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arXiv · 2509.09052

MoWE : A Mixture of Weather Experts

Dibyajyoti Chakraborty·Romit Maulik·Peter Harrington·Dallas Foster·Mohammad Amin Nabian·Sanjay Choudhry
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Abstract

Data-driven weather models have recently achieved state-of-the-art performance, yet progress has plateaued in recent years. This paper introduces a Mixture of Experts (MoWE) approach as a novel paradigm to overcome these limitations, not by creating a new forecaster, but by optimally combining the outputs of existing models. The MoWE model is trained with significantly lower computational resources than the individual experts. Our model employs a Vision Transformer-based gating network that dynamically learns to weight the contributions of multiple "expert" models at each grid point, conditioned on forecast lead time. This approach creates a synthesized deterministic forecast that is more accurate than any individual component in terms of Root Mean Squared Error (RMSE). Our results demonstrate the effectiveness of this method, achieving up to a 10% lower RMSE than the best-performing AI weather model on a 2-day forecast horizon, significantly outperforming individual experts as well as a simple average across experts. This work presents a computationally efficient and scalable strategy to push the state of the art in data-driven weather prediction by making the most out of leading high-quality forecast models.

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Dibyajyoti Chakraborty, Romit Maulik, Peter Harrington, Dallas Foster, Mohammad Amin Nabian, Sanjay Choudhry. 2025-09-10. MoWE : A Mixture of Weather Experts. https://arxiv.org/abs/2509.09052

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