Latent Diffusion Model for Generating Ensembles of Climate Simulations
This addresses the problem of computationally expensive uncertainty quantification in climate simulations for climate scientists and modelers, representing an incremental improvement through a hybrid method.
The paper tackles the computational challenge of generating large ensembles of high-resolution climate simulations for uncertainty quantification by training a novel generative deep learning model that combines a variational autoencoder with a denoising diffusion probabilistic model. The model achieves good agreement with the original ensemble in terms of variability and can rapidly generate large ensembles with minimal memory requirements.
Obtaining accurate estimates of uncertainty in climate scenarios often requires generating large ensembles of high-resolution climate simulations, a computationally expensive and memory intensive process. To address this challenge, we train a novel generative deep learning approach on extensive sets of climate simulations. The model consists of two components: a variational autoencoder for dimensionality reduction and a denoising diffusion probabilistic model that generates multiple ensemble members. We validate our model on the Max Planck Institute Grand Ensemble and show that it achieves good agreement with the original ensemble in terms of variability. By leveraging the latent space representation, our model can rapidly generate large ensembles on-the-fly with minimal memory requirements, which can significantly improve the efficiency of uncertainty quantification in climate simulations.