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- Author or Editor: Eunsil Jung x
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Abstract
Circulations in and around cumulus clouds are inferred by using a passive tracer (radar chaff) and an airborne cloud radar during the Barbados Aerosol Cloud Experiment (BACEX). The radar chaff elements used for this experiment are fibers that are cut to a length of about ½ of the radar wavelength to maximize radar returns by serving as dipole antennas. The fibers are packed in fiber tubes and are mounted in a dispenser beneath the wing of the aircraft. The chaff was released near the tops and edges of a growing small cumulus cloud. The aircraft then made penetrations of the cloud at lower levels to observe the chaff signals above the aircraft with the zenith-pointing cloud radar. This study shows that the environmental air above the cloud top descends along the downshear side of the cloud edge and is subsequently entrained back into the same cloud near the observation level. The in-cloud flow follows an inverted letter P pattern. The merits and limitations of the chaff method for tracking circulations in and around small cumuli are discussed.
Abstract
Circulations in and around cumulus clouds are inferred by using a passive tracer (radar chaff) and an airborne cloud radar during the Barbados Aerosol Cloud Experiment (BACEX). The radar chaff elements used for this experiment are fibers that are cut to a length of about ½ of the radar wavelength to maximize radar returns by serving as dipole antennas. The fibers are packed in fiber tubes and are mounted in a dispenser beneath the wing of the aircraft. The chaff was released near the tops and edges of a growing small cumulus cloud. The aircraft then made penetrations of the cloud at lower levels to observe the chaff signals above the aircraft with the zenith-pointing cloud radar. This study shows that the environmental air above the cloud top descends along the downshear side of the cloud edge and is subsequently entrained back into the same cloud near the observation level. The in-cloud flow follows an inverted letter P pattern. The merits and limitations of the chaff method for tracking circulations in and around small cumuli are discussed.
Abstract
The variability and the uncertainties in snowfall velocity measurements are addressed in this study. The authors consider (i) the instrumental uncertainty in the fall velocity measurement, (ii) the effect of unstable falling motion on the accuracy of velocity measurement, and (iii) the natural variability of homogeneous snow terminal fall velocity. It is shown that, when periods of homogeneous characteristics of snow are selected to minimize the mixture of particles of different origin, the standard deviation of snowfall velocity within each period tends to stabilize at a value between 0.1 and 0.2 m s−1.
In addition, the variability of snow terminal fall velocity is examined with three control variables: surface temperature Ts , echo-top temperature Tt , and the depth of precipitation system H. The results show that the exponent b in the power-law relationship V = aDb has little effect on the variability of snowfall velocity: the coefficient a correlates much better with the control variables (Ts , Tt , H) than the exponent b. Hence, snowfall velocity can be modeled with a varying coefficient a and a fixed exponent b = 0.18 (V = aD 0.18) with good accuracy.
Abstract
The variability and the uncertainties in snowfall velocity measurements are addressed in this study. The authors consider (i) the instrumental uncertainty in the fall velocity measurement, (ii) the effect of unstable falling motion on the accuracy of velocity measurement, and (iii) the natural variability of homogeneous snow terminal fall velocity. It is shown that, when periods of homogeneous characteristics of snow are selected to minimize the mixture of particles of different origin, the standard deviation of snowfall velocity within each period tends to stabilize at a value between 0.1 and 0.2 m s−1.
In addition, the variability of snow terminal fall velocity is examined with three control variables: surface temperature Ts , echo-top temperature Tt , and the depth of precipitation system H. The results show that the exponent b in the power-law relationship V = aDb has little effect on the variability of snowfall velocity: the coefficient a correlates much better with the control variables (Ts , Tt , H) than the exponent b. Hence, snowfall velocity can be modeled with a varying coefficient a and a fixed exponent b = 0.18 (V = aD 0.18) with good accuracy.
Abstract
For the first time, the Mie notch retrieval technique is applied to airborne cloud Doppler radar observations in warm precipitating clouds to retrieve the vertical air velocity profile above the aircraft. The retrieval algorithm prescribed here accounts for two major sources of bias: aircraft motion and horizontal wind. The retrieval methodology is evaluated using the aircraft in situ vertical air velocity measurements. The standard deviations of the residuals for the retrieved and in situ measured data for an 18-s time segment are 0.21 and 0.24 m s−1, respectively; the mean difference between the two is 0.01 m s−1. For the studied cases, the total theoretical uncertainty is less than 0.19 m s−1 and the actual retrieval uncertainty is about 0.1 m s−1. These results demonstrate that the Mie notch technique combined with the bias removal procedure described in this paper can successfully retrieve vertical air velocity from airborne radar observations with low spectral broadening due to Doppler fading, which enables new opportunities in cloud and precipitation research. A separate spectral peak due to returns from the cloud droplets is also observed in the same radar Doppler spectra and is also used to retrieve vertical air motion. The vertical air velocities retrieved using the two different methods agree well with each other, and the correlation coefficient is as high as 0.996, which indicates that the spectral peak due to cloud droplets might provide another way to retrieve vertical air velocity in clouds when the Mie notch is not detected but the cloud droplets’ spectral peak is discernable.
