Statistics-based optimization of the polarimetric radar hydrometeor classification algorithm and its application for a squall line in South China

Wu, Cao; Liu, Liping; Wei, Ming; Xi, B.; Yu, Minghui

doi:10.1007/s00376-017-6241-0

Cited by 32 publications

(32 citation statements)

References 31 publications

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“…Based on data quality, hydrometeor phase type, and polarimetric data threshold, the QPE algorithm is conducted for poor-quality data, mixed-phase precipitation, and liquid precipitation, respectively. An analysis of the PR data from China [27] indicates that noise can easily affect ZDR and KDP when the SNR is <20 dB, leading to large spatiotemporal variations. Therefore, under these SNR conditions, ZDR and KDP are not appropriate for precipitation estimation, and R1(ZH) is used instead.…”

Section: Qpe Algorithm and Evaluation Methodsmentioning

confidence: 99%

“…where ZH is in units of mm 6 /m 3 , ZDR is in dB, KDP is in °/km, and R is in mm/h. An analysis of the PR data from China [27] indicates that noise can easily affect Z DR and K DP when the SNR is <20 dB, leading to large spatiotemporal variations. Therefore, under these SNR conditions, Z DR and K DP are not appropriate for precipitation estimation, and R1(Z H ) is used instead.…”

Section: Qpe Algorithm and Evaluation Methodsmentioning

confidence: 99%

“…Figure 11 shows time series of the vertical profiles of Z H , Z DR , K DP , ρ HV , and Hcl (Hydrometeor classification result) at the three stations where the heaviest rainfall occurred. Hcl is obtained according to the Hydrometeor classification algorithm described in Wu et al [27]. This algorithm has been verified with the Zhuhai radar, the first operational PR in Guangdong, China.…”

Section: Dsd Characteristics In This Rainfall Event and Their Effectsmentioning

confidence: 98%

“…Updated in 2016, the S-band PR deployed in Guangzhou can measure multiple polarimetric variables such as Z H, Z DR , and Φ DP (differential phase). The radar was calibrated in the factory according to the criterion proposed by China Meteorological Administration (CMA) [27]. After radar construction, a series of tests, such as built-in testing, sun-calibration, and vertical pointed calibration were conducted by the Meteorological Observation Center of CMA.…”

Section: Measurement Instruments and Rainfall Eventmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Qpe Algorithm and Evaluation Methodsmentioning

confidence: 99%

Section: Qpe Algorithm and Evaluation Methodsmentioning

confidence: 99%

Section: Dsd Characteristics In This Rainfall Event and Their Effectsmentioning

confidence: 98%

Section: Measurement Instruments and Rainfall Eventmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2018

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“…The quality is affected by many factors, including partial beam blockages, bright band (BB), signal-to-noise ratio (SNR), and non-precipitation echoes. There have been many studies [13][14][15][16] on quality control methods for solving the problems caused by partial beam blockages, low SNR, and non-precipitation echoes, which are not studied here. When estimating QPE, BB should be paid attention to.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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Evaluating the Algorithm for Correction of the Bright Band Effects in QPEs with S-, C- and X-Band Dual-Polarized Radars

Cao

Fan

et al. 2018

Adv. Atmos. Sci.

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Statistics-based optimization of the polarimetric radar hydrometeor classification algorithm and its application for a squall line in South China

Cited by 32 publications

References 31 publications

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

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

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

Evaluating the Algorithm for Correction of the Bright Band Effects in QPEs with S-, C- and X-Band Dual-Polarized Radars

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