3rd International Workshop on

Cloud Turbulence

March 3­5, 2020
Nagoya Institute of Technology, Nagoya, Japan

Program of 3rd International Workshop on Cloud Turbulence
March 3-5, 2020, Hall, Building 4, Nagoya Institute of Technology
URL: http://comphys.web.nitech.ac.jp/cloud3

March 3
9:00

9:30

Registration

9:30

9:40

Opening

9:40 10:20

Grabowski

10:20 10:40

Diﬀusional growth of cloud droplets in homogeneous isotropic turbulence:
DNS, scaled-up DNS, and stochastic modelCoﬀee Break

10:40 11:20

Chandrakar

Droplet size distributions in turbulent clouds:
experimental evaluation of theoretical distributions

11:20 12:00

Saito

Scale dependence of droplet spectra broadening by stochastic condensation
in a rising air parcel

12:00 14:00

Lunch

14:00 14:40

Rosa

Eﬀects of two-way momentum coupling on the collision statistics of cloud dropletsin turbulent ﬂows

14:40 15:20

Onishi

Direct Lagrangian size-resolving simulation of droplet growth in cloud turbulence

15:20 15:40

Coﬀee Break

15:40 16:20

Watanabe

New insights into turbulence modulation by small particles
obtained using point-particle and particle-resolved DNSs

16:20 17:00

Bhowmick

Numerical investigation of droplet micro-physical growth inside atmospheric clouds

Yeung

Extreme-scale computing for turbulence and turbulent mixing:
status and future needs

Kobayashi

Subgrid-scale models based on coherent structures

March 4
9:00

9:40

9:40 10:20
10:20 10:40

Coﬀee Break

10:40 11:20

Tsuji

Universal property of velocity statistics in log-region of high-Re-number
turbulent boundary layer

11:20 12:00

Malinowski

Turbulence kinetic energy dissipation rate in clouds: new estimates

12:00 12:10

Photo Session

12:10 14:00

Lunch

14:00 14:40

Schumacher

Dry and moist mesoscale convection and machine learning

14:40 15:20

Bec

Homogeneous turbophoresis of small heavy particles

15:20 15:40

Coﬀee Break

15:40 16:20

HirataOnishi

16:20 17:00

Bodenschatz The Advanced Max Planck CloudKite: high-resolution airborne measurementsin the atmosphere

18:30 20:30

Estimation of time-course core temperature and water lossin Human Models with Urban Micrometeorology Prediction

Banquet (Hotel Mielparque Nagoya)March 5
9:00

9:40

Shima

Predicting the morphology of ice particles in deep convection
using the super-droplet method

9:40

10:20

Tajiri

MRI adiabatic-expansion-type cloud chamber experiments:
measurements of atmospheric aerosols to act as CCN and INP

10:20 10:40

Coﬀee Break

10:40 11:20

Hoﬀmann

11:20 12:00

Gorokhovski Fragmentation in clouds and eﬀects of intermittency in the sheared turbulence on a
droplet acceleration; direct simulations and stochastic models on residual scales

12:00 14:00

Bridging the gap between LES and DNS: A new subgrid-scheme for LES
with Lagrangian Cloud Microphysics

Lunch

14:00 14:40

Ishihara

Vortex cluster structures and particle motions in high Reynolds number turbulence

14:40 15:20

SatoReseach of aerosol cloud interaction using the global scale simulation
with explicit representation of cloud microphysics

15:20 15:30

ClosingDiﬀusional growth of cloud droplets in homogeneous
isotropic turbulence: DNS, scaled-up DNS, andstochastic model
Wojciech W. Grabowski1 , Lois Thomas2 , and Bipin Kumar2
1

National Center for Atmospheric Research, Boulder, Colorado, USA
Indian Institute of Tropical Meteorology, Pune, Maharashtra, India

