High Resolution Global Precipitation Downscaling with Latent Gaussian Models and Nonstationary SPDE Structure

TL;DR

This paper introduces a novel nonstationary latent Gaussian model with a deformed SPDE structure for high-resolution global precipitation downscaling, enabling detailed and accurate precipitation mapping at urban scales.

Contribution

It develops a new nonstationary SPDE-based approach with a buffer for land-sea differences, improving precipitation prediction at high resolution.

Findings

Enhanced predictability over stationary models

Feasible Bayesian inference for large datasets

High-resolution daily precipitation simulations across the US

Abstract

Obtaining high-resolution maps of precipitation data can provide key insights to stakeholders to assess a sustainable access to water resources at urban scale. Mapping a nonstationary, sparse process such as precipitation at very high spatial resolution requires the interpolation of global datasets at the location where ground stations are available with statistical models able to capture complex non-Gaussian global space-time dependence structures. In this work, we propose a new approach based on capturing the spatial dependence of a latent Gaussian process via a locally deformed Stochastic Partial Differential Equation (SPDE) with a buffer allowing for a different spatial structure across land and sea. The finite volume approximation of the SPDE, coupled with Integrated Nested Laplace Approximation ensures feasible Bayesian inference for tens of millions of observations. The simulation studies showcase the improved predictability of the proposed approach against stationary and no-buffer alternatives. The proposed approach is then used to yield high resolution simulations of daily precipitation across the United States.