三种插值方法对区域自动气象站日气温缺测数据插补的适用性研究  

Applicability of three interpolation methods in estimating daily temperature with missing data from regional automatic weather station

作　　者：孙越 王海军[1] 周月华[2] 严婧[1] 刘莹[1] SUN Yue;WANG Haijun;ZHOU Yuehua;YAN Jing;LIU Ying(Hubei Meteorological Information and Technological Support Center,Wuhan 430074;Wuhan Regional Climate Center,Wuhan 430074)

机构地区：[1]湖北省气象信息与技术保障中心,武汉430074 [2]武汉区域气候中心,武汉430074

出　　处：《暴雨灾害》2023年第1期97-104,共8页Torrential Rain and Disasters

基　　金：湖北省气象局科技发展基金(重点)项目(2022Z05-01)。

摘　　要：为解决气温观测记录缺测的问题,选择反距离权重插值(Inverse Distance Weighted,IDW)、普通克里金插值(Ordinary Kriging,OK)和多元线性回归(Multiple Linear Regression,MLR)三种方法,以湖北省2020年为例,对全省逐日平均气温(T)、最高气温(Tmax)和最低气温(Tmin)进行空间插补,并采用平均绝对误差(Mean Absolute Error,MAE)对3种方法的插补结果进行检验。结果表明:用MLR插补得到的Tmax、Tmin、T的MAE最小,分别为0.41℃、0.31℃和0.20℃,其中T的插补误差在1℃以内的站点比例高达100%;相比IDW和OK,MLR插补结果的MAE空间分布均匀,其不仅随海拔高度变化较小,随季节变化也相对较小。单站试验结果表明,当用于MLR模型的样本数量越多、时间离散度越大时,MLR对气温的插补效果越好。总体上,对日气温缺测数据的插补效果,MLR最优,IDW次之,OK最差;对于建立气象站点长时间连续气温数据集而言,MLR更适合解决区域自动气象站日气温数据缺测问题。In order to solve the problem of missing record of temperature observation,taking the Hubei Province in 2020 as an example,we selected three methods,that is,Inverse Distance Weighted(IDW),Ordinary Kriging(OK)and Multiple Linear Regression(MLR),to interpolate the missing values of daily mean temperature(T),maximum temperature(Tmax),and minimum temperature(Tmin).Based on this interpolation results,using the average absolute error(MAE),we evaluated the interpolation results obtained by these three methods.The results show that the MAE of Tmax,Tminand T obtained with the MLR interpolation is the lowest,which are 0.41℃,0.31℃,and 0.20℃,respectively.Meanwhile,the interpolation errors of T at all stations are less than 1℃.Compared with IDW and OK,the MAE spatial distribution of interpolation results obtained with MLR is more uniform with slight changes with altitude and seasons.Single station test shows that the more samples used for MLR model is and the greater the sample time dispersion is,the better the interpolation effect of MLR on temperature is.On the whole,the interpolation effect of MLR on missing values of daily temperature from regional stations is the best,IDW is the second,and OK is the worst.For establishing long-term continuous temperature datasets of meteorological stations,MLR is more suitable for solving the problem of missing records of daily temperature from regional automatic weather station(AWS).

关 键 词：气温 记录缺测 数据插补方法 平均绝对误差 多元线性回归 

分 类 号：P423[天文地球—大气科学及气象学]