Wind Effects on the Shape of Raindrop Size Distribution

Testik, Firat Yener; Pei, Bin

doi:10.1175/jhm-d-16-0211.1

Cited by 29 publications

(36 citation statements)

References 65 publications

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“…We have plotted the MVDs calculated from the impact disdrometer data against the horizontal wind speed from the AWS located in the HACPL site for strongly electrified and weakly electrified rain events for the same data periods used in this study (Figure a). Testik and Pei () observed an increase of number of large raindrops with the increase of LWC. For the present study, we have derived the LWC and MVD using formulas and , respectively, from the disdrometer measured DSD for all the rain events and plotted the results in Figure b.…”

Section: Resultsmentioning

confidence: 92%

“…where N 0 is the intercept parameter (mm −1−μ · m −3 ) and R is the rain rate (mm/hr) measured by the impact disdrometer. For the present study, N 0 values are calculated using the formula (Bringi & Chandrasekar, )

N_{0} = N_{w} \frac{bold6 {(μ + 4)}^{bold-italicμ + bold4}}{4^{4} boldΓ ((), boldμ + bold4)} D_{m}^{- bold-italicμ}

where г is the gamma function and D m is the MVD given by

D_{m} = \frac{\int_{{bold-italicD}_{boldmin}}^{{bold-italicD}_{boldmax}} D^{4} bold-italicN ((), D) bold-italicd ((), D)}{\int_{{bold-italicD}_{boldmin}}^{{bold-italicD}_{boldmax}} D^{3} bold-italicN ((), D) bold-italicd ((), D)}

μ is the gamma distribution shape parameter, given by the empirical relation (Testik & Pei, )

bold-italicμ = \frac{{bold-italicD}_{bold-italicm}^{- 0.66}}{{bold0.3}^{bold2}} - bold4

…”

Section: Resultsmentioning

confidence: 99%

“…These figures also show a clear difference between DSDs of strongly electrified and weakly electrified events consistent with changes in DSD at higher levels. Erpul et al (2000) and Testik and Pei (2017) have shown that the horizontal wind speed and LWC can influence the rain DSD. Erpul et al (2000) reported larger median drop size in wind-driven rain compared to windless rain in a wind tunnel study.…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 96%

“…μ is the gamma distribution shape parameter, given by the empirical relation (Testik & Pei, 2017) μ ¼ D À0:66 m 0:3 2 À 4 (4) N w is the DSD parameter given by…”

Section: /2017jd028205mentioning

confidence: 99%

See 3 more Smart Citations

Quantification of Observed Electrical Effect on the Raindrop Size Distribution in Tropical Clouds

et al. 2018

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In the backdrop of extensive laboratory and theoretical evidence of broadening of the drop size distribution (DSD) of raindrops in the presence of electric field, quantification of the same in observed tropical clouds is lacking. Here this is quantified using the DSD measured by a microrain radar at 2,400‐, 1,200‐, and 600‐m heights from the surface in six strongly electrified and six weakly electrified stratiform rain events together with the DSD of raindrops at the surface measured by a disdrometer for the same cases. The presence/absence of lightning is used to distinguish between strongly and weakly electrified events. The vertical profile of Median Volume Diameter below the melting layer and DSDs at all three heights for strongly electrified events and weakly electrified events are significantly different from each other, consistent with previous laboratory and numerical studies (Rayleigh, 1879; Davis, 1964; Moore et al., 1964, https://doi.org/10.1175/1520-0469(1964)021<0646:GORAMA>2.0.CO;2). Our results indicate that the electric field and surface charge of raindrops can affect the collision‐coalescence process and breakup characteristics of raindrops. Our study suggests that the parameterization of electrical processes in weather/climate models can possibly improve the simulation of tropical rainfall in numerical models as well as a proper representation of DSD will improve the estimation of tropical rainfall in airborne measurements.

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Section: Resultsmentioning

confidence: 92%

N_{0} = N_{w} \frac{bold6 {(μ + 4)}^{bold-italicμ + bold4}}{4^{4} boldΓ ((), boldμ + bold4)} D_{m}^{- bold-italicμ}

where г is the gamma function and D m is the MVD given by

D_{m} = \frac{\int_{{bold-italicD}_{boldmin}}^{{bold-italicD}_{boldmax}} D^{4} bold-italicN ((), D) bold-italicd ((), D)}{\int_{{bold-italicD}_{boldmin}}^{{bold-italicD}_{boldmax}} D^{3} bold-italicN ((), D) bold-italicd ((), D)}

