particle-based modeling of cloud microphysics
Particle-based modeling of cloud microphysics is becoming popular during the last 10 years. This workshop provides an opportunity to meet researchers in related fields, and exchange ideas to explore the possibilities of the methodology.
Date: Wed, November 22th, 2017. 13:00-17:00 (After party from 18:00)
Venue: University of Hyogo, Kobe Campus for Information Science, Computational Science Center Building, room 313 (兵庫県立大学神戸情報科学キャンパス 313セミナー室, http://www.simulation-studies.org/access)
|Modeling of cloud microphysics. Can we do better?,
Dr. Wojciech W. Grabowski (NCAR)
|Recent activities of climate study team in AICS,
Dr. Hirofumi Tomita (AICS)
|Subgrid-scale modeling for the super-droplet method,
Dr. Toshiki Matsushima (AICS)
|Stochastic coalescence in Lagrangian cloud microphysics,
Dr. Piotr Dziekan (University of Warsaw)
|Numerical study on marine stratocumulus and their turbulence structure using the super-droplet method,
Mr. Kazuya Takeda (University of Hyogo)
|Progress of the application of the super-droplet method to mixed-phase clouds,
Dr. Shin-ichiro Shima (University of Hyogo/AICS)
|After Party at Sannomiya area
Organizers: Shin-ichiro Shima (University of Hyogo), Toshiki Matsushima (AICS)
Sponsorship: Graduate School of Simulation Studies, University of Hyogo; RIKEN Advanced Institute for Computational Science
Support: JSPS KAKENHI Grant-in-Aid for Scientific Research(B): (Proposal number: 26286089), 名古屋大学宇宙地球環境研究所国際共同研究課題(290123)
Contact: Shin-ichiro Shima (島伸一郎), e-mail: email@example.com
Modeling of cloud microphysics. Can we do better?,
Dr. Wojciech W. Grabowski,
Mesoscale and Microscale Meteorology (MMM) Laboratory, National Center for Atmospheric Research (NCAR)
Traditional cloud modeling methodologies apply a continuous approach for all thermodynamic variables, not only for the temperature and water vapor, but also for cloud condensate and precipitation. Continuous in time and space Eulerian variables used to represent cloud and precipitation particles are mass and sometimes number mixing ratios in bulk schemes and mass and/or number spectral density mixing ratios in bin schemes. Such a methodology has been the workhorse of cloud-scale modeling from its early days. However, there are challenges in applying such approaches due to numerical diffusion in the physical space and in the particle mass (or size) space for bin schemes, difficulty in representing aerosol processing by clouds, and inability to properly represent unresolved spatial scales that arguably play a significant role in the development of the particle size/mass spectra. This presentation will discuss problems with the Eulerian methodology and introduce a particle-based Lagrangian approach that is gaining popularity in cloud-scale modeling. Application of this approach to the problem of droplet spectral broadening in warm shallow clouds will illustrate key advantages of the method. Prospects of applying the Lagrangian particle-based methodology to more complex simulations involving clouds will be discussed.
Recent activities of climate study team in AICS,
Dr. Hirofumi Tomita,
RIKEN Advanced Institute for Computational Science (AICS).
Subgrid-scale modeling for the super-droplet method,
Dr. Toshiki Matsushima,
RIKEN Advanced Institute for Computational Science (AICS).
Stochastic coalescence in Lagrangian cloud microphysics,
Dr. Piotr Dziekan,
Institute of Geophysics, Faculty of Physics, University of Warsaw.
Coalescence of hydrometeors is commonly modeled using the Smoluchowski equation. It is a mean-field equation that does not capture the stochastic nature of coalescence. More exact methods include the DNS and the master equation. The super-droplet method (SDM), which is a Lagrangian method for modeling cloud microphysics, is another alternative. It is shown that the SDM with multiplicities equal to 1 is in agreement with the master equation.
Next, we use SDM simulations to determine validity of more approximate methods: the Smoluchowski equation and the SDM with mulitplicities greater than 1. In the latter, we determine how many computational droplets are necessary to correctly model the expected number and the standard deviation of the autoconversion time.
Then, SDM is used to study stochastic effects relevant for rain formation: fluctuations in the autoconversion time and lucky droplets. Size of the coalescence cell is found to strongly affect system behavior. In small cells, correlations in droplet sizes and droplet depletion slow down rain formation. In large cells, collisions between rain drops are more frequent and this also can slow down rain formation. The increase in the rate of collision between rain drops may be an artefact caused by assuming a too large well-mixed volume. The highest ratio of rain water to cloud water is found in cells of intermediate sizes. Maximal size of a volume that is turbulently well-mixed with respect to coalescence is estimated at V = 1.5·10−2 cm3. The Smoluchowski equation is not valid in such small volumes. Implications for LES modeling are discussed.
Numerical study on marine stratocumulus and their turbulence structure using the super-droplet method,
Mr. Kazuya Takeda,
Graduate School of Simulation Studies, University of Hyogo.
We performed a series of numerical simulation of marine stratocumulus using the DYCOMS-II RF02 setup specified in Ackerman et al. (2011). For the LES model, we used CReSS (Tsuboki 2008). For the cloud microphysics model, we compared the difference between the super-droplet method (SDM) and the Kessler bulk scheme. We found that grid resolution of about (Δx,Δz)=(10m,10m) is necessary for SDM to sustain the cloud deck, though (Δx,Δz)=(50m,10m) is enough for the Kessler bulk.
In this talk, aiming to understand this difference, a detailed analysis on the turbulence structure in the boundary layer will be presented.
This is a collaboration work of K.T. and Shin-ichiro Shima (U Hyogo).
Progress of the application of the super-droplet method to mixed-phase clouds,
Dr. Shin-ichiro Shima,
Graduate School of Simulation Studies, University of Hyogo / RIKEN Advanced Institute for Computational Science (AICS).
The super-droplet method (SDM) is a particle-based and probabilistic numerical scheme, which enables the accurate simulation of cloud microphysics with less demand on computation. In the SDM, the time evolutions of aerosol/cloud/precipitation particles are calculated explicitly by solving the mesoscopic governing laws of cloud microphysics.
We recently applied the SDM to ice phase cloud microphysics to simulate mixed-phase clouds. The multicomponent bin model of Chen and Lamb (1994) was translated into the SDM framework. The latest advances in ice phase cloud microphyiscs were incorporated. Several new ideas were also introduced.
In this talk, the SDM for mixed-phase clouds will be briefly explained, and several preliminary results of the simulation of a cumulonimubus will be presented.
This is a collaboration work of S.S., Yousuke Sato (Nagoya U/AICS), and Akihiro Hashimoto (MRI).