Abstract
The quality of radar data is crucial for its application. In particular, before radar mosaic and quantitative precipitation estimation (QPE) can be conducted, it is necessary to know the quality of polarimetric parameters. The parameters include the horizontal reflectivity factor, ZH; the differential reflectivity factor, ZDR; the specific differential phase, KDP; and the correlation coefficient, ρHV. A novel radar data quality index (RQI) is specifically developed for the Chinese polarimetric radars. Not only the influences of partial beam blockages and bright band upon radar data quality, but also those of bright band correction performance, signal-to-noise ratio, and non-precipitation echoes are considered in the index. RQI can quantitatively describe the quality of various polarimetric parameters. A new radar mosaic QPE algorithm based on RQI is presented in this study, which can be used in different regions with the default values adjusted according to the characteristics of local radar. RQI in this algorithm is widely used for high-quality polarimetric radar data screening and mosaic data merging. Bright band correction is also performed to errors of polarimetric parameters caused by melting ice particles for warm seasons in this algorithm. This algorithm is validated by using nine rainfall events in Guangdong province, China. Major conclusions are as follows. ZH, ZDR, and KDP in bright band become closer to those under bright band after correction than before. However, the influence of KDP correction upon QPE is not as good as that of ZH and ZDR correction in bright band. Only ZH and ZDR are used to estimate precipitation in the bright band affected area. The new mosaic QPE algorithm can improve QPE performances not only in the beam blocked areas and the bright band affected area, which are far from radars, but also in areas close to the two radars. The sensitivity tests show the new algorithm can perform well and stably for any type of precipitation occurred in warm seasons. This algorithm lays a foundation for regional polarimetric radar mosaic precipitation estimation in China.
Highlights
For flash flood detection and warning, it is meaningful to obtain a large range of accurate quantitative precipitation estimation (QPE) products by using surface rain gauges and various remote sensing instruments
The National Mosaic and Multi-Sensor QPE (NMQ) system of the USA was built upon the Collaborative Radar Acquisition Field Test data network with multiple single-polarization radars and sensors [6]
The aim of this study is to develop and validate a suitable polarimetric radar mosaic QPE algorithm, so as to lay a foundation for regional polarimetric radar mosaic precipitation estimation in China
Summary
For flash flood detection and warning, it is meaningful to obtain a large range of accurate quantitative precipitation estimation (QPE) products by using surface rain gauges and various remote sensing instruments. Radar mosaic technology in this study aims to produce mosaic data used to estimate precipitation on the ground, so it usually employs a hybrid scan strategy to obtain high-quality data near the ground. The National Mosaic and Multi-Sensor QPE (NMQ) system of the USA was built upon the Collaborative Radar Acquisition Field Test data network with multiple single-polarization radars and sensors [6]. In this system, the hybrid scan reflectivity factors of single radar are obtained first. The data are mosaicked to calculate QPE based on different Z-R relationships (R is the rainfall rate and Z is the reflectivity factor) corresponding to five types of precipitation.
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