A Spatial Consistency Quality Control Method for Daily Surface Temperature Observations

Based on the spatial regression test (SRT) and random forest (RF), a new spatial consistency quality control method named SRF was adapted to identify potential outliers in daily surface temperature observations in this article. For the new method, the SRT method was used to filter the data and the RF method was used to conduct regression. To evaluate the performance of the quality control method, the SRF, SRT and RF methods were applied to a surface temperature dataset with seeded errors from different regions of China from 2005 to 2014. The results indicate that the SRF method outperforms the other two methods in most cases. And the results of the comparison led to the conclusion that the SRF method improves the regression accuracy of traditional spatial consistency quality control methods and reduces the runtime of random forest through data refinement.