Whose weather is it? A fairness perspective on data-driven weather forecasting

Recent years have seen rapid advancements in large-scale data-driven models for weather forecasting. Several of these models can now compete with, and in some respects outperform, physics-based numerical models for medium-range forecasting. They offer significant computational savings and potential forecasting accuracy improvements approximately equivalent to a decade of progress in traditional methods. This progress has prompted announcements from weather institutes across the world about plans to integrate AI-driven models into their operational workflows in the near future.

As data-driven models become integral to operational forecasting, critical questions about fairness and equity remain. Studies reveal substantial variations in forecast quality across regions, particularly for extreme weather. Unlike physical models, the disparities in data-driven models often stem from passive design decisions, such as inductive biases and weighting schemes, which may be reassessed and changed, if needed. Moreover, ensuring equitable access to these models, along with the means to effectively utilise and improve them, is essential so that both high- and low-income countries can share in their benefits.

This work explores fairness in data-driven weather forecasting, with a focus on outcome-based perspectives. We begin by defining fairness from both process and outcome viewpoints. We then analyse the performance of current data-driven models across different regions and socio-economic groups globally. Our findings reveal significant disparities that may exacerbate pre-existing socio-economic and climate-related vulnerabilities. To address these challenges, we advocate for a deliberate focus on fairness and equity in data-driven model development, emphasising the importance of active design choices to promote equitable outcomes.