Stochastic Downscaling to Chaotic Weather Regimes using Spatially Conditioned Gaussian Random Fields with Adaptive Covariance
Prudden, R; Robinson, N; Challenor, P; et al.Everson, R
Date: 30 November 2021
Article
Journal
Weather and Forecasting
Publisher
American Meteorological Society
Publisher DOI
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 ...
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.
Computer Science
Faculty of Environment, Science and Economy
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