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arXiv · 2610.00782

Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?

Colin Aitken·Michael K. Tippett·Pedram Hassanzadeh·Katherine Kowal·Rendani Mbuvha·John H. Marsham·Shruti Nath·Ousmane Ndiaye·Douglas J. Parker·Caroline M Wainwright·Michael Kremer·William R. Boos
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Abstract

Artificial-intelligence weather prediction (AIWP) models have made it possible to produce high-quality tailored forecasts with limited computational resources. This advance has the potential to benefit hundreds of millions of farmers in low- and middle-income countries who lack access to forecasts of critical weather phenomena. However, it can be difficult for key stakeholders to evaluate forecast quality, risking a "race to the bottom" as cheap but low-quality forecasts crowd out forecasts that would benefit farmers. We propose a set of principles and protocols for evaluating agriculturally-relevant forecasts as a starting point for standards that would let forecasters credibly convey their forecasts' quality.

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Colin Aitken, Michael K. Tippett, Pedram Hassanzadeh, Katherine Kowal, Rendani Mbuvha, John H. Marsham, Shruti Nath, Ousmane Ndiaye, Douglas J. Parker, Caroline M Wainwright, Michael Kremer, William R. Boos. 2026-09-30. Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?. https://arxiv.org/abs/2610.00782

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