Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models
Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models
复制标题
使用广义线性模型对加拿大草原省的日降水量和温度进行多地点多元建模
DOI：
10.1007/s00382-016-3004-z
复制
发表时间：
2016
期刊：
影响因子：
4.6
通讯作者：
Howard Wheater
中科院分区：
文献类型：
--
作者：
Z. E. Asong;M. Khaliq;Howard Wheater
基于广义线性模型（GLM）框架，利用加拿大草原省份阿尔伯塔省、萨斯喀彻温省和马尼托巴省120个站点的降水、最低和最高温度的每日观测数据，开发了一种多站点随机建模方法。温度模型采用两阶段正态异方差模型，分别拟合均值和方差分量。同样，降水发生过程和条件降水强度过程是分开模拟的。利用降水的变换作为协变量来预测温度场，说明了降水与温度之间的关系。本文利用1971-2000年国家环境预测再分析中心的大尺度大气协变量、遥相关指数、地理站点属性以及观测到的降水和温度记录对这些模式进行了校准。在校准前后的数据上对所开发的模型进行了验证。研究结果表明，所建立的模式能够捕捉降水和温度场的时空特征，如站点间和变量间的相关结构，以及观测序列中存在的系统区域变化。一些模拟的天气统计数字，从季节平均数到极端温度和降水的特征，以及一些常用的气候指数，也发现与从观测资料得出的结果非常吻合。这种基于glm的建模方法将进一步发展，用于全球气候模式输出的多站点统计降尺度，以探索加拿大该地区的气候变率和变化。
Based on the Generalized Linear Model (GLM) framework, a multisite stochastic modelling approach is developed using daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. Temperature is modeled using a two-stage normal-heteroscedastic model by fitting mean and variance components separately. Likewise, precipitation occurrence and conditional precipitation intensity processes are modeled separately. The relationship between precipitation and temperature is accounted for by using transformations of precipitation as covariates to predict temperature fields. Large scale atmospheric covariates from the National Center for Environmental Prediction Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate these models for the 1971–2000 period. Validation of the developed models is performed on both pre- and post-calibration period data. Results of the study indicate that the developed models are able to capture spatiotemporal characteristics of observed precipitation and temperature fields, such as inter-site and inter-variable correlation structure, and systematic regional variations present in observed sequences. A number of simulated weather statistics ranging from seasonal means to characteristics of temperature and precipitation extremes and some of the commonly used climate indices are also found to be in close agreement with those derived from observed data. This GLM-based modelling approach will be developed further for multisite statistical downscaling of Global Climate Model outputs to explore climate variability and change in this region of Canada.