DAMBench: A Multi-Modal Benchmark for Deep Learning-based Atmospheric Data Assimilation

2025-11-03

AI Summary

Existing deep learning data assimilation research is largely constrained by synthetic perturbed observation settings and the absence of standardized benchmarks. This paper introduces DAMBench, the first multimodal deep learning assimilation benchmark tailored for real atmospheric environments. It integrates heterogeneous observational sources—including ground-based meteorological stations, satellite remote sensing, and numerical weather prediction background fields—into a unified spatiotemporally aligned gridded dataset. Methodologically, it combines latent generative models, neural processes, and lightweight multimodal adapters within a standardized training and evaluation framework. Our contributions are threefold: (1) it fills a critical gap by establishing the first benchmark grounded in real-world, multi-source observational data; (2) it provides a reproducible, cross-modal, large-scale platform for fair model comparison; and (3) empirical results demonstrate that incorporating authentic multimodal observations substantially improves baseline model accuracy—thereby advancing data-driven assimilation toward operational deployment.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Data Assimilation is a cornerstone of atmospheric system modeling, tasked with reconstructing system states by integrating sparse, noisy observations with prior estimation. While traditional approaches like variational and ensemble Kalman filtering have proven effective, recent advances in deep learning offer more scalable, efficient, and flexible alternatives better suited for complex, real-world data assimilation involving large-scale and multi-modal observations. However, existing deep learning-based DA research suffers from two critical limitations: (1) reliance on oversimplified scenarios with synthetically perturbed observations, and (2) the absence of standardized benchmarks for fair model comparison. To address these gaps, in this work, we introduce DAMBench, the first large-scale multi-modal benchmark designed to evaluate data-driven DA models under realistic atmospheric conditions. DAMBench integrates high-quality background states from state-of-the-art forecasting systems and real-world multi-modal observations (i.e., real-world weather stations and satellite imagery). All data are resampled to a common grid and temporally aligned to support systematic training, validation, and testing. We provide unified evaluation protocols and benchmark representative data assimilation approaches, including latent generative models and neural process frameworks. Additionally, we propose a lightweight multi-modal plugin to demonstrate how integrating realistic observations can enhance even simple baselines. Through comprehensive experiments, DAMBench establishes a rigorous foundation for future research, promoting reproducibility, fair comparison, and extensibility to real-world multi-modal scenarios. Our dataset and code are publicly available at https://github.com/figerhaowang/DAMBench.

Problem

• Addressing oversimplified scenarios in deep learning-based atmospheric data assimilation
• Providing standardized benchmarks for fair comparison of DA models
• Integrating real-world multi-modal observations under realistic atmospheric conditions

Innovation

• Introduces first large-scale multi-modal benchmark DAMBench
• Integrates real-world observations with forecasting system data
• Provides unified evaluation protocols and lightweight plugin