Comparison of rainfall microphysics characteristics derived by numerical weather prediction modelling and dual‐frequency precipitation radar

Zhu, Jingxuan; Zhang, Shuliang; Yang, Qiqi; Shen, Qi; Zhuo, Lu; Dai, Qiang

doi:10.1002/met.2000

Cited by 12 publications

(12 citation statements)

References 66 publications

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“…4 and to the convective precipitation in Eq. 5, as recommended by Iguchi et al (37) I DSD ¼ 0:401ε (20).…”

Section: Dsd Retrieval Model Using Radar Measurementsmentioning

confidence: 99%

“…In recent years, ground-and space-based radars have shown promising capabilities for monitoring RKE fluxes (20)(21)(22). Compared to the ground dual-polarization radars that are available in limited areas (23), the space-borne dual-frequency precipitation radar (DPR) radar contains Ku band (13.6 GHz) and Ka band (35.5 GHz) on the Global Precipitation Measurement (GPM) core satellite.…”

Section: Introductionmentioning

confidence: 99%

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Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale

Dai,

Zhu,

et al. 2023

Sci. Adv.

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Rainfall kinetic energy (RKE) constitutes one of the most critical factors that drive rainfall erosivity on surface soil. Direct measurements of RKE are limited, relying instead on the empirical relations between kinetic energy and rainfall intensity ( KE-I relation), which have not been well regionalized for data-scarce regions. Here, we present the first global rainfall microphysics–based RKE ( RKE MPH ) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKE MPH flux outperforms the RKE estimates derived from a widely used empirical KE-I relation ( RKE KE-I ) validated using ground disdrometers. We found a potentially widespread underestimation of RKE KE-I , which is especially prominent in some low-income countries with ~20% underestimation of RKE and the resultant rainfall erosivity. Given the evidence that these countries are subject to greater rainfall-induced soil erosion, these underestimations would mislead conservation practices for sustainable development of terrestrial ecosystems.

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“…4 and to the convective precipitation in Eq. 5, as recommended by Iguchi et al (37) I DSD ¼ 0:401ε (20).…”

Section: Dsd Retrieval Model Using Radar Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale

Dai,

Zhu,

et al. 2023

Sci. Adv.

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“…Raindrop size distribution (RSD) provides fundamental information for characterizing the microphysical properties of precipitation and is an important factor in determining the accuracy of rainfall retrieval for radar-based quantitative precipitation estimation and rainfall simulation of numerical weather prediction systems [1][2][3]. Moreover, RSD also plays an important role in calculating rainfall kinetic energy [4], which is a dominant parameter affecting soil erosion [5,6].…”

Section: Introductionmentioning

confidence: 99%

WRF Rainfall Modeling Post-Processing by Adaptive Parameterization of Raindrop Size Distribution: A Case Study on the United Kingdom

et al. 2021

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Raindrop size distribution (RSD) is a key parameter in the Weather Research and Forecasting (WRF) model for rainfall estimation, with gamma distribution models commonly used to describe RSD under WRF microphysical parameterizations. The RSD model sets the shape parameter (μ) as a constant of gamma distribution in WRF double-moment bulk microphysics schemes. Here, we propose to improve the gamma RSD model with an adaptive value of μ based on the rainfall intensity and season, designed using a genetic algorithm (GA) and the linear least-squares method. The model can be described as a piecewise post-processing function that is constant when rainfall intensity is <1.5 mm/h and linear otherwise. Our numerical simulation uses the WRF driven by an ERA-interim dataset with three distinct double-moment bulk microphysical parameterizations, namely, the Morrison, WDM6, and Thompson aerosol-aware schemes for the period of 2013–2017 over the United Kingdom at a 5 km resolution. Observations were made using a disdrometer and 241 rain gauges, which were used for calibration and validation. The results show that the adaptive-μ model of the gamma distribution was more accurate than the gamma RSD model with a constant shape parameter, with the root-mean-square error decreasing by averages of 23.62%, 11.33%, and 22.21% for the Morrison, WDM6, and Thompson aerosol-aware schemes, respectively. This model improves the accuracy of WRF rainfall simulation by applying adaptive RSD parameterization and can be integrated into the simulation of WRF double-moment microphysics schemes. The physical mechanism of the RSD model remains to be determined to improve its performance in WRF bulk microphysics schemes.

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“…Mitigation and assessment of heavy rainfall events dictate to progress the precipitation estimation algorithms and cloud modeling simulations based on the enhanced understanding of cloud and rain microphysical process [11][12][13][14][15][16] . Such goals in Taiwan can be achieved with the aid of precipitation measurement instruments like disdrometers and radars.…”

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confidence: 99%

Regional discrepancies in the microphysical attributes of summer season rainfall over Taiwan using GPM DPR

Janapati¹,

Seela²,

Lin³

2023

Sci Rep

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Taiwan, an island located in the northwest Pacific region, is influenced by heavy rainfall events during warm seasons, particularly from June to August. Interaction of precipitating clouds with the complex topography results in inhomogeneous and intense rainfall over Taiwan. Hence, the present study investigates the regional discrepancies in the microphysical characteristics of summer season rainfall over (north, south, east, and central) Taiwan using 9 years (2014–2022) of GPM DPR measurements. The results showed clear distinctions in the precipitation and raindrop size distributions over the north, south, east, and central Taiwan. The contoured frequency by altitude diagrams (CFADs) of radar reflectivity, rainfall rate, drop diameter, and concentration clearly infer the dominance of large-size super cooled liquid and ice particles above the melting layer and rain particles below the melting layers in the south and central Taiwan. Central (north) Taiwan is dominated by large-size (small) drops among four regions. Higher concentrations of large drops over central Taiwan (principally from convective precipitation) and south Taiwan (primarily from stratiform precipitation) is attributed to higher rainfall amounts over these two regions than the north and east Taiwan. Furthermore, irrespective of precipitation type and geographic region, summer monsoon rainfall over Taiwan is dominated by coalescence and breakup processes. The microphysical characteristics of summer season rainfall addressed in this study could assist in refining the cloud modeling simulations over complex topography in Taiwan.

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Comparison of rainfall microphysics characteristics derived by numerical weather prediction modelling and dual‐frequency precipitation radar

Cited by 12 publications

References 66 publications

Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale

Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale

WRF Rainfall Modeling Post-Processing by Adaptive Parameterization of Raindrop Size Distribution: A Case Study on the United Kingdom

Regional discrepancies in the microphysical attributes of summer season rainfall over Taiwan using GPM DPR

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