An Evolutionary Spectrum Approach to Incorporate Large-scale Geographical Descriptors on Global Processes

TL;DR

This paper presents a flexible nonstationary spatio-temporal model for global data that incorporates large-scale geographical features, outperforming traditional models in capturing complex spatial patterns and enabling efficient large-scale data analysis.

Contribution

It introduces an evolutionary spectrum approach that models nonstationarity across Earth's geography, allowing for scalable estimation and improved representation of spatial variability.

Findings

Outperforms axially symmetric models in capturing longitudinal patterns.

Enables analysis of datasets larger than 20 million points within a day.

Provides a highly compressed synthetic description of climate model ensembles.

Abstract

We introduce a nonstationary spatio-temporal statistical model for gridded data on the sphere. The model specifies a computationally convenient covariance structure that depends on heterogeneous geography. Widely used statistical models on a spherical domain are nonstationary for different latitudes, but stationary at the same latitude (axial symmetry). This assumption has been acknowledged to be too restrictive for quantities such as surface temperature, whose statistical behavior is influenced by large scale geographical descriptors such as land and ocean. We propose an evolutionary spectrum approach that is able to account for different regimes across the Earth's geography, and results in a more general and flexible class of models that vastly outperforms axially symmetric models and captures longitudinal patterns that would otherwise be assumed constant. The model can be estimated with in a multi-step conditional likelihood approximation that preserves the nonstationary features while allowing for easily distributed computations: we show how the fit of a data sets larger than 20 million data can be performed in less than one day on a state-of-the-art workstation. Once the parameters are estimated, it is possible to instantaneously generate surrogate runs from a common laptop. Further, the resulting estimates from the statistical model can be regarded as a synthetic description (i.e. a compression) of the space-time characteristics of an entire initial condition ensemble. Compared to traditional algorithms aiming at compressing the bit-by-bit information on each climate model run, the proposed approach achieves vastly superior compression rates.