Data quality control method of automatic weather station based on strong weather process correction

Abstract

The invention provides a method for controlling data quality of an automatic meteorological station based on strong weather process correction. The method solves the problems of false judging and missed judging of the traditional quality control method, and can obviously improve the reliability and accuracy of the quality control of the automatic meteorological station; especially for China southeast areas and coastal regions with frequent occurrence of extreme weathers, a smaller judging threshold value can be adopted, so as to realize more accurate judging, and effectively identify the false judging caused by local strong weather process (especially cold front and warm front crossing regions). The method has the advantages that by adopting the secondary reliability test correction, the false judging on the tested correct records caused by false records of the adjacent stations in the test calculation process is effectively avoided, and the accuracy of the quality control is further improved; the more accurate weather actual monitoring data sources are provided for a meteorological service system, and the accuracy of weather precast is further improved.

Description

Technical field: [0001] The invention belongs to the field of atmospheric science, and relates to a quality control method of encrypted automatic weather station (hereinafter referred to as automatic station or station) observation data based on strong weather identification and correction of meteorological satellite data. By analyzing the meteorological laws and physical characteristics of different meteorological elements at the same time station by station, the confidence degree of each meteorological element is identified, and then the time change sequence of each element is obtained according to factors such as the degree of confidence and the horizontal distance between adjacent stations. As well as the interpolation between the same elements at adjacent stations, compare the relationship between the interpolation results and the time change rate of the same elements at the station to be tested. Then analyze whether there is a strong weather process in the site with abno...

AI Technical Summary

Problems solved by technology

In fact, there are generally differences in the geographical location, geological conditions, and altitude of each observation station. Especially in the northwest of my country and the Qinghai-Tibet Plateau, the distribution of automatic stations is very sparse, and the distance between stations is far away. The spatial correlation is inherently very In addition, the height difference between different stations in mountainous and hilly areas often exceeds hundreds of meters or even 1 kilometer. Such a spatial consistency test will inevitably have a very unsatisfactory effect.

Although the time consistency check does not need to consider the surrounding stations, the time change rate of each meteorological element is also affected by factors such as station location and season, so it is difficult to quantify and standardize

If the threshold value for quality judgment is too large, it is easy to miss the existing errors; if the threshold value is small, it is easy to falsely report the correct record

Images