论文标题
日间温度时间序列的空间建模：对西班牙阿拉贡的每日最高温度的检查
Spatial modeling of day-within-year temperature time series: an examination of daily maximum temperatures in Aragón, Spain
论文作者
论文摘要
认识到有关对每日温度数据进行建模的大量文献，我们提出了一个多级时空模型，该模型引入了几项创新，以解释西班牙包含Aragón的地区60年内夏季60年内的每日最高温度。该模型在连续空间上运行，但在一年中每年和日期都采用了两个离散的时间尺度。它在一年内和几年内通过自动估计来捕获时间依赖。空间依赖性是通过截距，斜率系数，方差和自相关的空间过程建模来捕获的。该模型以一种将固定效应与随机效应分开的形式表达，并分开每种效果的空间，年和天数。通过探索性数据分析的启发，采用了固定效果，以捕获海拔，季节性和线性趋势的影响。在几年内以及几年内的几天内引入了多年的纯错误。使用剩余的交叉验证检查模型的性能。介绍了该模型的应用，包括预测未观察到的位点或部分观察到的地点，以及研究气候变化比较的推断。
Acknowledging a considerable literature on modeling daily temperature data, we propose a multi-level spatio-temporal model which introduces several innovations in order to explain the daily maximum temperature in the summer period over 60 years in a region containing Aragón, Spain. The model operates over continuous space but adopts two discrete temporal scales, year and day within year. It captures temporal dependence through autoregression on days within year and also on years. Spatial dependence is captured through spatial process modeling of intercepts, slope coefficients, variances, and autocorrelations. The model is expressed in a form which separates fixed effects from random effects and also separates space, years, and days for each type of effect. Motivated by exploratory data analysis, fixed effects to capture the influence of elevation, seasonality and a linear trend are employed. Pure errors are introduced for years, for locations within years, and for locations at days within years. The performance of the model is checked using a leave-one-out cross-validation. Applications of the model are presented including prediction of the daily temperature series at unobserved or partially observed sites and inference to investigate climate change comparison.