Abstract
For the first time, the Mie notch retrieval technique is applied to airborne cloud Doppler radar observations in warm precipitating clouds to retrieve the vertical air velocity profile above the aircraft. The retrieval algorithm prescribed here accounts for two major sources of bias: aircraft motion and horizontal wind. The retrieval methodology is evaluated using the aircraft in situ vertical air velocity measurements. The standard deviations of the residuals for the retrieved and in situ measured data for an 18-s time segment are 0.21 and 0.24 m s−1, respectively; the mean difference between the two is 0.01 m s−1. For the studied cases, the total theoretical uncertainty is less than 0.19 m s−1 and the actual retrieval uncertainty is about 0.1 m s−1. These results demonstrate that the Mie notch technique combined with the bias removal procedure described in this paper can successfully retrieve vertical air velocity from airborne radar observations with low spectral broadening due to Doppler fading, which enables new opportunities in cloud and precipitation research. A separate spectral peak due to returns from the cloud droplets is also observed in the same radar Doppler spectra and is also used to retrieve vertical air motion. The vertical air velocities retrieved using the two different methods agree well with each other, and the correlation coefficient is as high as 0.996, which indicates that the spectral peak due to cloud droplets might provide another way to retrieve vertical air velocity in clouds when the Mie notch is not detected but the cloud droplets’ spectral peak is discernable.
Abstract
Enhancement of the air refractive index structure parameter
Abstract
Enhancement of the air refractive index structure parameter
Aerosol–cloud–radiation interactions are widely held to be the largest single source of uncertainty in climate model projections of future radiative forcing due to increasing anthropogenic emissions. The underlying causes of this uncertainty among modeled predictions of climate are the gaps in our fundamental understanding of cloud processes. There has been significant progress with both observations and models in addressing these important questions but quantifying them correctly is nontrivial, thus limiting our ability to represent them in global climate models. The Eastern Pacific Emitted Aerosol Cloud Experiment (E-PEACE) 2011 was a targeted aircraft campaign with embedded modeling studies, using the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft and the research vessel Point Sur in July and August 2011 off the central coast of California, with a full payload of instruments to measure particle and cloud number, mass, composition, and water uptake distributions. EPEACE used three emitted particle sources to separate particle-induced feedbacks from dynamical variability, namely 1) shipboard smoke-generated particles with 0.05–1-μm diameters (which produced tracks measured by satellite and had drop composition characteristic of organic smoke), 2) combustion particles from container ships with 0.05–0.2-μm diameters (which were measured in a variety of conditions with droplets containing both organic and sulfate components), and 3) aircraft-based milled salt particles with 3–5-μm diameters (which showed enhanced drizzle rates in some clouds). The aircraft observations were consistent with past large-eddy simulations of deeper clouds in ship tracks and aerosol– cloud parcel modeling of cloud drop number and composition, providing quantitative constraints on aerosol effects on warm-cloud microphysics.
Aerosol–cloud–radiation interactions are widely held to be the largest single source of uncertainty in climate model projections of future radiative forcing due to increasing anthropogenic emissions. The underlying causes of this uncertainty among modeled predictions of climate are the gaps in our fundamental understanding of cloud processes. There has been significant progress with both observations and models in addressing these important questions but quantifying them correctly is nontrivial, thus limiting our ability to represent them in global climate models. The Eastern Pacific Emitted Aerosol Cloud Experiment (E-PEACE) 2011 was a targeted aircraft campaign with embedded modeling studies, using the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft and the research vessel Point Sur in July and August 2011 off the central coast of California, with a full payload of instruments to measure particle and cloud number, mass, composition, and water uptake distributions. EPEACE used three emitted particle sources to separate particle-induced feedbacks from dynamical variability, namely 1) shipboard smoke-generated particles with 0.05–1-μm diameters (which produced tracks measured by satellite and had drop composition characteristic of organic smoke), 2) combustion particles from container ships with 0.05–0.2-μm diameters (which were measured in a variety of conditions with droplets containing both organic and sulfate components), and 3) aircraft-based milled salt particles with 3–5-μm diameters (which showed enhanced drizzle rates in some clouds). The aircraft observations were consistent with past large-eddy simulations of deeper clouds in ship tracks and aerosol– cloud parcel modeling of cloud drop number and composition, providing quantitative constraints on aerosol effects on warm-cloud microphysics.
Abstract
Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.
Abstract
Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.
