Abstract
The development and application of operational polarimetric radar (PR) in China is still in its infancy. In this study, an operational PR quantitative precipitation estimation (QPE) algorithm is suggested based on data for PR hydrometeor classification and local drop size distribution (DSD). Even though this algorithm performs well for conventional rainfall events, in which hourly rainfall accumulations are less than 50 mm, the capability of a PR to estimate extremely heavy rainfall remains unclear. The proposed algorithm is used for nine different types of rainfall events that occurred in Guangzhou, China, in 2016 and for an extremely heavy rainfall event that occurred in Guangzhou on 6 May 2017. It performs well for all data of these nine rainfall events and for light-to-moderate rain (hourly accumulation <50 mm) in this extremely heavy rainfall event. However, it severely underestimated heavy rain (>50 mm) and the extremely heavy rain at stations where total rainfall exceeded 300 mm within 5 h in this extremely heavy rainfall event. To analyze the reasons for underestimation, a rain microphysics retrieval algorithm is presented to retrieve Dm and Nw from the PR measurements. The DSD characteristics and the factors affecting QPE are analyzed based on Dm and Nw. The results indicate that compared with statistical DSD data in Yangjiang (estimators are derived from these data), the average raindrop diameter during this rainfall event occurred on 6 May 2017 was much smaller and the number concentration was higher. The algorithm underestimated the precipitation with small and midsize particles, but overestimated the precipitation with midsize and large particles. Underestimations occurred when Dm and Nw are both very large, and the severe underestimations for heavy rain are mainly due to these particles. It is verified that some of these particles are associated with melting hail. Owing to the big differences in DSD characteristics, R(KDP, ZDR) underestimates most heavy rain. Therefore, R(AH), which is least sensitive to DSD variations, replaces R(KDP, ZDR) to estimate precipitation. This improved algorithm performs well even for extremely heavy rain. These results are important for evaluating S-band Doppler radar polarization updates in China.
Highlights
Polarimetric radar (PR) can provide both backscatter and differential propagation phase information; this type of radar has significant advantages over single polarization radar.One of the advantages of PR is quantitative precipitation estimation (QPE) by using the PR variables.Previous studies have shown that polarimetric precipitation estimation (PPE) techniques are more robust with respect to drop size distribution (DSD) variations and the presence of hail than are the conventional Z–R relationship [1,2,3,4,5]
According to the method described by Cao et al [36], a sorting and averaging procedure based on two parameters (SATP) is introduced to mitigate the effects of sampling errors on DSD fitting
The extremely heavy rainfall event that occurred in Guangzhou on 6 May 2017 was used to test the capability of the algorithm to estimate heavy precipitation
Summary
Polarimetric radar (PR) can provide both backscatter and differential propagation phase information; this type of radar has significant advantages over single polarization radar. Previous studies have shown that polarimetric precipitation estimation (PPE) techniques are more robust with respect to drop size distribution (DSD) variations and the presence of hail than are the conventional Z–R relationship (here, R is the radar rainfall rate and Z is the radar-reflectivity factor) [1,2,3,4,5]. Algorithm that uses different combinations of radar variables depending on the rain rate estimates by using the conventional Z–R relationship; their synthetic algorithm was evaluated during the 2002–2003. An operational QPE algorithm based on a local DSD and mixed-phase precipitation identification is proposed and applied to some rainfall events. This algorithm performs well for most rainfall events investigated in this study.
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