μ is the gamma distribution shape parameter, given by the empirical relation (Testik & Pei, )

bold-italicμ = \frac{{bold-italicD}_{bold-italicm}^{- 0.66}}{{bold0.3}^{bold2}} - bold4

…”

Section: Resultsmentioning

confidence: 99%

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 96%

“…μ is the gamma distribution shape parameter, given by the empirical relation (Testik & Pei, 2017) μ ¼ D À0:66 m 0:3 2 À 4 (4) N w is the DSD parameter given by…”

Section: /2017jd028205mentioning

confidence: 99%

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Quantification of Observed Electrical Effect on the Raindrop Size Distribution in Tropical Clouds

et al. 2018

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show abstract

“…The maximum colliding angle occurs between the largest raindrop and the smallest one, while the angle equals to 0 between the raindrops with the same diameter. Note that the total number of raindrop breakup is larger than the number of breakup predictions presented in region II, since predictions fall into region I (coalescence) include both coalescence and neck breakup outcome, whereas predictions that fall into region II include only breakup outcome (Testik & Pei, ).…”

Section: Binary Colliding Raindrops and Their Parameterizationmentioning

confidence: 99%

A Modeling Study of the Effects of Vertical Wind Shear on the Raindrop Size Distribution in Typhoon Nida (2016)

Deng

Duan

2019

JGR Atmospheres

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In this study, the effects of vertical wind shear (VWS) on the raindrop size distribution in tropical cyclone are investigated, based on the theoretical analyses that intense VWS, which commonly appears in the lower layers of tropical cyclones, can enhance the collisional breakup of raindrops. This is achieved by comparing the numerical sensitivity experiments of Typhoon Nida (2016) using Weather Research and Forecasting model against the polarimetric radar and disdrometer observations. In the control run with the default Morrison microphysics, unrealistic large raindrops are produced with excessively large differential reflectivity and heavier precipitation. An obvious decrease of raindrop size is present when the constant value of cloud droplet number concentration reduces from 250 to 30 cm−3 (NC30), leading to more comparable microphysics characteristics with the observations due to the enhancement of autoconversion rate from cloud droplets to raindrops; however, some unrealistic large‐sized raindrops still exist. A semiempirical raindrop collection/breakup parameterization is further proposed in NC30_WS run by modifying the threshold diameter of raindrops at which breakup occurs as a function of VWS. The certain improvements of simulating raindrop size distribution and precipitation in Typhoon Nida are present in NC30_WS run, owing to the efficient collisional breakup and the induced stronger raindrop evaporation cooling. Our results indicate that the combined effects of reasonable cloud droplet numbers as well as reliable raindrop breakup parameterization are pronounced and highlight the impacts of VWS on raindrop size distribution, which should be involved in current microphysical schemes to forecast the TCs more accurately.

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Influence of below‐cloud evaporation on stable isotopes of precipitation in the Yellow River source region

Ye,

Zhu,

Chen

et al. 2024

Hydrological Processes

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The study of below‐cloud evaporation effects under clouds in the Yellow River source region is of great significance for regional water resource generation as well as for water resource security in the arid and semi‐arid regions of northern China. In this study, we quantitatively assessed the evapotranspiration effect in the Yellow River source region from March to November based on the improved Stewart model. The study concluded that: (1) below‐cloud evaporation was slightly higher in summer than in other seasons (residual fractions of raindrop evaporation were 80.57% in summer, 81.12% in spring, and 84.2% in autumn, respectively); and (2) sub‐cloud evaporation diminishes with increasing altitude (residual fractions of raindrop evaporation were 83.09% in the western part of the area, 81.82% in the central part of the area, and 81.36% in the eastern part of the area, respectively). (3) The total linear index between study areas f and ∆d is 2.24, where f > 95%, it is 1.19; that is, the evaporation of raindrops increases by 1% and the reduction in the excess of mercury by about 2‰. (4) Local meteorological factors (temperature, precipitation, and relative humidity) and raindrop diameter have a cross‐influence on below‐cloud evaporation, with relative humidity having the most significant effect, with the highest correlation coefficient of 3.03 when relative humidity is less than 70%. The results of the study can provide a parameter basis for hydrological and climatic models in the Yellow River Basin.

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Wind Effects on the Shape of Raindrop Size Distribution

Cited by 29 publications

References 65 publications

Quantification of Observed Electrical Effect on the Raindrop Size Distribution in Tropical Clouds

Quantification of Observed Electrical Effect on the Raindrop Size Distribution in Tropical Clouds

A Modeling Study of the Effects of Vertical Wind Shear on the Raindrop Size Distribution in Typhoon Nida (2016)

Influence of below‐cloud evaporation on stable isotopes of precipitation in the Yellow River source region

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