Computationally-Efficient Climate Predictions using Multi-Fidelity Surrogate Modelling
This provides a computationally efficient method for climate predictions, such as for mountainous regions in Peru, but is incremental as it applies an existing multi-fidelity approach to a specific climate modelling task.
The paper tackled the problem of expensive high-fidelity climate simulations by using Gaussian process-based multi-fidelity surrogate modelling to combine low-fidelity and high-fidelity models, resulting in high-fidelity temperature predictions with an average error of 15.62°C² at only 6% of the computational cost of the high-fidelity model alone.
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.