Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
<|reference_start|>Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales: Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retraining the model. They could also dramatically accelerate the costly data assimilation process used in operational regional weather models. Here, in a central US testbed, we demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather. We train an unconditional diffusion model to generate snapshots of a state-of-the-art km-scale analysis product, the High Resolution Rapid Refresh. Then, using score-based data assimilation to incorporate sparse weather station data, the model produces maps of precipitation and surface winds. The generated fields display physically plausible structures, such as gust fronts, and sensitivity tests confirm learnt physics through multivariate relationships. Preliminary skill analysis shows the approach already outperforms a naive baseline of the High-Resolution Rapid Refresh system itself. By incorporating observations from 40 weather stations, 10\% lower RMSEs on left-out stations are attained. Despite some lingering imperfections such as insufficiently disperse ensemble DA estimates, we find the results overall an encouraging proof of concept, and the first at km-scale. It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.<|reference_end|>
@article{manshausen2024generative,
    title={Generative Data Assimilation of Sparse Weather Station Observations at
  Kilometer Scales},
    author={Peter Manshausen, Yair Cohen, Jaideep Pathak, Mike Pritchard, Piyush
  Garg, Morteza Mardani, Karthik Kashinath, Simon Byrne, Noah Brenowitz},
    journal={arXiv preprint arXiv:2406.16947},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16947},
    primaryClass={cs.LG physics.ao-ph}
}