Estimating collision–coalescence rates from in situ observations of marine stratocumulus

Abstract

Precipitation forms in warm clouds via collision–coalescence. This process is difficult to observe directly in situ and its implementation in numerical models is uncertain. We use aircraft observations of the drop‐size distribution (DSD) near marine stratocumulus tops to estimate collision–coalescence rates. Marine stratocumulus is a useful system to study collisional growth because it is initiated near the cloud top and the clouds evolve slowly enough to obtain statistically useful data from aircraft. We compare rate constants estimated from observations with reference rate constants derived from a collision–coalescence box model, the result of which is termed the enhancement factor (EF). We evaluate two hydrodynamic collision–coalescence kernels, one quiescent and one including the effects of small‐scale turbulence. Due to sampling volume limitations, DSDs must be averaged over length‐scales much greater than those relevant to the underlying physics, such that we also examine the role of averaging length‐scale with respect to process representation. Averaging length‐scales of 1.5 and 30 km are used, corresponding roughly to the horizontal grid lengths of cloud‐resolving models and high‐resolution climate models, respectively. EF values range from 0.1 to 40, with the greatest EFs associated with small mode diameter cases and a generally decreasing trend with drop size. For any given drop size or averaging length‐scale, there is about an order of magnitude variability in EFs. These results suggest that spatial variability on length‐scales smaller than 1.5 km prevents accurate retrieval of rate constants from large‐scale average DSDs. Large‐scale models must therefore account for small‐scale variability to represent cloud microphysical processes accurately. The turbulent kernel reduces EFs for all drop sizes, but can only account for at most half of the calculated EFs in marine stratocumulus.

Impact of hygroscopic seeding on the initiation of precipitation formation: results of a hybrid bin microphysics parcel model

“…The role of coarse particles in the precipitation formation has been studied in much research (e.g., Jensen and Lee, 2008;Witte et al, 2017). In the numerical models, it is generally assumed that coarse particles could form large drops at the cloud base and accelerate the collision-coalescence.…”

Comparison of Observed and Simulated Drop Size Distributions from Large-Eddy Simulations with Bin Microphysics

“…The full sampling range of one instrument could be used, but the CIP has known sizing issues for drops smaller than 100 mm (e.g., Strapp et al 2001); hence, it is desirable to minimize use of CIP bins smaller than 100 mm. While the PDI has no such sizing issues, its small sampling volume results in degradation of population statistics for drops significantly larger than ;80 mm in the relatively clean conditions observed during POST (12 of 17 flights had mean drop concentration N & 100 cm 23 ; Witte et al 2017). The crossover diameter is chosen to be 65 mm, such that there is almost no overlap between the two instruments.…”

“…These discrepancies occur despite minor differences in mean profiles of LWC and N and therefore must be caused by differences in the shape of modeled and observed DSDs, which arise from a combination of uncertainty in process rates, simplified representation of the underlying microphysics, and differences in thermodynamic forcing. Directly untangling the contribution of process rate uncertainty and flawed physics based on aircraft observations is challenging (e.g., Witte et al 2017), but model output makes this task more tractable because output DSD statistics are robust, and different processes can be selectively activated or deactivated within the microphysics scheme.…”

Droplet size distributions in turbulent clouds: experimental evaluation of theoretical distributions

“…; Witte et al . ). Cloud droplet growth prior to onset of collision–coalescence occurs through water vapour condensation, and therefore the central variable affecting the DSD is the water vapour supersaturation, including both its mean value and its variability (Ditas et al .…”

“…Physical processes affecting the cloud DSD include the curvature and solute effects (and associated mechanisms such as spectral ripening and competing activation of cloud condensation nuclei of varying size, solubility, etc.) (Johnson, 1982;Korolev, 1995;Wood et al 2002;Yang et al 2018), turbulent fluctuations (Cooper, 1989;Paoli and Shariff, 2009;Sardina et al 2015;Chandrakar et al 2016;Siewert et al 2017), entrainment and mixing (Baker et al 1980;Lehmann et al 2009;Yum et al 2015), microphysical variability (Cooper, 1989;Desai et al 2018), internal mixing of parcels of different growth history (Hudson and Yum, 1997;Lasher-Trapp et al 2005), and droplet collision-coalescence (Pruppacher and Klett, 2010;Glienke et al 2017;Witte et al 2017). Cloud droplet growth prior to onset of collision-coalescence occurs through water vapour condensation, and therefore the central variable affecting the DSD is the water vapour supersaturation, including both its mean value and its variability (Ditas et al 2012;Siebert and Shaw, 2017).…”