SynWeather: Weather Observation Data Synthesis across Multiple Regions and Variables via a General Diffusion Transformer

2025-11-11

AI Summary

Existing meteorological data synthesis methods are largely confined to deterministic, single-variable, single-region modeling, failing to capture inter-variable complementarity and inter-regional consistency while often producing oversmoothed outputs. To address these limitations, we introduce SynWeather—the first benchmark dataset enabling joint multi-region, multi-variable meteorological modeling—and propose SynWeatherDiff, a diffusion-based generative model built upon the Diffusion Transformer architecture. SynWeatherDiff integrates heterogeneous observational modalities—including radar reflectivity, precipitation, visible-light imagery, and microwave brightness temperatures—to enable high-resolution, probabilistic weather data generation. Extensive experiments demonstrate that SynWeatherDiff significantly outperforms both domain-specific and general-purpose baseline models on multi-region synthesis tasks. It effectively preserves the spatial structure and dynamic evolution of weather systems, establishing a novel paradigm for high-fidelity synthetic data generation in complex meteorological scenarios.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

With the advancement of meteorological instruments, abundant data has become available. Current approaches are typically focus on single-variable, single-region tasks and primarily rely on deterministic modeling. This limits unified synthesis across variables and regions, overlooks cross-variable complementarity and often leads to over-smoothed results. To address above challenges, we introduce SynWeather, the first dataset designed for Unified Multi-region and Multi-variable Weather Observation Data Synthesis. SynWeather covers four representative regions: the Continental United States, Europe, East Asia, and Tropical Cyclone regions, as well as provides high-resolution observations of key weather variables, including Composite Radar Reflectivity, Hourly Precipitation, Visible Light, and Microwave Brightness Temperature. In addition, we introduce SynWeatherDiff, a general and probabilistic weather synthesis model built upon the Diffusion Transformer framework to address the over-smoothed problem. Experiments on the SynWeather dataset demonstrate the effectiveness of our network compared with both task-specific and general models.

Problem

• Limits unified synthesis across weather variables and geographical regions
• Overlooks cross-variable complementarity in meteorological data analysis
• Produces over-smoothed results using current deterministic modeling approaches

Innovation

• Diffusion Transformer for probabilistic weather synthesis
• Unified multi-region multi-variable data generation
• Addresses over-smoothed results in weather modeling