Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
CoRR(2024)
Abstract
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.
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