Random Sampling-Arithmetic Mean: A Simple Method of Meteorological Data Quality Control Based on Random Observation Thought

Abstract

The quality control of meteorological data has lately received great attention for its important significance to national ecological security and military security. However, the observational quality of the data has made it challenging to the quality control of meteorological data. In an effort to overcome this challenge, a random sampling-arithmetic mean (RS-AM) method based on the random observation method is proposed to solve the problem. Firstly, the reason why the arithmetic mean is not ideal for truth estimation is proved in the paper. Secondly, the method evaluates the goodness of fit between the expected distribution and the sampling distribution by repeatedly extracting the random observation vector based on the random sampling model, to find the random observation vector closest to the expected distribution. Then, the distance between the median and arithmetic mean of each set of claims is calculated by the distance formula, and the claims with the minimum distance are selected. The random extraction is continued on the selected set of claims until the stop condition is met. Finally, the truth is calculated by the method of arithmetic mean from the selected claims. Moreover, the convergence of this method is proved by theoretical derivation. Experimental results show that the proposed RS-AM method can effectively solve the problem of data observation quality. And, compared with the conflict resolution on heterogeneous data (CRH) method, the RS-AM method reduces 1.5% on MSE and 2.9% on RMSE while ensuring the error rate is basically the same.

Keywords

• Meteorological data quality control
• random observational methods
• truth discovery
• random sampling-arithmetic mean (RS-AM)