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a) N d * versus N dT * calculated with W b * from cloud base data shown in Figures 7-8. The CAS-DPOL values are
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Reliable aircraft measurements of cloud microphysical properties are essential for understanding liquid convective cloud formation. In September 2014, the properties of convective clouds were measured with a Cloud Combination Probe (CCP), a Cloud and Aerosol Spectrometer (CAS-DPOL), and a cloud condensation nuclei (CCN) counter on board the HALO (H...
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Quantifying the precipitation within clouds is a crucial challenge to improve our current understanding of the Earth’s hydrological cycle. We have investigated the relationship between the effective radius of droplets and ice particles (re) and precipitation water content (PWC) measured by cloud probes near the top of growing convective cumuli. The...
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... The information from CCP-CIPgs were used in this study to identify cloud with raindrops. From data of CAS-DPOL, CCP-CDP Braga et al. (2017b) and in the Supporting Information S1. ...
We investigated the relationship between the number concentration of cloud droplets (Nd) in ice‐free convective clouds and of particles large enough to act as cloud condensation nuclei (CCN) measured at the lateral boundaries of cloud elements. The data were collected during the ACRIDICON‐CHUVA aircraft campaign over the Amazon Basin. The results indicate that the CCN particles at the lateral cloud boundaries are dominated by detrainment from the cloud. The CCN concentrations detrained from non‐precipitating convective clouds are smaller compared to below cloud bases. The detrained CCN particles from precipitating cloud volumes have relatively larger sizes, but lower concentrations. Our findings indicate that CCN particles ingested from below cloud bases are activated into cloud droplets, which evaporate at the lateral boundaries and above cloud base and release the CCN again to ambient cloud‐free air, after some cloud processing. These results support the hypothesis that the CCN around the cloud are cloud‐processed.
... nuclei (IN) populations under undisturbed conditions (Pöschl et al., 2010;Prenni et al., 2009;Pöhlker et al., 2012Pöhlker et al., , 2016. Other sources of aerosol particles over the Amazon include long range Saharan dust and sea salt transport, biomass burning (either naturally-occurring or human-induced) and urban pollution downwind from cities and settlements (Talbot et al., 1988(Talbot et al., ,1990Cecchini et al., 2016;Martin et al., 2010;Kuhn et al., 2010). ...
... The time frame of the campaign corresponds to the local dry-to-wet season transition, when biomass burning is active in the southern Amazon (Artaxo et al. 2002, Andreae et al. 2015. 20 The flight paths followed a regular three-stage pattern: (i) Sampling of the air below clouds for aerosol characterization, (ii) Measurements of DSDs at cloud base, and (iii) Sampling of growing convective cloud tops (Braga et al., 2016;. Surface and thermodynamic conditions were different for the various flights (see Figure 1 and 3) with high contrasts in the north-south direction. ...
... The flow rate was set to 0.6 L min -1 , with a nominal cut-off particle size of 10 nm. NCCN at a given sources of uncertainty for the DSD measurements (Weigel et al., 2016). We excluded all cloud DSDs with droplet number concentrations (Nd) less than 1 cm -3 from further analysis. ...
The behavior of tropical clouds remains a major open scientific question, given that the associated phys-ics is not well represented by models. One challenge is to realistically reproduce cloud droplet size dis-tributions (DSD) and their evolution over time and space. Many applications, not limited to models, use the Gamma function to represent DSDs. However, there is almost no study dedicated to understanding the phase space of this function, which is given by the three parameters that define the DSD intercept, shape, and curvature. Gamma phase space may provide a common framework for parameterizations and inter-comparisons. Here, we introduce the phase-space approach and its characteristics, focusing on warm-phase microphysical cloud properties and the transition to the mixed-phase layer. We show that trajectories in this phase space can represent DSD evolution and can be related to growth processes. Condensational and collisional growth may be interpreted as pseudo-forces that induce displacements in opposite directions within the phase space. The actually observed movements in the phase space are a result of the combination of such pseudo-forces. Additionally, aerosol effects can be evaluated given their significant impact on DSDs. The DSDs associated with liquid droplets that favor cloud glaciation can be delimited in the phase space, which can help models to adequately predict the transition to the mixed phase. We also consider possible ways to constrain the DSD in two-moment bulk microphysics schemes, where the relative dispersion parameter of the DSD can play a significant role. Overall, the Gamma phase-space approach can be an invaluable tool for studying cloud microphysical evolution and can be readily applied in many scenarios that rely on Gamma DSDs.
... The CAS-DPOL (Cloud and Aerosol Spectrometer, with detector for polarization) instrument measures aerosol and cloud particles in the size range between 0.5 and 50.0 µm (Braga et al., 2016; by sensing individual particles passing a focused laser beam. The resulting intensity distribution of forward and backward scattered light is used to derive the 20 size distribution of the particles. ...
... The liquid water content (LWC) is measured with a King type LWC Hotwire (Braga et al., 2016) installed on the CAS-DPOL. ...
Vertical profiles of the cloud particle phase state in tropical deep-convective clouds (DCCs) were investigated using airborne solar radiation data collected by the German research aircraft HALO during the ACRIDICON-CHUVA campaign, which was conducted over the Brazilian Amazon in September 2014. A phase discrimination retrieval based on imaging spec-troradiometer measurements of cloud side spectral reflectivity was applied to DCCs under different aerosol conditions. From the retrieval results the height of the mixed phase layer of the DCCs was determined. The retrieved profiles were compared with in situ measurements and satellite observations. It was found that the depth and vertical position of the mixed phase layer can vary up to 900 m for one single cloud scene. In particular, this variation is attributed to the different stages of cloud development in one scene. Clouds of mature or decaying stage are affected by falling ice particles resulting in lower levels of fully glaciated cloud layers compared to growing clouds. Comparing polluted and moderate aerosol conditions revealed a shift of the lower boundary of the mixed phase layer from 5.6 ± 0.2 km (269 K) [moderate] to 6.2 ± 0.3 km (267 K) [polluted], and of the upper boundary from 6.8 ± 0.2 km (263 K) [moderate] to 7.4 ± 0.4 km (259 K) [polluted], as would be expected from theory.
