Utilization of a C-band Polarimetric Radar for Severe Rainfall Event Analysis in Complex Terrain over Eastern China

Abstract

Polarimetric radar measurements and products perform as the cornerstones of modern severe weather warning and nowcast systems. Two radar quantitative precipitation estimation (QPE) frameworks, one based on a radar-gauge feedback mechanism and the other based on standard rain drop size distribution (DSD)-derived rainfall retrieval relationships, are both evaluated and investigated through an extreme severe convective rainfall event that occurred on 23 June 2015 in the mountainous region over eastern China, using the first routinely operational C-band polarimetric radar in China. Complex rainstorm characteristics, as indicated by polarimetric radar observables, are also presented to account for the severe rainfall field center located in the gap between gauge stations. Our results show that (i) the improvements of the gauge-feedback-derived radar QPE estimator can be attributed to the attenuation correction technique and dynamically adjusted Z–R relationships, but it greatly relies on the gauge measurement accuracy. (ii) A DSD-derived radar QPE estimator based on the specific differential phase (KDP) performs best among all rainfall estimators, and the interaction between the mesocyclone and the windward slope of the mountainous terrain can account for its apparent overestimation. (iii) The rainstorm is mainly dominated by small-sized and moderate-sized raindrops, with the mean volume diameter being less than 2 mm, but its KDP column (KDP > 3°·km−1) has a liquid water content that is higher than 2.4815 g·m−3, and a high raindrop concentration (Nw) with log10(Nw) exceeding 5.1 mm−1m−3. In addition, small hailstones falling and melting are also found in this event, which further aggregates Nw upon the severe rainfall center in the gap between gauge stations.

Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

“…Some microphysical information about the rainstorms can accordingly be deduced. Assuming the gamma model and neglecting the possible uncertainty induced by parameter u, the polynomial relationship between the median volume diameter (D0) and ZDR and the exponential relationship between the liquid water content (LWC, g·m −3 ) and KDP, which were fitted using the localized DSD dataset during the monsoon season of Hangzhou [30] and the normalized concentration of raindrops Nw (mm −1 ·m −3 ) presented in Ryzhkov et al (2014) and Testud et al (2001) [23,37], respectively, can be represented as Some microphysical information about the rainstorms can accordingly be deduced. Assuming the gamma model and neglecting the possible uncertainty induced by parameter u, the polynomial relationship between the median volume diameter (D 0 ) and Z DR and the exponential relationship between the liquid water content (LWC, g·m −3 ) and K DP , which were fitted using the localized DSD dataset during the monsoon season of Hangzhou [30] and the normalized concentration of raindrops N w (mm −1 ·m −3 ) presented in Ryzhkov et al (2014) and Testud et al (2001) [23,37], respectively, can be represented as…”

Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

“…Some microphysical information about the rainstorms can accordingly be deduced. Assuming the gamma model and neglecting the possible uncertainty induced by parameter u, the polynomial relationship between the median volume diameter (D0) and ZDR and the exponential relationship between the liquid water content (LWC, g•m −3 ) and KDP, which were fitted using the localized DSD dataset during the monsoon season of Hangzhou [30] Some microphysical information about the rainstorms can accordingly be deduced. Assuming the gamma model and neglecting the possible uncertainty induced by parameter u, the polynomial relationship between the median volume diameter (D 0 ) and Z DR and the exponential relationship between the liquid water content (LWC, g•m −3 ) and K DP , which were fitted using the localized DSD dataset during the monsoon season of Hangzhou [30] and the normalized concentration of raindrops N w (mm −1 •m −3 ) presented in Ryzhkov et al (2014) and Testud et al (2001) [23,37], respectively, can be represented as…”

“…Recently, all the operational weather radars in the U.S. (i.e., Next-Generation Radar-NEXRAD), UK, and Ireland have been upgraded with dual-polarization capability [10], which can transmit/receive horizontal The novel contributions of this paper are as follows: (i) We develop and test a new R(A H ,Z DR ) estimator for a C-band polarimetric (CPOL) radar for the first time, which utilizes the improved self-consistent attenuation correction method (i.e., the ZPHI approach) proposed by Bringi et al [28] through the incorporation of more microphysical constraints in the correction process of Z H and Z DR [29,30]. (ii) We also intercompare different radar rainfall estimators to address their advantages and disadvantages, utilizing the first high-quality and high-resolution (i.e., 125 m range resolution and 0.46 deg azimuthal resolution) CPOL radar deployed in Hangzhou, China and rainfall recordings of the highly dense gauge network around Hangzhou.…”

Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China

“…With the development of weather radar over the past 60 years, QPE, with its very high spatial and temporal resolutions, can accurately detect the location of precipitation, and can be applied to practical hydrological operations such as flood forecasting [9,10]. However, an error-free radar QPE is not possible due to various sources of error, such as indirect precipitation measurement, the Z-R relationship, being above the ground, beam shielding, and ground clutter, which result in range degradation [8,11,12]. Preserving the high spatial accuracy of rainfall in radar QPEs remains a challenge for meteorologists.…”