English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Lagrangian cloud models (LCMs) are increasingly used in the cloud physicscommunity. They not only enable a very detailed representation of cloudmicrophysics but also lack numerical errors typical for most other models.However, insufficient statistics, caused by an inadequate number ofLagrangian particles to represent cloud microphysical processes, can limitthe applicability and validity of this approach. This study presents thefirst use of a splitting and merging algorithm designed to improve the warmcloud precipitation process by deliberately increasing or decreasing thenumber of Lagrangian particles under appropriate conditions. This newapproach and the details of how splitting is executed are evaluated in boxand single-cloud simulations, as well as a shallow cumulus test case. Theresults indicate that splitting is essential for a proper representation ofthe precipitation process. Moreover, the details of the splitting method(i.e., identifying the appropriate conditions) become insignificant forlarger model domains as long as a sufficiently large number of Lagrangianparticles is produced by the algorithm. The accompanying merging algorithm isessential to constrict the number of Lagrangian particles in order tomaintain the computational performance of the model. Overall, splitting andmerging do not affect the life cycle and domain-averaged macroscopicproperties of the simulated clouds. This new approach is a useful addition toall LCMs since it is able to significantly increase the number of Lagrangianparticles in appropriate regions of the clouds, while maintaining acomputationally feasible total number of Lagrangian particles in the entiremodel domain.

Improving collisional growth in Lagrangian cloud models: development and verification of a new splitting algorithm