2

grabow@ucar.edu
Diﬀusional growth of cloud droplets in a turbulent environment is a poorly understoodaspect of cloud physics. In this study, we use the Direct Numerical Simulation (DNS)
to study the impact of large turbulent eddies on the growth of cloud droplets by thediﬀusion of water vapor. Large eddies generate higher supersaturation ﬂuctuations anddroplets grow or evaporate in response to these local ﬂuctuations. The traditional DNS
cannot simulate scales larger than ∼1 m due to the excessive computational cost thatcomes from resolving all scales involved and from following every single droplet. We
address this problem by using what we refer to as the ‘scaled-up DNS’. The scalingup is done in two parts, ﬁrst by increasing the size of the computational domain andappropriately increasing the Turbulent Kinetic Energy (TKE), and second by using superdroplets instead of real droplets. In the scaled-up DNS, the domain size is increased
keeping the number of grid points the same as in the real DNS. To ensure proper TKE
dissipation at small scales, the viscosity is also appropriately scaled-up with the grid
length. For the scaled-up domains, say, meters and tens of meters, the number of droplets
to be followed is in billions, which is computationally impossible. To address this issue we
use super-droplets, where each super-droplet represents a group of identical real droplets.
The number of real droplets represented by a super-droplet is given by the additional
attribute referred to as the multiplicity. We conducted scaled-up simulations for ﬁve
domains, the largest being 643 m3 volume using a DNS of 2563 grid points. All thesimulations are carried out with vanishing mean vertical velocity and thus with no mean
supersaturation, similarly to the past DNS studies. As expected, the supersaturation
ﬂuctuations as well as the spread in droplet size distribution increase with the domain
size, with the mean droplet radius variance increasing with time t as t1/2 as identiﬁed
in previous DNS studies. Scaled-up simulations were repeated with diﬀerent values ofthe super-droplet multiplicity to show numerical convergence of the scaled-up solutions.
Finally, we compare the scaled-up DNS results with a stochastic model that calculates thesupersaturation ﬂuctuations and droplet growth based on the vertical velocity ﬂuctuations
updated using the Langevin equation. Overall, the results document similar scaling asin previous small-domain DNS simulations and support the notion that the stochastic
subgrid-scale model is a valuable tool for the multi-scale large-eddy simulation of natural
clouds.

Note:
This study has been submitted to the journal "Atmospheric Chemistry and Physics"
and now in the discussion. The manuscript can be downloaded from the link below.
URL: https://www.atmos-chem-phys-discuss.net/acp-2020-159/

Droplet size distributions in turbulent clouds:
experimental evaluation of theoretical distributions
Kamal Kant Chandrakar1 , Izumi Saito2 , Fan Yang3 , Will Cantrell4 ,Toshiyuki Gotoh2 , and Raymond A. Shaw41

2

National Center for Atmospheric Research, Boulder, USA
Department of Physical Science and Engineering, Nagoya Institute of Technology, Japan
3
Brookhaven National Laboratory, Upton, New York USA
4
Department of Physics, Michigan Technological University, Houghton, USA
kkchandr@ucar.edu

Cloud droplet size distribution shape is a key aspect of parameterized cloud processes
in large scale weather and climate models, where explicit representation of the smallscale processes is diﬃcult, and in remote sensing retrievals that require input for the
inversion process. Typically, the size distribution shapes utilized have been obtained empirically from ﬁeld measurements. A theoretical droplet size-distributions grounded on
fundamental physical principles would be highly valuable for these applications. However,
it is essential to test these theoretical formulations using well characterized observations,which is challenging with ﬁeld measurements because of sampling challenges, variable conditions, and the simultaneous action of many diﬀerent physical processes. In this work
theoretical expressions for cloud droplet size distribution shape are evaluated using measurements in a convective-cloud chamber. The experiments are a unique opportunity toconstrain theory because they are in steady-state, because the initial and boundary conditions are well characterized, and because some complicating processes are eliminated.
Three distributions obtained from a Langevin drift-diﬀusion approach to cloud formation via stochastic condensation are tested: (i) stochastic condensation with a constantremoval timescale; (ii) stochastic condensation with a size-dependent removal timescale;(iii) droplet growth under ﬁxed supersaturation conditions and with removal due to thegravitational settling. In addition, a similar Weibull distribution that can be obtained
from the drift-diﬀusion approach, as well as from mechanism-independent probabilistic
arguments (e.g., maximum entropy), is tested as a fourth hypothesis. An extensive setof cloud droplet size distributions are measured under diﬀerent aerosol injection rates.
Five diﬀerent aerosol injection rates are run both for size-selected aerosol particles, andsix aerosol injection rates are run for broad-distribution, polydisperse aerosol particles.
In relative comparison, the most favorable comparison to the measurements is the expression for stochastic condensation with size-dependent droplet removal rate. However,even this optimal distribution breaks down for broad aerosol size distributions, primarily
due to deviations from the measured large-droplet tail. A possible explanation for thedeviation is the Ostwald ripening eﬀect coupled with deactivation/activation in polluted
cloud conditions or the unaccounted spatial variability in the droplet concentration. Bothmean supersaturation and ﬂuctuations play an important role in determining the parameters (e.g., scale parameter) of the droplet size distribution, with their relative importancedepending on the conditions of the experiment (e.g., cloud droplet number density).
References[1] K. K. Chandrakar, I. Saito, F. Yang, W. Cantrell, T. Gotoh, and R. A. Shaw.
Droplet size distributions in turbulent clouds: experimental evaluation of theoret-ical distributions. Quarterly Journal of the Royal Meteorological Society, 2019;1–22.
https://doi.org/10.1002/qj.3692

