Open Access
March 2013 Daily minimum and maximum temperature simulation over complex terrain
William Kleiber, Richard W. Katz, Balaji Rajagopalan
Ann. Appl. Stat. 7(1): 588-612 (March 2013). DOI: 10.1214/12-AOAS602


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.


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William Kleiber. Richard W. Katz. Balaji Rajagopalan. "Daily minimum and maximum temperature simulation over complex terrain." Ann. Appl. Stat. 7 (1) 588 - 612, March 2013.


Published: March 2013
First available in Project Euclid: 9 April 2013

zbMATH: 06171285
MathSciNet: MR3086432
Digital Object Identifier: 10.1214/12-AOAS602

Keywords: Complex terrain , Gaussian process , maximum temperature , minimum temperature , multivariate covariance , nonstationary , simulation , stochastic weather generator

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.7 • No. 1 • March 2013
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