ML-Down
Title: ML-Down
SNIC Project: Berzelius-2022-138
Project Type: LiU Berzelius
Principal Investigator: Kristofer Krus <kristofer.krus@smhi.se>
Affiliation: SMHI
Duration: 2022-06-27 – 2023-01-01
Classification: 10501
Keywords:

Abstract

The goal of this project is to use deep learning to perform statistical downscaling of simulated climate data, from a global grid with a large distance between discretization points to a regional grid with a smaller distance between discretization points. We plan to start by basing the neural network on the following papers: * Configuration and intercomparison of deep learning neural models for statistical downscaling * Downscaling Multi-Model Climate Projection Ensembles with Deep Learning (DeepESD): Contribution to CORDEX EUR-44 We then plan to make further improvements upon this work. If we can successfully do this, we expect that we can substitute this form of statistical downscaling for some of the dynamic downscaling that we do today; the latter basically requires a lot of extra simulation, and is much more computationally expensive than the former. We therefore expect to be able to reduce the amount of computation that we need to do by a significant amount.