Scale dependence of droplet spectra broadening by
stochastic condensation in a rising air parcel
Izumi Saito, Toshiyuki Gotoh, Takeshi Watanabe & Tatsuya Yasuda
Department of Scientiﬁc and Engineering Simulation, Nagoya Institute of Technology,
Gokiso-cho, Showa-ku, Nagoya 466-8555, Japanizumi@gfd-dennou.org
Scale dependence is investigated for droplet spectra broadening by stochastic condensation in an air parcel rising adiabatically at constant speed. It is shown that the scaledependence can be interpreted as the dependence on the Damköhler number Da(= T /τc ),where T is the large-eddy turnover time and τc is the phase relaxation time. For Da ≪ 1,
which is the case for many previous direct numerical simulation (DNS) studies on cloud
turbulence, the supersaturation fluctuation is not so much aﬀected by cloud droplets, andthe variance of the squared radius of cloud droplets, σR2 2 , grows in proportional to the timet, just like the variance of the displacement of a Brownian particle. On the other hand,for Da ≫ 1, which applies to the atmospheric clouds and many of the large-eddy simulation (LES) studies, the supersaturation fluctuation is damped by cloud droplets, and its
amplitude is limited to that required from the so-called quasi-equilibrium condition. In
this case, σR2 2 grows approximately as t1/3 .
Large-scale DNSs are conducted for the growth of cloud droplets in an adiabatically rising
air parcel by using the DNS model “cloud microphysics simulator”, which has been developed in our group [1, 2]. In the air parcel, turbulent velocity field is kept at a statisticallysteady state by the external random Gaussian forcing, and the supersaturation fluctuation is excited by the forcing which is proportional to the vertical turbulent velocity field.
The simulation results with various parameters (including T and τc ) confirms σR2 2 ∝ t and∝ t1/3 for experiments with Da ≪ 1 and Da ≫ 1, respectively, and also demonstrates
the transition between the two limits. At least Da > 5 is needed for the assumption of
Da ≫ 1 to be valid. We also discuss possible implications for observations and cloud
turbulence parameterizations proposed in the previous studies.
References[1] T. Gotoh, T. Suehiro & I. Saito, Continuous growth of cloud droplets in cumulus
cloud, New J. Phys. 18, 043042 (2016).
[2] I. Saito & T. Gotoh, Turbulence and cloud droplets in cumulus clouds, New J. Phys.
20, 023001 (2018).

Eﬀects of two-way momentum couplingon the collision statistics of cloud dropletsin turbulent ﬂows.
Bogdan Rosa
Institute of Meteorology and Water Management - National Research Institute, 01-673
Warszawa, ul. Podlesna 61, Poland
bogdan.rosa@imgw.pl
Parametrization of cloud microphysical processes in NWP models is viewed as the major
source of uncertainty of simulated forecasts. To develop more realistic parameterizationsof these sub-grid scale cloud processes a wide range of eﬀects related to the interaction of
the water droplets with turbulent air needs to be explored. Most of the former numericalsimulations were based on Eulerian-Lagrangian approach, being typically simpliﬁed to
one-way coupling. In the present study [1] the dynamics of cloud droplets in isotropic,
homogeneous turbulence is investigated with taking into account eﬀects of two-way interphase momentum transfer, so-called two-way coupling. The continuous phase is solvedin the Eulerian approach employing DNS. The size of computational meshes varies from
1283 up to 10243 . The dispersed phase is treated using the Lagrangian approach alongwith the point-particle assumption.
It turns out that the two-way momentum coupling aﬀects more strongly the dynamics ofthe systems with the settling droplets. This is mainly reﬂected in increase of the radial
relative velocity at larger mass loadings. The eﬀect of two-way coupling on the RDF israther complex and depends on the particle inertia. For smaller droplets we observed
little enhancement of the RDF which is a consequence of formation of additional vorticalstructures by settling droplets. The RDF of large inertia droplets is signiﬁcantly reduced.
The relation between collision kernel and the droplet mass loading is presented in theﬁgure below. The increase of Γ in simulations with gravity is the eﬀect of larger RVV.

