Reply to Comments on: `A comparison of optical measurements of liquid water content and drop size distribution in adiabatic regions of Florida cumuli'

1999 ◽  
Vol 50 (2) ◽  
pp. 77-80 ◽  
Author(s):  
R.P Lawson ◽  
A.M Blyth
Keyword(s):  
Water Content ◽  
Size Distribution ◽  
Liquid Water ◽  
Drop Size ◽  
Liquid Water Content ◽  
Drop Size Distribution ◽  
Optical Measurements

scholarly journals A Warm Rain Microphysics Parameterization that Includes the Effect of Turbulence

2008 ◽  
Vol 65 (6) ◽  
pp. 1795-1816 ◽  
Author(s):  
Charmaine N. Franklin
Keyword(s):  
Kinetic Energy ◽  
Size Distribution ◽  
Turbulent Kinetic Energy ◽  
Dissipation Rate ◽  
Drop Size ◽  
Reynolds Numbers ◽  
Liquid Water Content ◽  
Drop Size Distribution ◽  
Collision Kernel ◽  
Warm Rain

Abstract A warm rain parameterization has been developed by solving the stochastic collection equation with the use of turbulent collision kernels. The resulting parameterizations for the processes of autoconversion, accretion, and self-collection are functions of the turbulent intensity of the flow and are applicable to turbulent cloud conditions ranging in dissipation rates of turbulent kinetic energy from 100 to 1500 cm2 s−3. Turbulence has a significant effect on the acceleration of the drop size distribution and can reduce the time to the formation of raindrops. When the stochastic collection equation is solved with the gravitational collision kernel for an initial distribution with a liquid water content of 1 g m−3 and 240 drops cm−3 with a mean volume radius of 10 μm, the amount of mass that is transferred to drop sizes greater than 40 μm in radius after 20 min is 0.9% of the total mass. When the stochastic collection equation is solved with a turbulent collision kernel for collector drops in the range of 10–30 μm with a dissipation rate of turbulent kinetic energy equal to 100 cm2 s−3, this percentage increases to 21.4. Increasing the dissipation rate of turbulent kinetic energy to 500, 1000, and 1500 cm2 s−3 further increases the percentage of mass transferred to radii greater than 40 μm after 20 min to 41%, 52%, and 58%, respectively, showing a substantial acceleration of the drop size distribution when a turbulent collision kernel that includes both turbulent and gravitational forcing replaces the purely gravitational kernel. The warm rain microphysics parameterization has been developed from direct numerical simulation (DNS) results that are characterized by Reynolds numbers that are orders of magnitude smaller than those of atmospheric turbulence. The uncertainty involved with the extrapolation of the results to high Reynolds numbers, the use of gravitational collision efficiencies, and the range of the droplets for which the effect of turbulence has been included should all be considered when interpreting results based on these new microphysics parameterizations.

scholarly journals Estimating drizzle drop size and precipitation rate using two-colour lidar measurements

2010 ◽  
Vol 3 (3) ◽  
pp. 671-681 ◽  
Author(s):  
C. D. Westbrook ◽  
R. J. Hogan ◽  
E. J. O'Connor ◽  
A. J. Illingworth
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Drop Size ◽  
Liquid Water Content ◽  
Precipitation Rate ◽  
Infrared Light ◽  
Cloud Base ◽  
Size Spectrum ◽  
Drop Diameter ◽  
Lidar Measurements

Abstract. A method to estimate the size and liquid water content of drizzle drops using lidar measurements at two wavelengths is described. The method exploits the differential absorption of infrared light by liquid water at 905 nm and 1.5 μm, which leads to a different backscatter cross section for water drops larger than ≈50 μm. The ratio of backscatter measured from drizzle samples below cloud base at these two wavelengths (the colour ratio) provides a measure of the median volume drop diameter D0. This is a strong effect: for D0=200 μm, a colour ratio of ≈6 dB is predicted. Once D0 is known, the measured backscatter at 905 nm can be used to calculate the liquid water content (LWC) and other moments of the drizzle drop distribution. The method is applied to observations of drizzle falling from stratocumulus and stratus clouds. High resolution (32 s, 36 m) profiles of D0, LWC and precipitation rate R are derived. The main sources of error in the technique are the need to assume a value for the dispersion parameter μ in the drop size spectrum (leading to at most a 35% error in R) and the influence of aerosol returns on the retrieval (≈10% error in R for the cases considered here). Radar reflectivities are also computed from the lidar data, and compared to independent measurements from a colocated cloud radar, offering independent validation of the derived drop size distributions.

