English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Heat waves resulting from prolonged extreme temperatures pose a significantrisk to human health globally. Given the limitations of observations of extremetemperature, climate models are often used to characterize extreme temperatureglobally, from which one can derive quantities like return values to summarizethe magnitude of a low probability event for an arbitrary geographic location.However, while these derived quantities are useful on their own, it is alsooften important to apply a spatial statistical model to such data in order to,e.g., understand how the spatial dependence properties of the return valuesvary over space and emulate the climate model for generating additional spatialfields with corresponding statistical properties. For these objectives, whenmodeling global data it is critical to use a nonstationary covariance function.Furthermore, given that the output of modern global climate models can be onthe order of $\mathcal{O}(10^4)$, it is important to utilize approximateGaussian process methods to enable inference. In this paper, we demonstrate theapplication of methodology introduced in Risser and Turek (2020) to conduct anonstationary and fully Bayesian analysis of a large data set of 20-year returnvalues derived from an ensemble of global climate model runs with over 50,000spatial locations. This analysis uses the freely available BayesNSGP softwarepackage for R.

Nonstationary Bayesian modeling for a large data set of derived surface temperature return values