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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2018-110
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and technical paper
17 May 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
Improving Collisional Growth in Lagrangian Cloud Models: Development and Verification of a New Splitting Algorithm
Johannes Schwenkel1, Fabian Hoffmann1,a, and Siegfried Raasch1 1Institute of Meteorology and Climatology, Leibniz Universität Hannover, Hannover, Germany
aCurrent affiliation: Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, USA; NOAA Earth System Research Laboratory (ESRL) Chemical Sciences Division, Boulder, Colorado, USA
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 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 order 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.
Citation: Schwenkel, J., Hoffmann, F., and Raasch, S.: Improving Collisional Growth in Lagrangian Cloud Models: Development and Verification of a New Splitting Algorithm, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-110, in review, 2018.
Johannes Schwenkel et al.
Johannes Schwenkel et al.
Johannes Schwenkel et al.

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Short summary
Lagrangian cloud models are a powerful tool to understand cloud microphysics and are increasingly used in the cloud physics community. In this study we present a method designed to improve the warm cloud precipitation process in such models. Our results indicate that using this method is essential for a proper representation of the collisional process of warm clouds, while maintaining an appropriate computational demand.
Lagrangian cloud models are a powerful tool to understand cloud microphysics and are...
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