Daily minimum and maximum temperature simulation over complex terrain
Daily minimum and maximum temperature simulation over complex terrain
复制标题
复杂地形每日最低和最高温度模拟
DOI：
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发表时间：
2012
期刊：
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通讯作者：
B. Rajagopalan
中科院分区：
文献类型：
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作者：
W. Kleiber;R. Katz;B. Rajagopalan
最低和最高温度的时空模拟是气候影响研究和水文或农业模型的基本要求。特别是在可变地形的地区，由于地形驱动的非平稳性，这些模拟很难产生。我们建立了一个最低和最高温度时空场的双变量随机模型。建议的框架分裂成两个组成部分的“当地气候”和“天气”的双变量字段。“当地气候部分是一个线性模型，具有空间变化的过程系数，捕捉年周期，并得出所有地点的当地气候估计值，而不仅仅是观测网络内的地点。天气分量空间相关的二元模拟，其矩阵值的协方差函数，我们估计使用非参数内核平滑，保持非负定性，并允许大量的非平稳性在整个模拟域。统计模型增加了空间变化的块金效应，以允许局部变化的小尺度变化。我们的模型被应用到每日温度数据集覆盖的复杂地形的科罗拉多，美国，并成功地容纳大量的随时间变化的非平稳性的直接协方差和互协方差函数。
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.