Convective cloud vertical velocity and mass‐flux characteristics from radar wind profiler observations during GoAmazon2014/5

Abstract

A radar wind profiler data set collected during the 2 year Department of Energy Atmospheric Radiation Measurement Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign is used to estimate convective cloud vertical velocity, area fraction, and mass flux profiles. Vertical velocity observations are presented using cumulative frequency histograms and weighted mean profiles to provide insights in a manner suitable for global climate model scale comparisons (spatial domains from 20 km to 60 km). Convective profile sensitivity to changes in environmental conditions and seasonal regime controls is also considered. Aggregate and ensemble average vertical velocity, convective area fraction, and mass flux profiles, as well as magnitudes and relative profile behaviors, are found consistent with previous studies. Updrafts and downdrafts increase in magnitude with height to midlevels (6 to 10 km), with updraft area also increasing with height. Updraft mass flux profiles similarly increase with height, showing a peak in magnitude near 8 km. Downdrafts are observed to be most frequent below the freezing level, with downdraft area monotonically decreasing with height. Updraft and downdraft profile behaviors are further stratified according to environmental controls. These results indicate stronger vertical velocity profile behaviors under higher convective available potential energy and lower low‐level moisture conditions. Sharp contrasts in convective area fraction and mass flux profiles are most pronounced when retrievals are segregated according to Amazonian wet and dry season conditions. During this deployment, wet season regimes favored higher domain mass flux profiles, attributed to more frequent convection that offsets weaker average convective cell vertical velocities.

Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA

“…This is likely associated with crystal orientation by the electrical field as more lightning occurs (see Williams et al, 2002), and more vertically oriented ice shapes such as graupel may occur during the dry season than the wet season. The KDP distribution shows considerably larger values in the warm layer during the dry compared to the wet season, indicating that the higher RRs and greater number of positive values in the mixed phase are probably associated with intense updrafts, as shown by Giangrande et al (2016). The correlation coefficient highlights an interesting feature.…”

“…Two different PARSIVEL disdrometers were used during the entire campaign: one during the CHUVA project from January to September 2014 and another, the ARM (Atmospheric Radiation Measurement), from September 2014 to October 2015. Raindrops larger than 5 mm were eliminated from the dataset to best match the co-located rain gauge accumulated rainfall, and a complementary filter was applied as described by Giangrande et al (2016). The drop size distribution (DSD) and all respective rainfall rates (RRs, in mm h −1 ) and mass-weighted mean diameters (D m , in mm) were obtained in 5 min intervals for periods during which RR ≥ 0.5 mm h −1 , as suggested by Tokay et al (2013).…”

“…Jäkel et al (2017) have presented a new methodology to retrieve the vertical distribution of the hydrometeor phase using cloud-side reflected solar radiation measurements and have discussed the mixed-phase layer as a function of aerosol loading. Giangrande et al (2016) have presented the statistical behaviour of vertical cloud motions as a function of season, instability, and convective inhibition. Burleyson et al (2016) have discussed the diurnal cycle and spatial variability of deep convection among the different GoAmazon sites.…”

Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E

“…However, practical hazards and operational costs have resulted in a valuable, but limited, dataset (e.g., Byers and Braham, 1948;LeMone and Zipser, 1980;Donner et al, 2001). Recent studies using profiling Doppler radars have suggested an ability to retrieve vertical velocities in convective clouds with an uncertainty of the order of 1-2 m s −1 , thus offering a viable substitute for in situ aircraft measurements (Jorgensen and LeMone, 1989;Cifelli and Rutledge, 1994;May and Rajopadhyaya, 1999;Williams, 2012;Heymsfield et al, 2010;Giangrande et al, 2013a;Kumar et al, 2015;Giangrande et al, 2016). Furthermore, profiling radars provide a high degree of detail of convective clouds in time and height, and can sample even the most intense convective cores.…”

“…Several studies have investigated multi-Doppler wind retrieval uncertainties by identifying or utilizing (i) the importance of Doppler radar measurement errors and beam geometry (e.g., Doviak et al, 1976;Nelson and Brown, 1987;Matejka and Bartels, 1998;Bousquet et al, 2008), (ii) the influence of radar data objective analysis (e.g., Clark et al, 1980;Gal-Chen, 1982;Testud and Chong, 1983;Given and Ray, 1994;Majcen et al, 2008;Shapiro et al, 2010;Collis et al, 2010), and (iii) OSSEs (e.g., Fanyou and Jietai, 1994;Gao et al, 1999;Liou and Chang, 2009;Potvin et al, 2012b;Potvin and Wicker, 2012). However, few studies have compared practical retrieval performance to other independent air motion estimates from aircraft or ground-based profiling radars (e.g., Collis et al, 2013;Newsom et al, 2014).…”