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Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces
Atmospheric observations are sparse, heterogeneous, and unevenly distributed, whereas many generative atmospheric models learn distributions over regularly gridded multivariate states. Once pretrained, diffusion models can supply atmospheric priors that can be combined with observation-derived likel
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