In the context of accelerating global climate change and increasingly frequent extreme weather events, accurate weather and climate prediction has become critically important for safeguarding human society and the natural environment.Traditional meteorological models face significant challenges in simulating complex atmospheric systems and handling high-dimensional data, limiting the accuracy and reliability of both short-term weather forecasts and long-term climate projections.In recent years, revolutionary advancements in artificial intelligence (AI), particularly in generative models, have shown remarkable potential across a range of scientific domains.Among these, diffusion models have emerged as a particularly promising approach, owing to their stable training processes, unique generative mechanisms, and superior sample quality.This paper provides a comprehensive review of diffusion models and their applications within meteorological science, focusing on their current implementation, performance characteristics, and future prospects across multiple meteorological subfields.
The review first establishes the theoretical foundations of diffusion models, detailing their core mechanisms, including the forward and reverse diffusion processes.It then explores the intrinsic connections between diffusion models and meteorological science, emphasizing shared mathematical frameworks and physical principles such as stochastic processes, Bayesian inference, and multi-scale dynamics.Building on this foundation, the paper systematically examines four key application areas where diffusion models have demonstrated particular promise.In precipitation forecasting, models such as LDCast, PreDiff, GED, and DiffCast have achieved significant improvements in generating diverse precipitation scenarios and accurately capturing extreme events.For data assimilation, approaches such as SDA, DiffDA, and SLAMS integrate heterogeneous, sparse, and noisy observational data into weather fields while significantly reducing computational costs compared to traditional methods.In spatial downscaling, models like CorrDiff, StormCast, and STVD enhance spatial resolution from coarse global climate models to fine-scale regional predictions while maintaining physical consistency and capturing local terrain effects.In weather system simulation, models such as SEEDS and GenCast have shown strong capabilities in modeling complex atmospheric dynamics and producing high-quality ensemble forecasts that reflect the inherent uncertainties in weather prediction.
Despite these advancements, several challenges remain, including high computational demands, maintaining physical consistency, ensuring long-term model stability, and improving model interpretability to foster greater trust among meteorologists.Looking ahead, the paper identifies five promising research directions:climate change scenario generation, multi-scale weather-climate joint modeling, meteorological data completion and reconstruction, linking global and regional scales, and uncertainty quantification in meteorological modeling.These emerging applications span a wide range of temporal and spatial scales, from microscale to macroscale and from short-term forecasts to long-term projections.The integration of diffusion models with traditional physical physics-based weather models holds significant potential for improving forecast accuracy, enhancing climate model resolution, and advancing extreme weather early-warning systems.As technology innovation progresses and interdisciplinary collaboration deepens, diffusion models are expected to play an increasingly critical role in meteorological research and operational applications, offering powerful new tools for understanding atmospheric phenomena and addressing the challenges of climate change.