Research on quality control methods for surface temperature observations via spatial correlation analysis.

This article proposed a new quality control method (CS‐MSF) to identify potential outliers in the surface temperature observations. The CS‐MSF method employed cosine similarity and moving surface fitting to obtain the estimated value of the target station. For the regions with complex terrain and low weather station density, another quality control method (CE‐GBDT) was employed to compensate for the shortcomings of CS‐MSF. Compared to the spatial regression test method (SRT) and inverse distance weighting method (IDW), the results indicated that CS‐MSF outperformed SRT and IDW in all the cases. And CE‐GBDT was superior to the other methods for the regions with complex terrain and low weather station density. The comparison results led to the recommendation that the two proposed methods are effective quality control methods in identifying the seeded errors for the surface temperature observations. [ABSTRACT FROM AUTHOR]