基于CLDAS温度适宜度指标空间化方法

摘要

为了避免站点观测数据空间插值误差,提高玉米温度适宜度指标空间化精度,本文利用陆面数据同化系统CLDAS逐小时气温同化数据,基于内蒙古玉米动态适宜度计算方法,利用GIS空间分析和建模功能,构建逐日温度适宜度指标的空间化计算模型.该模型根据温度适宜度动态模型计算指定日期的“三基点”温度指标空间分布;结合CLDAS日平均气温空间分布,利用条件函数实现适宜度指标分段空间化计算.以2015年5-8月为例,进行常规气象站点误差检验,结果表明:常规站检验最大绝对误差0.156,90％的站点绝对误差小于0.1;最大相对误差36.9％,70％的站点相对误差不足8％;CLDAS数据很好的把握了5月高温、8月低温的不利影响,适宜度为0.基于CLDAS气温拟合数据的温度适宜度模型流程清晰实用,适宜度指标空间化精度较高.%In order to avoid the errors of the site observation data during the spatial interpolation and improve the accuracy of the spatialization on corn temperature suitability index,the CMA Land Data Assimilation System is used,which applies hourly temperature data.The daily temperature suitability index calculation model is set up in the space,which is based on the corn dynamic suitability calculation method of Inner Mongolia by using of GIS spatial analysis and Model Builder.The temperature suitability dynamic model requires inputting date that is used to calculate the spatial distribution of temperature indicators,such as optimum temperature,maximum temperature,and minimum temperature.Combining with the spatial distribution of the average daily temperature of CLDAS,the temperature suitability index is calculated on the space by using condition functions.Regular site suitability and model calculation results are compared from May to August 2015 as an example.The results show that the maximum absolute error is 0.156,and the absolute error of about 90％ results is less than 0.1.The maximum relative error is 36.9％,and the relative error of about 70％ results is less than 8％.CLDAS data can reflect the influence of high temperature in May and low temperature in August,in which suitability index is 0.By using the Model Builder,the constructed calculation model of suitability index has higher practicability.Based on CLDAS temperature data,the temperature suitability index error is relatively small,and the precision of the spatialization can be used for further researches.