Rainfall quality control and evaluation method fused with satellite radar multi-source data and application

Abstract

The invention discloses a precipitation quality control and evaluation method fused with satellite radar multi-source data and application. The method comprises the following steps: acquiring satellite data, radar data and ground rainfall data, and defining a quality control reference domain, a quality evaluation reference domain, a real-time abnormal event and a quasi-real-time abnormal event; marking abnormal sites in the ground rainfall data, calculating quality control factors and weather backgrounds of the abnormal sites so as to identify real-time abnormal events, and marking whether data corresponding to the real-time abnormal events is correct or not; and obtaining ground precipitation data and a quality marking result of a quality evaluation reference domain corresponding to the to-be-evaluated site, calculating a quality evaluation factor of the to-be-evaluated site, and identifying a quasi-real-time abnormal event according to the quality evaluation factor. When rainfall quality control and evaluation are carried out, satellite data and radar data are introduced, the automatic identification level of real-time quality abnormal events is improved, a mutual feedback mechanism between processing of different quality problem events is achieved, and the effect is good.

Description

technical field [0001] The invention belongs to the technical field of applied meteorology, and more specifically relates to a method for controlling and evaluating precipitation quality and its application by fusing satellite radar multi-source data. Background technique [0002] Climate change has exacerbated the occurrence of extreme weather and hydrological events. Precipitation is the most direct driver of regional drought and flood events, and it is also the basic element for studying climate change. The precipitation data of ground automatic station observation stations are direct measurements, with high observation accuracy and wide application. They are very important for meteorological disaster early warning, weather forecast verification, climate prediction and decision-making services. Affected by many factors, the quality of precipitation data observed by automatic stations exists objectively, but as long as its quality status is known, it can be properly used. ...

AI Technical Summary

Problems solved by technology

Conventional quality control methods revolve around the observation data of automatic stations, but most of these methods can only pick out mistakes but not make mistakes, and the final confirmation of quality problem data requires manual intervention

The multi-source data quality control method now introduces data from different visual observation systems to judge surface precipitation, which can realize automatic judgment of quality problem data. At present, radar data is the main method, but ground radar observations are easily affected by complex terrain, etc. Moreover, radar detection still has limitations in space, and there are uncertainties in the multi-source quality control method of precipitation based only on radar data.

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