OmniCast: A Masked Latent Diffusion Model for Weather Forecasting Across Time Scales

TL;DR

OmniCast introduces a scalable probabilistic weather forecasting model that unifies multiple timescales using a latent diffusion approach, outperforming existing methods especially at longer horizons.

Contribution

The paper presents OmniCast, a novel latent diffusion model combining VAE and transformer architectures for improved long-term weather forecasting across various timescales.

Findings

Achieves state-of-the-art performance at subseasonal-to-seasonal scales.

Runs 10-20 times faster than comparable probabilistic models.

Capable of generating stable weather forecasts up to 100 years ahead.

Abstract

Accurate weather forecasting across time scales is critical for anticipating and mitigating the impacts of climate change. Recent data-driven methods based on deep learning have achieved significant success in the medium range, but struggle at longer subseasonal-to-seasonal (S2S) horizons due to error accumulation in their autoregressive approach. In this work, we propose OmniCast, a scalable and skillful probabilistic model that unifies weather forecasting across timescales. OmniCast consists of two components: a VAE model that encodes raw weather data into a continuous, lower-dimensional latent space, and a diffusion-based transformer model that generates a sequence of future latent tokens given the initial conditioning tokens. During training, we mask random future tokens and train the transformer to estimate their distribution given conditioning and visible tokens using a per-token diffusion head. During inference, the transformer generates the full sequence of future tokens by iteratively unmasking random subsets of tokens. This joint sampling across space and time mitigates compounding errors from autoregressive approaches. The low-dimensional latent space enables modeling long sequences of future latent states, allowing the transformer to learn weather dynamics beyond initial conditions. OmniCast performs competitively with leading probabilistic methods at the medium-range timescale while being 10x to 20x faster, and achieves state-of-the-art performance at the subseasonal-to-seasonal scale across accuracy, physics-based, and probabilistic metrics. Furthermore, we demonstrate that OmniCast can generate stable rollouts up to 100 years ahead. Code and model checkpoints are available at https://github.com/tung-nd/omnicast.