Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods

• URL: http://arxiv.org/abs/2103.10935v1
• Date: Sat, 13 Feb 2021 19:57:33 GMT
• Title: Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods
• Authors: Boumediene Hamzi, Romit Maulik, Houman Owhadi
• Abstract summary: We show that when the kernel of these emulators is also learned from data, the resulting data-driven models are faster than equation-based models. We see significant improvements over climatology and persistence based forecast techniques.
• Score: 0.6875312133832078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling geophysical systems as dynamical systems and regressing their vector field from data is a simple way to learn emulators for such systems. We show that when the kernel of these emulators is also learned from data (using kernel flows, a variant of cross-validation), then the resulting data-driven models are not only faster than equation-based models but are easier to train than neural networks such as the long short-term memory neural network. In addition, they are also more accurate and predictive than the latter. When trained on observational data for the global sea-surface temperature, considerable gains are observed by the proposed technique in comparison to classical partial differential equation-based models in terms of forecast computational cost and accuracy. When trained on publicly available re-analysis data for temperatures in the North-American continent, we see significant improvements over climatology and persistence based forecast techniques.

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