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arXiv · 2407.04724

A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling

Jose González-Abad
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Abstract

Deep learning has emerged as a promising tool for precipitation downscaling. However, current models rely on likelihood-based loss functions to properly model the precipitation distribution, leading to spatially inconsistent projections when sampling. This work explores a novel approach by fusing the strengths of likelihood-based and adversarial losses used in generative models. As a result, we propose a likelihood-based generative approach for precipitation downscaling, leveraging the benefits of both methods.

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BibTeXRIS

Jose González-Abad. 2024-06-26. A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling. https://arxiv.org/abs/2407.04724

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