A Lagrangian drop model to study warm rain microphysical processes in shallow cumulus

Abstract

In this study, we introduce a Lagrangian drop (LD) model to study warm rain microphysical processes in shallow cumulus. The approach combines Large-Eddy Simulations (LES) including a bulk microphysics parameterization with an LD model for raindrop growth. The LD model is one-way coupled with the Eulerian LES and represents all relevant rain microphysical processes such as evaporation, accretion, and selfcollection among LDs as well as dynamical effects such as sedimentation and inertia. To test whether the LD model is fit for purpose, a sensitivity study for isolated shallow cumulus clouds is conducted. We show that the surface precipitation rate and the development of the raindrop size distribution are sensitive to the treatment of selfcollection in the LD model. Some uncertainty remains for the contribution of the subgrid-scale turbulence to the relative velocity difference of a pair of LDs, which appears as a factor in the collision kernel. Sensitivities to other model parameters such as the initial multiplicity or the initial mass distribution are small. Overall, sensitivities of the LD model are small compared to the uncertainties in the assumptions of the bulk rain microphysics scheme, and the LD model is well suited for particle-based studies of raindrop growth and dynamics. This opens up the opportunity to study effects like recirculation, deviations from terminal fall velocity and other microphysical phenomena that so far were not accessible for bin, bulk, or parcel models.

On the limits of Köhler activation theory: how do collision and coalescence affect the activation of aerosols?

“…One can refer to Riechelmann et al (2012) for the original description, Hoffmann et al (2015) for the consideration of aerosols during diffusional growth, and Hoffmann et al (2017) for the most recent description of the LCM. This LCM, as with all other available particle-based cloud physical models (Andrejczuk et al, 2008;Shima et al, 2009;Sölch and Kärcher, 2010;Naumann and Seifert, 2015), is based on the so-called super-droplet approach in which each simulated particle represents an ensemble of identical, real particles, growing continuously from an aerosol to a cloud droplet. The number of particles within this ensemble, the so-called weighting factor, is a unique feature of each particle, which is considered for an appropriate physical representation of cloud microphysics within the super-droplet approach.…”

“…The LCM is based on a recently developed approach which simulates individual particles that represent an ensemble of identical particles and maintains, as an inherent part of this approach, the identity of droplets and their aerosols throughout the simulation (Andrejczuk et al, 2008;Shima et al, 2009;Sölch and Kärcher, 2010;Riechelmann et al, 2012;Naumann and Seifert, 2015). A summary of the governing equations and the extensions carried out for this study to treat aerosol mass change during collision and coalescence is given in the Appendix A.…”

Recirculation and growth of raindrops in simulated shallow cumulus

“…The momentum equation for each LD includes dynamical effects such as sedimentation and inertia, and a contribution from the parameterized subgrid‐scale fluid velocity (equations and therein). Naumann and Seifert [] show that the uncertainty of the LD model is much smaller than the uncertainty caused by the choice of the shape parameter in a two‐moment bulk rain microphysics scheme.…”

“…To analyze the LDs' trajectories and their growth histories, all LDs have a unique identification number and their properties are saved to output files every 15 s. The process of selfcollection, i.e., the collision‐coalescence of pairs of LDs, introduces some ambiguity in the history of individual LDs. After each selfcollection event, one of the two LDs that coalesce decreases in multiplicity but retains its mass [ Naumann and Seifert , , equations (8)–(11) therein]. Obviously, this LD keeps its trajectory and its growth history also after the selfcollection event.…”

Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation

“…An interesting question is to explain why the CDSD is wider than predicted and the presence of the large droplet sizes in the tail of the distribution (e.g., Siebert and Shaw, 2017), which might be related to the fast-rain process in the atmosphere (e.g., Göke et al, 2007). Several pos- 15 sible mechanisms have been proposed, including the existence of giant cloud condensational nuclei (GCCN, usually defined as aerosols with dry diameter larger than few µm) (e.g., Feingold et al, 1999;Yin et al, 2000;Jensen and Lee, 2008;Cheng et al, 2009;Jensen and Nugent, 2017), lucky cloud droplets (e.g., Kostinski and Shaw, 2005;Naumann and Seifert, 2015;Lozar and Muessle, 2016), mixing with environmental air (e.g., Lasher-Trapp et al, 2005;Cooper et al, 2013;Korolev et al, 2013;Yang et al, 2016), supersaturation fluctuations (e.g., Chandrakar et al, 2016;Siebert and Shaw, 2017), and enhancement of collision 20 efficiency due to turbulence or charge (e.g., Paluch, 1970;Grabowski and Wang, 2013;Falkovich and Pumir, 2015;Lu and Shaw, 2015). Recently, Jensen and Nugent (2017) investigated the effect of GCCN on droplet growth and rain formation using a cloud parcel model.…”