Automatic meteorological station data quality control method based on empirical mode decomposition

An empirical mode decomposition, automatic weather station technology, applied in the direction of instruments, etc., can solve the problems of inability to eliminate random noise, system noise, micro-meteorological noise and coarse noise, and achieve the effect of improving reliability.

Inactive Publication Date: 2015-02-04
NANJING UNIV OF INFORMATION SCI & TECH
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Provide a denoising method for automatic weather station data based on empirical mode decomposition, which solves the problem that current quality control methods cannot eliminate random noise, system noise, micrometeorological noise and coarse noise, and improves the quality of automatic weather station observation data

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Automatic meteorological station data quality control method based on empirical mode decomposition
  • Automatic meteorological station data quality control method based on empirical mode decomposition
  • Automatic meteorological station data quality control method based on empirical mode decomposition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] Below in conjunction with accompanying drawing, the technical scheme of invention is described in detail:

[0034] Be the flow process of the method of the present invention, as figure 1 As shown, the single-station data is collected, and after the basic quality control of the collected data, the EEMD (empirical mode) decomposition is performed to obtain the eigenmode components and trend items, and the eigenmode components and trend items are further weighted , to obtain the weighted eigenmode component and the weighted trend item, and finally perform data reconstruction on the weighted eigenmode component and the weighted trend item to obtain the denoised data, and the denoising process is completed.

[0035] The following will be numbered as 58238 stations in Nanjing in January, 2007, and the temperature data observed once every 10 minutes is carried out embodiment analysis, further illustrate the present invention:

[0036] 1. Collect temperature observation data x...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an automatic meteorological station data quality control method based on the empirical mode decomposition. The empirical mode decomposition is used for the automatic meteorological station data quality control. The method comprises the following steps: firstly, collecting single-station data; then, after the collected data is subjected to basic quality control, carrying out EEMD decomposition to obtain an eigenmode component and a trend term; further carrying out weighted approach to the eigenmode component and the trend term; obtaining a weighted eigenmode component and a weighted trend term; and finally, carrying out data reconstruction to the weighted eigenmode component and the weighted trend term so as to obtain denoised data and finish the denoising process. According to the method, on the basis of the basic quality control method, the defect that random noise, system noise, microclimate noise and big noise can not be eliminated with the traditional quality control method is made up, and the quality of the data observation of the automatic meteorological station is improved.

Description

technical field [0001] The invention relates to the field of quality control of data collected by an automatic weather station, in particular a method for denoising the temperature data collected by an automatic weather station. Background technique [0002] At present, domestic automatic weather stations have the characteristics of dense distribution of stations, large terrain differences, harsh station environment, high degree of automation in data collection and transmission, strong real-time data, and obvious weather phenomena in small and medium scales. Observations from ground stations appear to be more complex and serious, so quality control must be performed on observations from automatic weather stations. The quality control of the collected data in our country is carried out at two levels. One is the basic quality control at the observation station; the other is the data processing center. Instrument sensor failure, long-term drift should be evaluated. According ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G01W1/00
Inventor 叶小岭张颖超熊雄张齐东胡凯孙宁
Owner NANJING UNIV OF INFORMATION SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap