Precipitation quality control method based on SFA and phase space local prediction

Abstract

Most of the quality control applications of hydrological data are based on basic quality control methods such as logical detection, extreme value check and spatial consistency check. Although these methods can detect problem data with large errors, this makes the data lack credibility. Therefore, a single station data quality control method, SFA-WZLM, is proposed in this paper. This method uses slow feature analysis (SFA) to extract external forcing factors for embedding in chaotic local prediction models. Observations from January 1987 to October 2015 was used as the train set, and observations from December 2015 to October 2017 were used as the test set. The results indicate that the method has higher prediction accuracy than the prediction model without embedded external forcing factors, weighted first-order local prediction model (WFLM) and weighted first-order local prediction model (SFA-WFLM) including external forcing factors and exhibited the best quality control error detection. In addition, the method shows good stability in 6 different climates and different terrain stations across the country in China.