Algorithm Design of Fast and RealTime Quality Control forPrecipitation Data from Automatic Stations
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Abstract:
Quality control （QC） methods for Automatic Weather Stations （AWS） precipitation data are combined to design a fast and realtime QC Algorithm. Abnormal precipitation data are classified firstly. The Grubbs test is then used to screen out suspicious stations in a local and finite space preliminarily. Three factors are considered in the second step of the QC procedure, which are the spatial distribution of precipitation per hour, the timehistory equation of precipitation per minute, and the related characteristics between temperature and humidity. Practical test shows that the integrated quality control algorithm has a better balance between performance and accuracy, which is especially effective in summer.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.