A Likelihood-Based Generative Approach for Spatially Consistent Precipitation Downscaling
This addresses a specific issue in climate modeling for researchers, but it appears incremental as it combines existing methods.
The paper tackled the problem of spatially inconsistent projections in precipitation downscaling by fusing likelihood-based and adversarial losses, resulting in a novel generative approach that improves spatial consistency.
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.