DABench: A Benchmark Dataset for Data-Driven Weather Data Assimilation
<|reference_start|>DABench: A Benchmark Dataset for Data-Driven Weather Data Assimilation: Recent advancements in deep learning (DL) have led to the development of several Large Weather Models (LWMs) that rival state-of-the-art (SOTA) numerical weather prediction (NWP) systems. Up to now, these models still rely on traditional NWP-generated analysis fields as input and are far from being an autonomous system. While researchers are exploring data-driven data assimilation (DA) models to generate accurate initial fields for LWMs, the lack of a standard benchmark impedes the fair evaluation among different data-driven DA algorithms. Here, we introduce DABench, a benchmark dataset utilizing ERA5 data as ground truth to guide the development of end-to-end data-driven weather prediction systems. DABench contributes four standard features: (1) sparse and noisy simulated observations under the guidance of the observing system simulation experiment method; (2) a skillful pre-trained weather prediction model to generate background fields while fairly evaluating the impact of assimilation outcomes on predictions; (3) standardized evaluation metrics for model comparison; (4) a strong baseline called the DA Transformer (DaT). DaT integrates the four-dimensional variational DA prior knowledge into the Transformer model and outperforms the SOTA in physical state reconstruction, named 4DVarNet. Furthermore, we exemplify the development of an end-to-end data-driven weather prediction system by integrating DaT with the prediction model. Researchers can leverage DABench to develop their models and compare performance against established baselines, which will benefit the future advancements of data-driven weather prediction systems. The code is available on this Github repository and the dataset is available at the Baidu Drive.<|reference_end|>
@article{wang2024a,
    title={A Benchmark for AI-based Weather Data Assimilation},
    author={Wuxin Wang, Weicheng Ni, Tao Han, Taikang Yuan, Xiaoyong Li, Lei Bai,
  Boheng Duan, and Kaijun Ren},
    journal={arXiv preprint arXiv:2408.11438},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11438},
    primaryClass={cs.LG cs.CV physics.ao-ph}
}