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arXiv Machine Learning By Yang Xu, Dibyajyoti Chakraborty, Haiwen Guan, Sen Wang, Romit Maulik

Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces

Read the original on arXiv Machine Learning →

The paper presents a method for improving atmospheric state reconstructions by conditioning a pretrained diffusion model on heterogeneous in‑situ observations. It introduces composable interfaces that transform sparse radiosonde, clustered aircraft, and dense surface‑station data into likelihood factors, allowing these diverse sources to guide posterior sampling consistently. Using 2019 data to build the interfaces and evaluating them in 2020, the combined R+A+S approach reduces RMSE by 9.24% and improves CRPS compared to using radiosondes alone, demonstrating a modular way to incorporate varied observations without retraining the model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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