Assessing specific differential phase (K_DP)-based quantitative precipitation estimation for the record- breaking rainfall over Zhengzhou city on 20 July 2021

Abstract

Abstract. Although radar-based quantitative precipitation estimation (QPE) has been widely investigated from various perspectives, very few studies have been devoted to extreme-rainfall QPE. In this study, the performance of specific differential phase (KDP)-based QPE during the record-breaking Zhengzhou rainfall event that occurred on 20 July 2021 is assessed. Firstly, the OTT Parsivel disdrometer (OTT) observations are used as input for T-matrix simulation, and different assumptions are made to construct R(KDP) estimators. KDP estimates from three algorithms are then compared in order to obtain the best KDP estimates, and gauge observations are used to evaluate the R(KDP) estimates. Our results generally agree with previous known-truth tests and provide more practical insights from the perspective of QPE applications. For rainfall rates below 100 mm h−1, the R(KDP) agrees rather well with the gauge observations, and the selection of the KDP estimation method or controlling factor has a minimal impact on the QPE performance provided that the controlling factor used is not too extreme. For higher rain rates, a significant underestimation is found for the R(KDP), and a smaller window length results in a higher KDP and, thus, less underestimation of rain rates. We show that the QPE based on the “best KDP estimate” cannot reproduce the gauge measurement of 201.9 mm h−1 with commonly used assumptions for R(KDP), and the potential factors responsible for this result are discussed. We further show that the gauge with the 201.9 mm h−1 report was in the vicinity of local rainfall hot spots during the 16:00–17:00 LST period, while the 3 h rainfall accumulation center was located southwest of Zhengzhou city.

Revisiting Raindrop Axis Ratios Based on 3D Oblate Spheroidal Reconstruction: 2D Video Disdrometer Observations During Tropical Cyclone Passages

“…With the assumption that the raindrop canting angle satisfies a Gaussian distribution with a mean value of 0° and a standard deviation of 10.5° (shown in Figure 2), we assessed the impact of AR parameterizations on Z DR at a temperature of 20°C. The specific differential phase ( K DP ) was not presented here, although the effect of AR parameterization on K DP is also significant (Li et al., 2023), mainly because K DP is more sensitive to the raindrop concentration (dominated by small raindrops), while the focus of this study is large raindrops (diameter >3 mm). The raindrop size distribution is assumed to be a Gamma distribution with an intercept parameter ($$\mathrm{m}{\mathrm{m}}^{-1-\mu }\,{\mathrm{m}}^{-3}$$) of 1.…”

“…Although the shapes of raindrops periodically change as they fall, which is known as drop oscillations (Brant Foote, 1973; Szakáll et al., 2010; Thurai et al., 2014), their AR is statistically well correlated with the drop diameter (e.g., Beard & Chuang, 1987; Thurai & Bringi, 2005). The AR‐diameter parameterization lays the basis for polarimetric remote sensing of precipitation, such as raindrop size distribution inversion, quantitative precipitation estimation, and radar self‐consistency calibration (Bringi & Chandrasekar, 2001; Keenan et al., 2001; Li et al., 2023; Pei et al., 2014; Ryzhkov et al., 2005; Ryzhkov & Zrnic, 2019, and references therein). Specifically, radar polarimetric signatures that originate from this asymmetry have proliferated a novel understanding of precipitation microphysics (Kumjian et al., 2022; Kumjian & Prat, 2014), leading to more microphysically interpretable model simulations (Brown et al., 2016) as well as improved assimilation of radar observations by implementing polarimetric forward operators (Jung et al., 2010; Matsui et al., 2019; Oue et al., 2020; Trömel et al., 2021).…”

Raindrop Deformation in Turbulence

“…For example, Li et al. (2023) hypothesized that the underestimation of extreme rainfall is attributed to the assumed raindrop equilibrium state. In numerical models, raindrop coalescence and breakup remain the most uncertain and theoretically challenging liquid microphysical processes to quantify (Morrison et al., 2020).…”

“…We argue that the impact of turbulence may be considered in extreme conditions such as those that arise during typhoons, extreme rainfall, tornadoes, etc. For example, Li et al (2023) hypothesized that the underestimation of extreme rainfall is attributed to the assumed raindrop equilibrium state. In numerical models, raindrop coalescence and breakup remain the most uncertain and theoretically challenging liquid microphysical processes to quantify (Morrison et al, 2020).…”

“…In stagnant air, the physical behaviors of raindrops are relatively stable, known as the equilibrium state, and the shape, orientation as well as fall speed can be well parameterized as a function of raindrop diameter (Beard & Chuang, 1987). This equilibrium state assumption has facilitated the parameterization of rainfall processes in multiple applications, such as remote sensing (Chandrasekar et al., 2023; Kumjian et al., 2022; Ryzhkov & Zrnic, 2019; Zheng et al., 2024), numerical models (Lin et al., 1983; Matsui et al., 2019; Morrison & Milbrandt, 2015), disaster prevention/evaluation (Li et al., 2023; Vaezi et al., 2017), etc.…”