LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting (2506.09193v1)

Abstract: Accurate probabilistic weather forecasting demands both high accuracy and efficient uncertainty quantification, challenges that overburden both ensemble numerical weather prediction (NWP) and recent machine-learning methods. We introduce LaDCast, the first global latent-diffusion framework for medium-range ensemble forecasting, which generates hourly ensemble forecasts entirely in a learned latent space. An autoencoder compresses high-dimensional ERA5 reanalysis fields into a compact representation, and a transformer-based diffusion model produces sequential latent updates with arbitrary hour initialization. The model incorporates Geometric Rotary Position Embedding (GeoRoPE) to account for the Earth's spherical geometry, a dual-stream attention mechanism for efficient conditioning, and sinusoidal temporal embeddings to capture seasonal patterns. LaDCast achieves deterministic and probabilistic skill close to that of the European Centre for Medium-Range Forecast IFS-ENS, without any explicit perturbations. Notably, LaDCast demonstrates superior performance in tracking rare extreme events such as cyclones, capturing their trajectories more accurately than established models. By operating in latent space, LaDCast reduces storage and compute by orders of magnitude, demonstrating a practical path toward forecasting at kilometer-scale resolution in real time. We open-source our code and models and provide the training and evaluation pipelines at: https://github.com/tonyzyl/ladcast.

• The paper introduces LaDCast, a novel latent diffusion framework that achieves high forecast accuracy and efficient uncertainty quantification.
• It utilizes an autoencoder to compress ERA5 data and a transformer-based diffusion model with GeoRoPE to capture seasonal and spatial dynamics.
• Results show robust performance by matching traditional models while accurately tracking extreme weather events and reducing computational costs.

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting

The paper introduces LaDCast, a novel global latent diffusion framework designed specifically for medium-range ensemble weather forecasting. This approach addresses the significant challenge of achieving high accuracy in probabilistic weather forecasting while efficiently quantifying uncertainty. Traditional ensemble numerical weather prediction (NWP) models and recent machine-learning-based methods often struggle with these demands due to computational overburden and accuracy limitations.

LaDCast leverages a latent space for forecasting, a departure from typical methods that require operating in high-dimensional physical spaces. The framework utilizes an autoencoder to compress the sprawling ERA5 reanalysis data into a compact latent representation. From this compressed space, a transformer-based diffusion model is tasked with generating sequential updates, facilitating ensemble forecasts without the need for explicit perturbations of initial conditions. The incorporation of Geometric Rotary Position Embedding (GeoRoPE) to handle Earth's spherical geometry, coupled with a dual-stream attention mechanism and sinusoidal temporal embeddings, enables LaDCast to efficiently capture seasonal patterns and handle initial and target conditions separately.

Strong numerical results highlight LaDCast's proficiency, notably matching deterministic and probabilistic skills akin to the European Centre for Medium-Range Weather Forecast IFS-ENS. The model demonstrates particularly robust performance in tracking rare extreme weather events, such as cyclones, proving more adept at capturing trajectory accuracy compared to established models. This capability of real-time forecasting at kilometer-scale resolution offered by LaDCast marks a significant computational reduction: the framework operates at reduced storage and compute requirements at orders of magnitude lower than traditional NWP systems.

Implications and Speculations

The implications of LaDCast are multifaceted, spanning practical advancements for meteorological applications and contributing to theoretical developments in AI-based forecasting. Practically, LaDCast enhances disaster preparedness and early-warning systems through precise probabilistic tracking of extreme weather trajectories. This offers tangible benefits for sectors reliant on accurate weather predictions, such as agriculture, aviation, and emergency management.

Theoretically, the adoption of latent space diffusion models opens new pathways for forecasting applications, suggesting that such frameworks could be extended to tackle more minute resolutions or assimilate real-time observational data. As the field progresses, we speculate that AI-driven methodologies in weather forecasting will become increasingly integral, offering platforms that can handle multi-variable, multi-pressure-level data efficiently.

Future developments may seek to refine latent space accuracy further, perhaps through enhanced autoencoder architectures or incorporating multimodal data sources for real-time applications. Addressing current constraints, such as the reconstruction error from deep-compression autoencoders, will be crucial for achieving higher fidelity forecasts. Furthermore, novel data assimilation techniques may allow MLWP models like LaDCast to operate seamlessly on real-time datasets, further broadening their applicability in operational contexts.

LaDCast is positioned as a promising direction for the convergence of AI and meteorology, showcasing the potential for latent diffusion modeling techniques to revolutionize ensemble forecasting on a global scale.