DAILY MINIMUM AND MAXIMUM TEMPERATURE SIMULATION OVER COMPLEX TERRAIN

• 成果类型: Article
• 署名作者: Kleiber, William; Katz, Richard W.; Rajagopalan, Balaji
• 署名单位: University of Colorado System; University of Colorado Boulder; National Center Atmospheric Research (NCAR) - USA; University of Colorado System; University of Colorado Boulder
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/12-AOAS602
• 发表日期: 2013
• 页码: 588-612
• 关键词: cross-covariance functions; Hidden Markov model; stochastic simulation; spatial variability; daily precipitation; CLIMATE-CHANGE; interpolation; air; distributions; SURFACES

摘要

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.