Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 9
Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4107-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-13-4107-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 9
Development and technical paper
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08 Sep 2020
Development and technical paper | Highlight paper |  | 08 Sep 2020

Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2

Shin-ichiro Shima, Yousuke Sato, Akihiro Hashimoto, and Ryohei Misumi

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Short summary
Using the super-droplet method, we constructed a detailed numerical model of mixed-phase clouds based on kinetic description and subsequently demonstrated that a large-eddy simulation of a cumulonimbus which predicts ice particle morphology without assuming ice categories or mass–dimension relationships is possible. Our results strongly support the particle-based modeling methodology’s efficacy for simulating mixed-phase clouds.
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