Continuous Ensemble Weather Forecasting with Diffusion models

2024-10-07

arXiv.org

AI Summary

Diffusion models for weather forecasting suffer from autoregressive rollout, high computational cost, and error accumulation at high temporal resolution. To address these issues, this paper proposes Continuous Ensemble Diffusion (CED), a parallel ensemble forecasting framework based on continuous-time modeling. CED treats meteorological fields as a continuous-time stochastic process governed by an implicit ordinary differential equation (ODE), enabling parallel generation of temporally coherent ensemble trajectories in a single forward pass—eliminating autoregressive iteration entirely. The method integrates implicit ODE solvers, ensemble probabilistic calibration, and parallel denoising, supporting arbitrary temporal resolution and flexible hybridization with conventional rollout. On global weather forecasting benchmarks, CED achieves state-of-the-art performance: it improves skill scores significantly, reduces the Continuous Ranked Probability Score (CRPS) by 12.3%, and exhibits superior spread-error consistency.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Reduces computational cost in ensemble weather forecasting
• Minimizes error accumulation in high temporal resolution
• Enables parallel generation of consistent ensemble forecasts

Innovation

• Parallel ensemble forecasting without autoregressive steps
• Combines with autoregressive rollouts for fine resolution
• Continuous Ensemble Forecasting for temporal consistency