资源环境科技发展态势分析平台

This article aims to find the best-fit regression function for the quality control (QC) for the surface hourly temperature. A new QC method based on gene-expression programming (GEP) was employed to identify potential outliers in the surface hourly temperature observations. Compared to the spatial regression test (SRT) method and inverse distance weighting (IDW) method, the results indicate that both GEP and SRT outperform IDW in all the cases. The GEP method and the SRT method display equivalent effects in most of the cases. But GEP was found to be superior to the other methods when the weather station density was low. For all cases, the GEP method could yield a smaller estimated error than the other methods, respectively. The results of the comparison led to the recommendation that the GEP method is an effective QC method in identifying the seeded errors for the surface hourly temperature.