Kernel Learning for Explainable Climate Science

• URL: http://arxiv.org/abs/2209.04947v2
• Date: Sun, 16 Jul 2023 17:15:56 GMT
• Title: Kernel Learning for Explainable Climate Science
• Authors: Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, Scott Hosking
• Abstract summary: We propose non-stationary kernels to model precipitation patterns in the Upper Himalayas Indus Basin. We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale. In ablation experiments we motivate each component of the proposed kernel by demonstrating its ability to model the spatial covariance, temporal structure and joint-temporal reconstruction.
• Score: 19.654936516882803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Upper Indus Basin, Himalayas provides water for 270 million people and countless ecosystems. However, precipitation, a key component to hydrological modelling, is poorly understood in this area. A key challenge surrounding this uncertainty comes from the complex spatial-temporal distribution of precipitation across the basin. In this work we propose Gaussian processes with structured non-stationary kernels to model precipitation patterns in the UIB. Previous attempts to quantify or model precipitation in the Hindu Kush Karakoram Himalayan region have often been qualitative or include crude assumptions and simplifications which cannot be resolved at lower resolutions. This body of research also provides little to no error propagation. We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale. This allows the posterior function samples to adapt to the varying precipitation patterns inherent in the distinct underlying topography of the Indus region. The input dependent lengthscale is governed by a latent Gaussian process with a stationary squared-exponential kernel to allow the function level hyperparameters to vary smoothly. In ablation experiments we motivate each component of the proposed kernel by demonstrating its ability to model the spatial covariance, temporal structure and joint spatio-temporal reconstruction. We benchmark our model with a stationary Gaussian process and a Deep Gaussian processes.

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