Fig. 1: Dynamic collision kernel as a function of droplet mass loading. Open markers, simulations without gravity; ﬁlled
markers, with gravity. Dashed lines - results from simpliﬁed simulations in which
one computational droplet represents several real droplets.
References[1] B. Rosa, J. Pozorski and L-P. Wang, Eﬀects of turbulence modulation and gravity on
particle collision statistics, Submitted to Int. J. Multiph. Flow (2020).

Direct Lagrangian size-resolving simulation of droplet growth incloud turbulence
R. Onishi1 and S. Takeuchi2
1

Center for Earth Information Science and Technology, Japan Agency for Marine-Earth Science andTechnology,
3173-25 Showa-machi Kanazawa-ku, Yokohama Kanagawa 236-0001, Japan.
2

Department of Mechanical Engineering, Osaka University,
2-1 Yamada-oka, Suita Osaka 565-0871, Japan
onishi.ryo@jamstec.go.jp

We have implemented the Immersed Solid Method (ISM) [1], one of the immersed boundary
methods for size-resolving simulations, in the Lagrangian Cloud Simulator (LCS) [2], which adopts
the so-called Euler-Lagrangian framework and can simulate droplet growth in air turbulence. This
implementation enables the LCS to deal with large particles whose particle Reynolds numbers (Re p)
exceed unity or whose sizes exceed the Kolmogorov scale. Several validation tests for dragcoefficient and collision efficiency have been completed. They have shown the robustness of the
LCS-ISM and its good computational efficiency. A high-performance computing with the LCS-ISM
has successfully obtained turbulent collision statistics of large spherical particles in homogeneous
isotropic turbulence. The LCS-ISM simulations, i.e., the size-resolving simulations, has quantified
the possible error induced if the point-particle assumption is adopted for large Rep inappropriately. It
can be also used to highlight several issues unsolvable if with the point-particle assumption.

Fig.1: Energy dissipation distributions in homogeneous isotropic turbulence with (a)Reλ=53 and (b)Reλ=210with containing 4,096 large particles.
References[1] R. Onishi, K. Matsuda and K. Takahashi. Lagrangian Tracking Simulation of Droplet Growth in
Turbulence–Turbulence Enhancement of Autoconversion Rate, J. Atmos. Sci., 72: 2591-2607, 2015.
[2] T. Kajishima, S. Takiguchi, H. Hamasaki and Y. Miyake. Turbulence structure of particle-laden flow in a
vertical plane channel due to vortex shedding, JSME International Journal, Ser.B, 44: 526-535, 2001.

