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

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

doi:https://doi.org/10.5194/gmd-13-4107-2020

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:

Particle-based methods for simulating atmospheric aerosol...

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

| Highlight paper

|

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

Model code and software

SCALE-SDM_0.2.5-2.2.0, -2.2.1, and -2.2.2 Shin-ichiro Shima https://doi.org/10.5281/zenodo.3483650

Video supplement

Supplement to the article "Predicting the morphology of ice particles in deep convection using the super-droplet method" (Shima et al., 2020, GMD) Shin-ichiro Shima https://doi.org/10.5281/zenodo.3478207

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.