A screening method for abnormal sites in large-scale rainfall monitoring

Abstract

The invention relates to a large-scale rainfall monitoring abnormal site screening method, comprising the following steps: step 1, adopting the Hampel method to preliminarily determine the reference station based on annual rainfall observation data; step 2, adopting the Grubbs criterion to determine the reference station again; step 3. After determining the base station, use the surrounding station analysis method to identify abnormal sites based on hourly rainfall monitoring data; step 4, radar-assisted verification of abnormal sites. The invention can realize abnormal identification and rapid processing of large-scale rainfall monitoring station data, the abnormal identification rate is as high as 95% or more, and provides accurate and reliable basis for rainstorm and flood risk prompt and early warning.

Description

technical field [0001] The invention relates to a large-scale rainfall monitoring abnormal site screening method, belonging to the field of meteorology and hydrology, and is mainly used for providing accurate and reliable rainfall monitoring information for rainstorm flood risk prompt and early warning. Background technique [0002] Rainfall monitoring is an important part of hydrological monitoring, and it is the eyes, eyes and staff of the rainstorm and flood disaster prevention work. Since the 21st century, water conservancy departments have increased their support for the construction of automatic monitoring stations, especially after the construction of mountain torrent disaster prevention and control projects, there are 132,000 automatic monitoring stations for mountain torrent disasters nationwide, and the average density of automatic rainfall station network is 38km. 2 / station, 22 times that of 2006 (6,000 stations), the minimum flood reporting period was shortened...

AI Technical Summary

Problems solved by technology

However, due to the low construction standards of some measuring stations, the operation and maintenance of measuring stations located in hilly areas is difficult, and the data quality is difficult to be guaranteed. There are often situations such as taking large numbers, missing measurements, and missing measurements, and the problems of measuring stations have a strong influence Randomness, it is impractical to completely abandon a station

[0003] In order to effectively use the monitoring data of the stations, it is necessary to find out the stations with accurate monitoring data in different periods from many rainfall monitoring stations and eliminate the stations with problematic data quality. However, most of the current identification methods are highly subjective and misjudge The possibility is very high, and there are great risks in the application of business. First, it is difficult to identify abnormal sites. Second, it is easy to mistake the correct site for abnormal site, which is contrary to the original intention of carrying out abnormal site identification.

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