Predicting the morphology of ice particles in deep convection using the super-droplet method
- 1Graduate School of Simulation Studies, University of Hyogo, Kobe, Japan (s_shima@sim.u-hyogo.ac.jp)
- 2RIKEN Center for Computational Science, Kobe, Japan
- 3Department of Earth and Planetary Sciences, Faculty of Science, Hokkaido University, Sapporo, Japan
- 4Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
- 5National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan
In this presentation, we summarize the main results of Shima et al. (2019). The super-droplet method (SDM) is a particle-based numerical algorithm that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, we developed a detailed numerical model of mixed-phase clouds in which ice morphologies are explicitly predicted without assuming ice categories or mass-dimension relationships. Ice particles are approximated as porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; collision-coalescence, -riming, and -aggregation. To evaluate the model's performance, we conducted a 2D large-eddy simulation of a cumulonimbus. The results well capture characteristics of a real cumulonimbus. The mass-dimension and velocity-dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128/cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds.
Shima, S., Sato, Y., Hashimoto, A., and Misumi, R.: 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, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-294, 1-83, 2019.
How to cite: Shima, S., Sato, Y., Hashimoto, A., and Misumi, R.: Predicting the morphology of ice particles in deep convection using the super-droplet method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2097, https://doi.org/10.5194/egusphere-egu2020-2097, 2020
How to cite: Shima, S., Sato, Y., Hashimoto, A., and Misumi, R.: Predicting the morphology of ice particles in deep convection using the super-droplet method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2097, https://doi.org/10.5194/egusphere-egu2020-2097, 2020
