A quality control method for real-time data of automatic weather stations based on pca and pso-elm

Abstract

The invention discloses a real-time data quality control method of an automatic weather station based on PCA and PSO-ELM. The method firstly collects the temperature data of a certain historical time scale of an observation station (adjacent station) within a certain range of the inspected station, and constitutes The original acquisition signal; then principal component analysis is performed on the acquisition signal to achieve the purpose of eliminating redundancy; finally, the temperature reconstruction model is constructed through the improved extreme learning machine, and the data of the adjacent station at the time of inspection is used to obtain the temperature at the time of inspection. Estimated value; finally, according to the comparison between the estimated value and the actual observed value, the condition correction is carried out to complete the quality control of the temperature at this time. On the basis of the basic quality control method, this method makes up for the fixedness of the neighbor station selection in the current multi-station networking quality control method and improves the dynamic adaptability of the algorithm; on the other hand, the improved extreme learning machine improves the quality of the network. Generalization performance increases the accuracy of the reconstructed model.

Description

technical field [0001] The invention relates to the field of quality control of real-time data collected by an automatic weather station, in particular a quality control method for real-time data collected by an automatic weather station. Background technique [0002] In recent years, the number of surface meteorological observation stations has been increasing. Ground automatic weather stations have distribution characteristics such as dense station distribution, large terrain differences, and harsh station environments, which determine that there are systematic errors, random errors, gross errors, and micro-meteorological errors in the observation data. , which will affect the quality of observation data of automatic weather stations, thereby affecting the accuracy of climate change, climate model research and short-term numerical weather prediction. Therefore, quality control of the collected data is required. The existing three-level quality control business in my count...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for quality control of surface air temperature observation data based on principal component analysis and improved extreme learning machine, which solves the problem that the current quality control method cannot eliminate random errors, systematic errors, and micro-meteorological errors, and improves the quality of automatic meteorological data. The quality of the real-time observation temperature of the station

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