Research Posters Archive

HPC Accelerated Generative Deep Learning Approach for Creating Digital Twins of Climate Models

Authors: Johannes Meuer, Christopher Kadow, and Thomas Ludwig (German Climate Computing Centre (DKRZ)) and Claudia Timmreck (Max Planck Institute for Meteorology)

Abstract: Climate models cannot perfectly represent the complex climate system, but by running them multiple times with small variations in input parameters, it's possible to estimate uncertainties and explore different climate scenarios. Generating these ensembles demands significant computational resources and time, which can be crucial for risk assessments and decision-making. This study utilizes generative adversarial networks (GANs) and deep diffusion models (DDMs) to produce low-resolution ensemble runs trained on data provided by climate model simulations with low computational expense. Additionally, convolutional neural networks (CNNs) are employed for downscaling as well as parallelization techniques to enhance performance and reduce computation time. This approach allows for time-efficient exploration of high-resolution ensemble members, facilitating climate modeling investigations that were previously challenging due to resource constraints.

Best Poster Finalist (BP): no

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