Objective grouping method for regional automatic meteorological stations

Abstract

The invention discloses an objective grouping method for regional automatic meteorological stations. The method comprises the following steps of acquiring an analysis frequency by optimizing samplingpoints and combining a distance factor to establish an inter-station detection environment similarity judgment index according to the intrinsic objective association between a difference bit / frequencysequence generated by the inter-station daily minimum temperature difference and the inter-station underlying surface difference; on the basis of index values among national stations, establishing echelon judgment and typing regional stations, and highlighting the comparability and group representativeness among the combined stations. Sample stability is improved by combining frequency median with processing of sensor sampling errors, the sampling time period is greatly shortened, and dynamic clustering adapting to regional station environment fluctuation and seasonal factors is achieved. According to the method, automatic typing and dynamic tracking of the environmental background of meteorological observation data can be realized, and errors and lag generated by subjective description of an observation environment are eliminated; fine underlying surface background data can be provided for actual temperature field and data authenticity analysis, and the application value of a large number of regional automatic station temperature data is explored.

Description

technical field [0001] The invention relates to the technical field of data application and management of regional automatic weather stations, in particular to an objective grouping method for regional automatic weather stations. Background technique [0002] In order to provide refined meteorological services and improve the ability of meteorological disaster reduction and disaster prevention, a large number of regional automatic meteorological stations have been built successively across the country to continuously monitor the meteorological elements in each region. Real-time data such as rainfall and wind speed have been used in disaster prevention and mitigation. played an important role in meteorological services. However, since the temperature measurement data is greatly affected by local environmental conditions, and the large number of regional automatic stations is not suitable for the construction and maintenance standards of national stations (large monitoring sta...

AI Technical Summary

Problems solved by technology

[0003] 1. Only relying on station location information and a brief subjective description of the underlying surface, it is difficult to accurately correspond to the observation environment conditions of the regional automatic stations that are not stable enough, which has a negative impact on the application of temperature data;

[0004] 2. Under different observation underlying surface conditions, the temperature change patterns are quite different. However, under the indiscriminate management mode without grouping, the temperature data quality judgment threshold needs to be set relatively broadly, which is not conducive to the dynamic judgment threshold of classification setting refinement Ensuring data quality;

[0005] 3. The number of regional automatic stations is large and widely distributed, and manual management is limited. The automatic online identification mechanism for sensors and abnormal conditions in the observation environment is very important, but it needs to be improved;

[0006] 4. Although the regional automatic stations are built according to the regional grid, the specific location of the station is greatly influenced by human factors, and the layout optimization of the station lacks objective methods for research and judgment

But this is not enough to solve the problem of the environment in which the air temperature sensor operates

Images