ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 48 Issue 7 > 2022,48(7):891-898. DOI:10.7519/j.issn.1000-0526.2022.041201 Prev Next
Disdrometer’s Data Quality Control Method Based on Speed and Quantity Threshold
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Abstract:
The observation of the microstructure of rainfall is very important for the accurate prediction of precipitation and weather modification. However, due to factors such as nonprecipitation factors, turbulence and raindrop overlap, there exist certain errors in the quality of the current raindrop spectrum observation data. This paper selects 9 national meteorological observation stations in Beijing from the Parsivel disdrometer observation data from April to October 2017, combined with the tippingbucket gauge rainfall observation data and manual record weather phenomenon, and studies the quality control method of disdrometer observation data. The results show that the wrong disdrometer observation data are mainly caused by haze, sanddust weather and insect activities. The particle velocity is mainly below 5 m·s-1, and the drop size distribution is relatively scattered. Through the speed threshold and quantity threshold (STQT) quality control methods, the wrong disdrometer observation data can be effectively eliminated. When the 0.4 speed threshold coefficient and 0.7 quantity threshold coefficient are used respectively, the best threat score is 0.92. After using STQT method for disdrometer observation data quality control, the correlation coefficient between disdrometer observation precipitation and tippingbucket gauge observation precipitation increases from 0.757 to 0.985. Thus, this method can make more effective use of rainfall observation data and give full play to the benefit of new observing equipment.
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