Evaluation of the Polarimetric-Radar Quantitative Precipitation Estimates of an Extremely Heavy Rainfall Event and Nine Common Rainfall Events in Guangzhou

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.

Quantifying Precipitation Efficiency and Drivers of Excessive Precipitation in Post-Landfall Hurricane Harvey

“…Finally, radar-derived rainfall rates and precipitation accumulations were derived using the R(K DP , Z DR ) relationship determined by Zhang et al 2018 and described in Equation 2. This quantitative precipitation estimation (QPE) algorithm was found to perform best when Z H ≥ 38 dBZ, K DP ≥ 1 deg/km, and Z DR ≥ 1 dB, using the constants a = 51.16, b = 0.9311, and c = −0.0852 which are empirically derived and are best used in convective, warm rain events (Zhang et al 2018). 1-hour rainfall rates were computed using the 1-hour means of Z H , Z DR , and K DP at 2 km.…”

Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars

“…The flowchart of this new QPE algorithm is shown in Figure 8. The QPE algorithm mainly follows the polarimetric radar QPE algorithm proposed by Zhang et al [12] (hereafter Z18). The Z18 algorithm performs well for rainfall events occurring in Guangdong province; however, an SNR > 20 dB is simply used to distinguish ZDR and KDP values of high quality from those of low quality in Z18 algorithm.…”

“…The Z18 algorithm performs well for rainfall events occurring in Guangdong province; however, an SNR > 20 dB is simply used to distinguish ZDR and KDP values of high quality from those of low quality in Z18 algorithm. The Z-R relationship is used to estimate The QPE algorithm mainly follows the polarimetric radar QPE algorithm proposed by Zhang et al [12] (hereafter Z18). The Z18 algorithm performs well for rainfall events occurring in Guangdong province; however, an SNR > 20 dB is simply used to distinguish Z DR and K DP values of high quality from those of low quality in Z18 algorithm.…”

“…The estimator R(A) must be very carefully used in the above situations or else local adjustment will be needed. However, the performance of R(A) is unstable for Guangzhou radar when estimating precipitation during rainfall events with hourly accumulations below 50 mm [12]. Hence, the other polarimetric parameters (e.g., Z H , Z DR , and K DP ) are chosen to estimate precipitation in this study.…”

Analysis of Dual-Polarimetric Radar Variables and Quantitative Precipitation Estimators for Landfall Typhoons and Squall Lines Based on Disdrometer Data in Southern China

“…DSD data from different precipitations and regions are used to fit the rainfall estimator for dual-pol radars [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. On the basis of local DSD data, radar rainfall estimators for the C-band polarimetric radar were proposed by Aydin et al [24] in Colorado, by Bringi et al [16] in Okinawa, by Wang et al [17] in Taiwan, by Gu et al [25] in Oklahoma, by Wu et al [26] in Jianghai, China, by Silvestro et al [27] in Italy, and by Bringi et al [28] in the United Kingdom, respectively, which are remarkably different.…”