DL4DS -- Deep Learning for empirical DownScaling

TL;DR

DL4DS is a Python library that leverages deep neural networks for efficient statistical downscaling of climate data, enabling better local and regional climate predictions from global models.

Contribution

The paper introduces DL4DS, a flexible framework for training and comparing deep learning models for climate downscaling, with a focus on robustness and versatility.

Findings

Demonstrated DL4DS on air quality data over the western Mediterranean.

Showcased the library's ability to facilitate comparative studies of neural network architectures.

Validated the effectiveness of deep learning approaches for climate data downscaling.

Abstract

A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution which can be prohibitive due to long model runtimes. On the other hand, statistical downscaling techniques present an alternative approach for learning links between the large- and local-scale climate in a more efficient way. A large number of deep neural network-based approaches for statistical downscaling have been proposed in recent years, mostly based on convolutional architectures developed for computer vision and super-resolution tasks. This paper presents DL4DS, Deep Learning for empirical DownScaling, a python library that implements a wide variety of state-of-the-art and novel algorithms for downscaling gridded Earth Science data with deep neural networks. DL4DS has been designed with the goal of providing a general framework for training convolutional neural networks with configurable architectures and learning strategies to facilitate the conduction of comparative and ablation studies in a robust way. We showcase the capabilities of DL4DS on air quality CAMS data over the western Mediterranean area. The DL4DS library can be found in this repository: https://github.com/carlos-gg/dl4ds