Computationally-Efficient Climate Predictions using Multi-Fidelity Surrogate Modelling

• URL: http://arxiv.org/abs/2109.07468v1
• Date: Tue, 3 Aug 2021 15:26:42 GMT
• Title: Computationally-Efficient Climate Predictions using Multi-Fidelity Surrogate Modelling
• Authors: Ben Hudson, Frederik Nijweide, Isaac Sebenius
• Abstract summary: We investigate the potential of multi-fidelity surrogate modelling as a way to produce high-fidelity climate predictions at low cost. Our model combines the predictions of a low-fidelity Global Climate Model (GCM) and those of a high-fidelity Regional Climate Model (RCM) We are able to produce high-fidelity temperature predictions at significantly lower computational cost compared to the high-fidelity model alone.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately modelling the Earth's climate has widespread applications ranging from forecasting local weather to understanding global climate change. Low-fidelity simulations of climate phenomena are readily available, but high-fidelity simulations are expensive to obtain. We therefore investigate the potential of Gaussian process-based multi-fidelity surrogate modelling as a way to produce high-fidelity climate predictions at low cost. Specifically, our model combines the predictions of a low-fidelity Global Climate Model (GCM) and those of a high-fidelity Regional Climate Model (RCM) to produce high-fidelity temperature predictions for a mountainous region on the coastline of Peru. We are able to produce high-fidelity temperature predictions at significantly lower computational cost compared to the high-fidelity model alone: our predictions have an average error of $15.62^\circ\text{C}^2$ yet our approach only evaluates the high-fidelity model on 6% of the region of interest.

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