A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling

Autor:	González-Abad, Jose
Rok vydání:	2024
Předmět:	Physics - Atmospheric and Oceanic Physics Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at ICML 2024 Machine Learning for Earth System Modeling workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.04724 Zobrazit plný text záznamu View this record from Arxiv