Application of PSO-PSR-ELM-Based Ensemble Learning Algorithm in Quality Control of Surface Temperature Observations

In order to overcome the quality control problem over areas where the station density is low, or for some stations that have no adjacent stations and lack effective internal reference data, for example those newly deployed stations and special single factor stations, a new quality control method for surface temperature observations based on the ensemble learning algorithm of Phase Space Reconstruction (PSR) and Extreme Learning Machine (ELM) that was improved by Particle Swarm Optimization (PSO) was introduced in detail in this paper. This method considers the chaotic characteristics of the time series of temperature. In order to assess the feasibility and applicability of the proposed method, it was applied to hourly temperature observations from 2007 to 2009 in eight cities of Jiangsu Province. Results were examined and compared against that of conventional single-station quality control method and Tshebyshev Polynomial Interpolation (TPI) method. It was found that the method introduced in this study can flag suspicious data more effectively, and it also has the advantages of high identification accuracy and good adaptability and controllability over different regions with various climate backgrounds.