Precipitation phenomenon instrument software quality control method and system

Abstract

The invention discloses a precipitation phenomenon instrument software quality control method. The method comprises the following steps: establishing a discrimination model according to observation data of 20 stations capable of representing rainfall conditions in the north and south of 9 provinces and cities; receiving original data; judging data; correcting abnormal data; outputting correction data. The invention also discloses a precipitation phenomenon instrument software quality control system. The system comprises a global control unit; a peripheral communication unit; a data acquisitionunit; a data management unit; a data determination unit; a model library; a statistical analysis and evaluation unit; and a data output unit. By means of the method and system, rainfall types of allplaces can be objectively fed back, false alarms and false alarms are removed to a certain degree through the rainfall phenomenon instrument after quality control, the recognition accuracy is improved, and the workload of manual intervention or correction is reduced.

Description

technical field [0001] The invention belongs to the fields of meteorological detection and surface meteorological observation, and in particular relates to a software quality control method and system for a precipitation phenomenology instrument. Background technique [0002] 1) Overview: [0003] At present, most provinces (autonomous regions and municipalities directly under the Central Government) across the country are carrying out parallel observations of precipitation weather phenomena. After one year of parallel observation, the automatic observation results will be used as the official observation record. The artificially observed weather phenomena need to be continuously observed during the day, and should be judged and recorded as much as possible at night. [0004] During the parallel observation period, the types of precipitation phenomena observed by manual observation and automatic observation are as follows: [0005] Manual observation: 8 types of drizzle, ...

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Problems solved by technology

[0007] Limitations: The relevant definitions of the above precipitation types in operational observations mainly refer to the definitions of manual observations, and there is no clear definition of the identification standards for instrumental observations (ie: automatic observations), resulting in large discrepancies between instrumental and manual identification when precipitation occurs difference

[0012] The limitations of the precipitation phenomenon instrument are: (the low accuracy of precipitation phenomenon identification is mainly manifested in the difference between the precipitation type results output by the instrument and the manual observation results, and the identification of precipitation phenomena at various stations across the country due to different hardware technologies and precipitation type identification standards The effect is mixed) The precipitation phenomenon meter is prone to false positives due to the limitations of the sampling area, false positives due to the influence of instrument accuracy and algorithms, and the inability to record two simultaneous precipitation phenomena, etc., resulting in discontinuous automatic precipitation records, which cannot truly and completely reflect the whole precipitation process

[0014] In response to the above situation, scholars at home and abroad organized various research experiments on the observation performance of the raindrop spectrometer. From the test results, it can be seen that the overall observation of the raindrop spectrometer is good, but the observed values ​​​​of different intervals of the obtained basic spectral data are still high or biased. Low phenomenon, and compare the distribution of basic spectrum data in various regions to get different fitting curves for different precipitation types

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