Understanding Deep Learning Decisions in Statistical Downscaling Models
Jorge Baño‐Medina
Abstract
Deep learning (DL) models are progressively being applied to climate applications due to their ability to learn complex nonlinear spatiotemporal patterns, typically present in the atmosphere. In particular, deep learning has landed on the downscaling field, providing high-resolution climate change projections crucial for sectorial applications. Despite their merits, they are still seen as “black boxes” generating distrust among the climate community and thus limiting their use in real applications. Therefore, there is a need to develop techniques that unravel the knowledge hidden in the neural models to 1) gain understanding about their decisions and 2) make these models more reliable to the community. In this study, we adopt a technique used in computer vision to visualize the decisions, to convolutional-based downscaling models. The results show comprehensive links learned by the network connecting the large-scale to the local-scale and prove the implicit feature selection that occurs within the hidden layers. To our knowledge, this is the first study that properly assesses a methodology to unravel the “black box”, in particular information concerning the predictor-predictand link, in a downscaling application.