Featured Preprint: Continuous Ensemble Weather Forecasting with Diffusion Models
Authors: Martin Andrae, Tomas Landelius, Joel Oskarsson, Fredrik Lindsten
Traditional numerical weather prediction (NWP) systems rely on complex physical models and extensive computational resources. Diffusion models have been proposed as a promising direction. This paper addresses some of the limitations of weather forecasting using diffusion models; they are computationally expensive and accumulate errors for high temporal resolution due to the many rollout steps.
Continuous Ensemble Forecasting: The authors propose a method that generates temporally consistent ensemble trajectories in parallel, eliminating the need for autoregressive steps. This approach addresses the computational inefficiencies and error accumulation associated with high temporal resolution forecasts.
Integration with Autoregressive Rollouts: The method can be combined with autoregressive rollouts to produce forecasts at arbitrarily fine temporal resolutions without compromising accuracy.
Probabilistic Modeling: By employing diffusion models, the approach captures the inherent uncertainties in weather forecasting, providing a probabilistic framework that enhances the reliability of predictions.
This research signifies a shift towards more efficient and accurate weather forecasting methods, which are crucial for climate modeling and decision-making. The ability to generate ensemble forecasts with reduced computational resources aligns with the goals of Climate Change AI, promoting the development of scalable and effective tools to address climate challenges.