A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling
<|reference_start|>A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling: 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.<|reference_end|>
@article{gonzález-abad2024a,
    title={A Likelihood-Based Generative Approach for Spatially Consistent
  Precipitation Downscaling},
    author={Jose Gonz'alez-Abad},
    journal={arXiv preprint arXiv:2407.04724},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04724},
    primaryClass={physics.ao-ph cs.LG stat.ML}
}