Stochastic Downscaling to Chaotic Weather Regimes using Spatially Conditioned Gaussian Random Fields with Adaptive Covariance

TL;DR

This paper introduces a statistical downscaling method using adaptive covariance Gaussian random fields to generate high-resolution convective weather data, validated through a novel approach, addressing the challenge of modeling chaotic weather regimes.

Contribution

It presents a new adaptive covariance-based Gaussian random field model for downscaling convective weather variables, with a validation method for generative models.

Findings

Effective downscaling of WBPT data over the UK

Adaptive covariance captures variable spatial properties

Validation method improves generative model assessment

Abstract

Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high resolution information at a lower computational and storage cost than numerical simulation alone. This is especially appealing for targeting convective scales, which are at the edge of what is possible to simulate operationally. Since convective scale weather has a high degree of independence from larger scales, a generative approach is essential. We here propose a statistical method for downscaling moist variables to convective scales using conditional Gaussian random fields, with an application to wet bulb potential temperature (WBPT) data over the UK. Our model uses an adaptive covariance estimation to capture the variable spatial properties at convective scales. We further propose a method for the validation, which has historically been a challenge for generative models.