Skip to yearly menu bar Skip to main content


Poster

Continuous Ensemble Weather Forecasting with Diffusion models

Martin Andrae · Tomas Landelius · Joel Oskarsson · Fredrik Lindsten

Hall 3 + Hall 2B #29
[ ]
Wed 23 Apr 7 p.m. PDT — 9:30 p.m. PDT

Abstract:

Weather forecasting has seen a shift in methods from numerical simulations to data-driven systems. While initial research in the area focused on deterministic forecasting, recent works have used diffusion models to produce skillful ensemble forecasts. These models are trained on a single forecasting step and rolled out autoregressively. However, they are computationally expensive and accumulate errors for high temporal resolution due to the many rollout steps. We address these limitations with Continuous Ensemble Forecasting, a novel and flexible method for sampling ensemble forecasts in diffusion models. The method can generate temporally consistent ensemble trajectories completely in parallel, with no autoregressive steps. Continuous Ensemble Forecasting can also be combined with autoregressive rollouts to yield forecasts at an arbitrary fine temporal resolution without sacrificing accuracy. We demonstrate that the method achieves competitive results for global weather forecasting with good probabilistic properties.

Live content is unavailable. Log in and register to view live content