Daily minimum and maximum temperature simulation over complex terrain

TL;DR

This paper introduces a novel bivariate stochastic model for simulating daily minimum and maximum temperatures over complex terrains, effectively capturing nonstationarity and local variability for climate impact studies.

Contribution

It develops a new framework that separates local climate and weather components, with a nonparametric covariance estimation method suitable for nonstationary terrains.

Findings

Successfully applied to Colorado data set

Captures substantial nonstationarity in temperature fields

Provides accurate local climate estimates across complex terrain

Abstract

Spatiotemporal simulation of minimum and maximum temperature is a fundamental requirement for climate impact studies and hydrological or agricultural models. Particularly over regions with variable orography, these simulations are difficult to produce due to terrain driven nonstationarity. We develop a bivariate stochastic model for the spatiotemporal field of minimum and maximum temperature. The proposed framework splits the bivariate field into two components of "local climate" and "weather." The local climate component is a linear model with spatially varying process coefficients capturing the annual cycle and yielding local climate estimates at all locations, not only those within the observation network. The weather component spatially correlates the bivariate simulations, whose matrix-valued covariance function we estimate using a nonparametric kernel smoother that retains nonnegative definiteness and allows for substantial nonstationarity across the simulation domain. The statistical model is augmented with a spatially varying nugget effect to allow for locally varying small scale variability. Our model is applied to a daily temperature data set covering the complex terrain of Colorado, USA, and successfully accommodates substantial temporally varying nonstationarity in both the direct-covariance and cross-covariance functions.