Improving Collisional Growth in Lagrangian Cloud Models: Development and Verification of a New Splitting Algorithm

Abstract

Abstract. Lagrangian cloud models (LCMs) are used increasingly in the cloud physics community. They not only enable a very detailed representation of cloud microphysics but also lack numerical errors typical for most other models. However, insufficient statistics, caused by an inadequate number of Lagrangian particles to represent cloud microphysical processes, can limit the applicability and validity of this approach. This study presents the first use of a splitting and merging algorithm designed to improve the warm cloud precipitation process by deliberately increasing or decreasing the number of Lagrangian 5 particles under appropriate conditions. This new approach and the details of how splitting is executed are evaluated in box and single-cloud simulations, as well as a shallow cumulus test case. The results indicate that splitting is essential for a proper representation of the precipitation process. Moreover, the details of the splitting method (i.e., identifying the appropriate conditions) become insignificant for larger model domains as long as a sufficiently large number of Lagrangian particles is produced by the algorithm. The accompanying merging algorithm is essential to constrict the number of Lagrangian particles in or-10 der to maintain the computational performance of the model. Overall, splitting and merging did not affect the life cycle and domain-averaged macroscopic properties of the simulated clouds. This new approach is a useful addition to all LCMs since it is able to significantly increase the number of Lagrangian particles in appropriate regions of the clouds, while maintaining a computationally feasible total number of Lagrangian particles in the entire model domain.

Collection/Aggregation in a Lagrangian cloud microphysical model: Insights from column model applications using LCM1D (v0.9)

“…The reason for this behavior is that the largest and hence fastest falling droplets are no longer confined to the same GB and to the same potential collection partners, hence increasing the ensemble of potential collection partners. A similar observation has been made by Schwenkel et al (2018), who used randomized motions between individual GBs. Overall, these results indicate that a simulation with only 24 SIPs per GB can yield reasonable results if (i) these SIPs are able to move between GBs and (ii) the SIP weighting factors are ideally chosen in the beginning by using an approriate SIP initialisation technique.…”

“…In a technical experiment, sedimentation is turned off, but SIPs are randomly redistributed inside the column after each time step (panel f) similar to Schwenkel et al (2018). Again, we find converged results for small κ-values down to 5 (panel f).…”

Overview of the PALM model system 6.0

“…The splitting algorithm is mainly steered by three parameters: 1. The minimum radius of superdroplets that will be split potentially, 2. a threshold for the weighting factor of that superdroplet (can either be prescribed or is approximated by assuming a gamma distribution, see Schwenkel et al (2018)), and 3. the splitting factor, which describes in how many particles one superdroplet will be split (prescribed or calculated by the LCM). However, the general splitting procedure is simple: If one superdroplet fulfills all criteria, the superdroplet is η spl − 1 times cloned and the weighting factor of the original and all new superdroplets is reduced to A n,new = A n /η spl , while η spl is the splitting factor, determining how many new superdroplets will be created during one operation.…”

“…By doing so, the first superdroplet is deleted and the weighting factor of the other superdroplet is adapted to obey mass conservation. The splitting/merging algorithm is described in detail in Schwenkel et al (2018). Their results show that the merging algorithm improves the representation of the collection process significantly, while decreasing computational time by up to 18% compared to a simulation with a globally increased superdroplet number.…”

Cloud Microphysical Implications for Marine Cloud Brightening: The Importance of the Seeded Particle Size Distribution

“…To represent the hydrometeors, several computational particles are placed in the single volume of the parcel model and about three million grid boxes in the 200 lowermost layers of the LES. For the parcel model (LES model), 100 (20) computational particles are assigned to each mode of the aforementioned background aerosol distribution, that is, 300 (60) in total, as well as additional 100 (20) computational particles for the seeded aerosol distribution, which is sufficient for an accurate representation of the microphysical processes that are encountered (Unterstrasser et al 2017(Unterstrasser et al , 2020Schwenkel et al 2018). A constant number of computational particles for the seeded aerosol has the advantage that the seeded and the background particles are always adequately represented, and not over or undersampled as the seeded aerosol concentration varies by four orders of magnitude.…”