Research And Implementation Of Data Quality Control Method Of Comprehensive Meteorological

In recent years, with the wide use of meteorological automatic observation stations in the whole country, the meteorological observation data grow exponentially. The quality of the meteorological observation data has a direct impact on the accuracy of weather forecasting and climate prediction. Classic meteorological data quality control process can check common meteorological data properly, but there are some disadvantages such as misclassification of meteorological data error, low sensitivity for error and so on.In this paper, an improved spatial interpolation algorithm based on the 1DW (Inverse Distance Weighted) method is introduced. Compared with the traditional algorithms, this improved IDW algorithm which can define the small threshold of the error data has higher accuracy. At the same time, meteorological data quality control codes based on sensitivity analysis are designed. And the quality control methods are applied to the integrated quality control prototype system of the automatic weather station observation data. The main contributions of this paper are as follows.(1) Based on the study of the commonly used spatial interpolation algorithms, especially the analysis of the principle and implementation of the IDW and ordinary Kriging (OK) methods, an improved spatial interpolation algorithm is designed which is combined with OK and IDW. This algorithm stressed the importance of changes domain of site space correlation. Based on the rule of the weight changes, this algorithm gets certain error threshold by using fitting functions of the changes domain.(2) Based on the improved IDW algorithm, the spatial correlations between meteorological elements are considered as the objects. The standard quality control codes for meteorological data are designed and implemented.The hourly observation data sets selected from some automatic weather stations in East China are considered as testing data sets in experiments. The experimental results show that the proposed methods in this paper can effectively improve the data quality control precision of surface observation data and strengthen the application of the correlations between meteorological elements during the quality control process.