Radar Observations of Updrafts, Downdrafts, and Turbulence in Fair-Weather Cumuli

Kollias, Pavlos; Albrecht, Bruce A.; Lhermitte, Roger M.; Savtchenko, A.

doi:10.1175/1520-0469(2001)058<1750:roouda>2.0.co;2

Cited by 129 publications

(119 citation statements)

References 42 publications

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“…Rogers, 1976;Kollias et al, 2001;Khvorostyanov and Curry, 2002). If we limit our considerations to observations with only liquid cloud droplets present, the use of Doppler velocity as a proxy for the vertical air velocity can be justified.…”

Section: Observationsmentioning

confidence: 99%

“…The Doppler velocity is, in this case, related to the vertical motion of atmospheric particles (hydrometeors, aerosols, insects) and can be used to derive estimates of the statistics of atmospheric vertical velocities (e.g. Frisch et al, 1995;Feingold et al, 1999;Kollias and Albrecht, 2000;Kollias et al, 2001;O'Connor et al, 2005;Hogan et al, 2009). Similar to in-situ measurements, the vertically pointing remote sensing observations suffer from poor spatial coverage, but they have the important advantage of being able to measure different layers of the atmospheric vertical column simultaneously with good temporal resolution, which is practically impossible to attain with in-situ measurements.…”

Section: Introductionmentioning

confidence: 99%

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Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

et al. 2011

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Abstract. The statistics of cloud base vertical velocity simulated by the non-hydrostatic mesoscale model AROME are compared with Cloudnet remote sensing observations at two locations: the ARM SGP site in central Oklahoma, and the DWD observatory at Lindenberg, Germany. The results show that AROME significantly underestimates the variability of vertical velocity at cloud base compared to observations at their nominal resolution; the standard deviation of vertical velocity in the model is typically 4-8 times smaller than observed, and even more during the winter at Lindenberg. Averaging the observations to the horizontal scale corresponding to the physical grid spacing of AROME (2.5 km) explains 70-80 % of the underestimation by the model. Further averaging of the observations in the horizontal is required to match the model values for the standard deviation in vertical velocity. This indicates an effective horizontal resolution for the AROME model of at least 10 km in the presented case. Adding a TKE-term on the resolved grid-point vertical velocity can compensate for the underestimation, but only for altitudes below approximately the boundary layer top height. The results illustrate the need for a careful consideration of the scales the model is able to accurately resolve, as well as for a special treatment of sub-grid scale variability of vertical velocities in kilometer-scale atmospheric models, if processes such as aerosol-cloud interactions are to be included in the future.

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Section: Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

et al. 2011

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“…So, we can directly derive the vertical air velocity by using the left spectral bin, which regards the smallest cloud particles as a tracer of clear-air motions. This simple idea (here referred to as "small-particle-traced idea") was previously described by Gossard and Kollias and then further validated by Shupe [24][25][26]. Figure 4 shows a schematic of the small-particle-traced idea, where the represents the initial estimate of vertical air A schematic of the small-particle-traced idea, the blue dashed line is the estimated radar mean noise, the solid red line is the max noise, the ω represents the initial retrieval of vertical air velocity.…”

Section: Essential Ideamentioning

confidence: 95%

“…and this term can be estimated from equation (3) [17,[25][26]. The first term represents transverse shear and the second term represents radial shear.…”

Section: Integrated Retrieval Technique Using Multimode Doppler Spectramentioning

confidence: 99%

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Retrieving Vertical Air Velocity in Convective Cloud over the Tibetan Plateau from TIPEX-III Cloud Radar Doppler Spectra

Zheng¹,

Liu²,

Zhu³

et al. 2017

Preprint

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Abstract:In the summertime, convections occur frequently over the Tibetan Plateau (TP) because of the large dynamic and thermal effects of the landmass. Measurements of vertical air velocity in convective cloud are useful for advancing our understanding of the dynamic and microphysical mechanisms of clouds and can be used to improve the parameterization of current numerical models. This paper presents a technique for retrieving high-resolution vertical air velocity from convective cloud over the TP, by using Doppler spectra from a vertically pointing Ka-band cloud radar. The method is based on the development of a "smallparticle-traced" idea and the necessary data processing and uses three modes of radar. Spectral broadening corrections, uncertainty estimations, and result merging are used to ensure accurate results. Qualitative analysis of two typical convective cases shows that the retrievals are reliable and agree with the expectant results inferred from other radar measurements. A quantitative retrieval of vertical air motion from a ground-based optical disdrometer is used to preliminarily validate our radar-derived results. The comparison illustrates that while the data trends from the two methods of retrieval are similar, with the updrafts and downdrafts coinciding, cloud radar has a much higher resolution and can reveal the small-scale variation of vertical air motion.

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On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars

2016

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Coincident profiling observations from Doppler lidars and radars are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (i) Doppler radar velocity (DRV), (ii) Doppler lidar velocity (DLV), and (iii) Doppler radar spectrum width (DRW) measurements. The agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement (correlation coefficient of 0.86 and 0.78 for both cases analyzed here) during both drizzling and nondrizzling conditions. This suggests that unified (below and above cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced. This also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. Eddy dissipation rate estimates based on DRW measurements compare well with the estimates based on Doppler velocity but their performance deteriorates as precipitation size particles are introduced in the radar volume and broaden the DRW values. Based on this finding, a methodology to estimate the Doppler spectra broadening due to the spread of the drop size distribution is presented. The uncertainties in ε introduced by signal-to-noise conditions, the estimation of the horizontal wind, the selection of the averaging time window, and the presence of precipitation are discussed in detail.

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Radar Observations of Updrafts, Downdrafts, and Turbulence in Fair-Weather Cumuli

Cited by 129 publications

References 42 publications

Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

Retrieving Vertical Air Velocity in Convective Cloud over the Tibetan Plateau from TIPEX-III Cloud Radar Doppler Spectra

On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars

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