different income groups and population densities. We find that although AIFS provides substantial overall improvements in forecast skill, these gains are unevenly distributed: on average, wealthier and more densely populated areas are more likely to experience forecast improvements, violating group fairness and conditional independence criteria. We conclude by discussing how fairness-aware loss functions could be incorporated into data-driven weather forecasting systems and argue for a broader integration of fairness considerations into model development and evaluation.