scholarly journals Estimating drizzle drop size and precipitation rate using two-colour lidar measurements

2010 ◽  
Vol 3 (2) ◽  
pp. 891-921
Author(s):  
C. D. Westbrook ◽  
R. J. Hogan ◽  
E. J. O'Connor ◽  
A. J. Illingworth
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Drop Size ◽  
Liquid Water Content ◽  
Precipitation Rate ◽  
Infrared Light ◽  
Cloud Base ◽  
Size Spectrum ◽  
Drop Diameter ◽  
Lidar Measurements

Abstract. A method to estimate the size and liquid water content of drizzle drops using lidar measurements at two wavelengths is described. The method exploits the differential absorption of infrared light by liquid water at 905 nm and 1.5 µm, which leads to a different backscatter cross section for water drops larger than ≈50 µm. The ratio of backscatter measured from drizzle samples below cloud base at these two wavelengths (the colour ratio) provides a measure of the median volume drop diameter D0. This is a strong effect: for D0=200 µm, a colour ratio of ≈6 dB is predicted. Once D0 is known, the measured backscatter at 905 nm can be used to calculate the liquid water content (LWC) and other moments of the drizzle drop distribution. The method is applied to observations of drizzle falling from stratocumulus and stratus clouds. High resolution (32 s, 36 m) profiles of D0, LWC and precipitation rate R are derived. The main sources of error in the technique are the need to assume a value for the dispersion parameter μ in the drop size spectrum (leading to at most a 35% error in R) and the influence of aerosol returns on the retrieval (≈10% error in R for the cases considered here). Radar reflectivities are also computed from the lidar data, and compared to independent measurements from a colocated cloud radar, offering independent validation of the derived drop size distributions.

scholarly journals A Model Intercomparison of CCN-Limited Tenuous Clouds in the High Arctic

2017 ◽  
Author(s):  
Robin G. Stevens ◽  
Katharina Loewe ◽  
Christopher Dearden ◽  
Antonios Dimitrelos ◽  
Anna Possner ◽  
...  
Keyword(s):  
Water Content ◽  
Size Distribution ◽  
Liquid Water ◽  
Atmospheric Stability ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Liquid Water Content ◽  
High Arctic ◽  
Model Intercomparison ◽  
Cloud Droplet Size Distribution

Abstract. We perform a model intercomparison of summertime high Arctic (> 80 N) clouds observed during the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign, when observed cloud condensation nuclei (CCN) concentrations fell below 1 cm−3. Previous analyses have suggested that at these low CCN concentrations the liquid water content (LWC) and radiative properties of the clouds are determined primarily by the CCN concentrations, conditions that have previously been referred to as the tenuous cloud regime. The intercomparison includes results from three large eddy simulation models (UCLALES-SALSA, COSMO-LES, and MIMICA) and three numerical weather prediction models (COSMO-NWP, WRF, and UM-CASIM). We test the sensitivities of the model results to different treatments of cloud droplet activation, including prescribed cloud droplet number concentrations (CDNC) and diagnostic CCN activation based on either fixed aerosol concentrations or prognostic aerosol with in-cloud processing. There remains considerable diversity even in experiments with prescribed CDNCs and prescribed ice crystal number concentrations (ICNC). The sensitivity of mixed-phase Arctic cloud properties to changes in CDNC depends on the representation of the cloud droplet size distribution within each model, which impacts on autoconversion rates. Our results therefore suggest that properly estimating aerosol–cloud interactions requires an appropriate treatment of the cloud droplet size distribution within models, as well as in-situ observations of hydrometeor size distributions to constrain them. The results strongly support the hypothesis that the liquid water content of these clouds is CCN-limited. For the observed meteorological conditions, the cloud generally did not collapse when the CCN concentration was held constant at the relatively high CCN concentrations measured during the cloudy period, but the cloud thins or collapses as the CCN concentration is reduced. The CCN concentration at which collapse occurs varies substantially between models. Only one model predicts complete dissipation of the cloud due to glaciation, and this occurs only for the largest prescribed ICNC tested in this study. Global and regional models with either prescribed CDNCs or prescribed aerosol concentrations would not reproduce these dissipation events. Additionally, future increases in Arctic aerosol concentrations would be expected to decrease the frequency of occurrence of such cloud dissipation events, with implications for the radiative balance at the surface. Our results also show that cooling of the sea-ice surface following cloud dissipation increases atmospheric stability near the surface, further suppressing cloud formation. Therefore, this suggests that linkages between aerosol and clouds, as well as linkages between clouds, surface temperatures and atmospheric stability need to be considered for weather and climate predictions in this region.

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