新疆区域逐月缺测气温序列的插补及重建

以新疆区域的墨玉站、牧气站和阿克达拉站为例, 使用多元回归分析的方法对3站月平均气温缺测资料进行插补, 通过基于MAE与RMISE的交叉验证及M-K突变检验和小波分析, 分析3站实测月平均气温距平资料与模拟月平均气温距平资料间的误差, 验证回归方法的精度. 同时, 使用该方法插补和重建新疆1961-2010年105个站月平均气温数据, 并对插补前90个站和插补后105个站的序列进行了EOF(经验正交函数)分析, 空间特征向量相似度高. 结果表明: 多元回归方法具有较高的精度, 回归方法重建新疆区域逐月气温资料能较好地反映当地月平均气温变化规律及特征, 重建逐月气温资料与实测逐月气温误差在信度范围内, 重建后的完整序列可作为气候变化业务及科学研究基础数据集.

Interpolation and reconstruction of the missing monthly air temperature records in Xinjiang

Based on the contrast between the measured monthly mean temperature anomaly and the simulated monthly mean temperature anomaly reconstructed with multiple regression analyses at Moyu Station, Muqi Station and Akdala Station, the accuracies of the multiple regression analyses are analyze through the means of the cross-validation based on MAE and RMSIE, M-K mutation tests and wavelet analyses. Similarly, based on the analyses of EOF the contrast between the measured monthly mean temperature data from 90 weather stations and the data interpolated and reconstructed from 105 weather stations in Xinjiang also show that there is high similarity in eigenvectors. Finally, it is found that the multiple regression analysis possesses higher accuracy. The reconstructed Xinjiang monthly temperature data have reliability same as the measured monthly temperature data, which could better reflect the characteristics and variation of the monthly mean temperature and provide basic data for climate change research.