New insights into turbulence modulation by small
particles obtained using point-particle andparticle-resolved DNSs
Takeshi Watanabe, Izumi Saito and Toshiyuki Gotoh
Department of Scientiﬁc and Engineering Simulation, Nagoya Institute of Technology,
Gokiso, Showa-ku, Nagoya 466-8555, Japanwatanabe@nitech.ac.jp
Turbulent flows observed in nature and engineering generally involve the transport ofsmall objects such as the grains of sand in rivers, volcanic ash in atmosphere, dropletsin cloud, and so on. To understand the behavior of particles in turbulence is one of theimportant issues in the fundamental study of turbulence, so that much eﬀorts have been
made to clarify the nature of particle dynamics in turbulence [1].
In this study we show the importance of time scale τf = (6πνnp r)−1 , where np and r are
respectively the particle number density and particles radius, to characterize the degreeof turbulence modulation. Our DNS results for inertial particles in isotropic turbulencein the two-way coupling regime show that the turbulent kinetic energy reduction can be
expressed by the single parameter Da, called Damköhler number Da = T /τf , T being thelarge-eddy turnover time [2].
Results obtained in [2] rely on the point-particle approximation with two-way coupling
calculation. Recent progress of computing power also allows us to conduct more accurate
DNS of particulate flow that the disturbed flow around each particles is accurately resolved
on grids. Question raised is how the two-way coupling method using point-particles accurately reproduce the turbulence modulation in experiments? Second issue of this study
is to examine the accuracy of turbulence modulation by point particles by conducting
the two kinds of DNSs of isotropic turbulence with small particles: point-particle DNS(PP-DNS) and particle-resolved DNS (PR-DNS)[3]. For PR-DNS, the flow disturbancearound each particles is accurately resolved by performing the high resolution DNSs withvolume penalization method. It is found that the kinetic energy spectra modified by the
point particles with larger mass loading almost collapse onto the spectral curves obtainedby the PR-DNSs under the same physical conditions. Although the diﬀerence among
PP- and PR-DNSs can be found in the scale below the particle diameter, we show thisis explained by corrected spectrum using the filter function that is originated from thenumerical diﬀusion included in the two-way coupling calculation of PP-DNS.
The authors thank Mr. Tomoki Kozawa for his contribution on numerical simulations.
The computational supports provided by JHPCN (jh180009,jh190018-NAH), the High
Performance Computing (HPC2018) at Nagoya University, the National Institute for Fusion Science of Japan (NIFS18KNXN366, NIFS18KNSS105) are also gratefully acknowledged. This work was supported by MEXT KAKENHI through Grant No. 15H02218
and JSPS KAKENHI Grants No.18K03925 and No.18K13611.
References[1] G. Falkovich, K. Gawedzki and M. Vergasola, Particles and ﬁelds in ﬂuid turbulence,
Rev. Mod. Phys. 73, 913 (2001).
[2] I. Saito, T. Watanabe and T. Gotoh, A new time scale for turbulence modulation by
particles, J. Fluid Mech. 880, R6 (2019).
[3] T. Watanabe, T. Kozawa, I. Saito and T. Gotoh (2020) in preparation.

Numerical Investigation of Droplet Micro-Physical
Growth inside Atmospheric Clouds
T. Bhowmick1,3 , M. Iovieno2 , Y. Wang3 , G. Bagheri3 and E. Bodenschatz3
12

Dept. of Applied Science and Technology, Politecnico di Torino, Torino, Italy
Dept. of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
3
Laboratory for Fluid Physics, Pattern Formation and Biocomplexity, Max Planck
Institute for Dynamics and Self-Organization, Göttingen, Germany
taraprasad.bhowmick@polito.it

Atmospheric clouds are well known for their impact on the evolution of global or local
climate, but science has not yet understood many of the physical processes, interactions
and the interplays happening inside and out of the clouds, spanning over a vast rangeof spatial and temporal scales [1, 2]. I present two complementary parts of my PhD
activity, focused to contribute to the understanding of the interplay between cloud droplet
micro-physical growth and various small-scale cloud turbulent processes, using numericalmethods.
In the ﬁrst part, I looked into the broadening of cloud droplet population inside smallscale turbulent mixing at the cloud and clear air interface [3]. Using pseudo-spectral direct
numerical simulation along with Lagrangian droplet equations, transient evolution of incloud turbulent kinetic energy, density of water vapour and temperature was simulated
to investigate its impact on size broadening of three initial mono-disperse cloud droplet
populations. Larger droplet populations (∼ 20 µm initial radii) showed signiﬁcant growthby droplet-droplet collision and a higher rate of gravitational sedimentation. Whereas, thesmaller droplets (6 µm initial radius) did not show any collision or signiﬁcant sedimentation, but a large size distribution broadening due to diﬀerential condensation/evaporationinduced by turbulent mixing.
The second part attemps to understand the role of large hydrometeors [∼ O(mm) size]on secondary droplet generation. Motivated by the experimental investigation of comparatively large sedimenting droplets which generated secondary cloud droplets in it’s wakein an atmospheric cloud like environment [4], we conduct incompressible direct numericalsimulations of such precipitating large hydrometeors using Lattice-Boltzmann method ina cloud like ambient. Fluid ﬂow and advected in-cloud temperature and water vapor
ﬁelds are simulated around a ﬁxed hydrometeor, and we investigate the development ofsupersaturation in its wake. In-cloud aerosol populations are simulated as tracer particles, which follow the ﬂuid ﬁeld exactly due to negligible Stokes number of the aerosols.
Depending on the hydrometeor Reynolds number, various ﬂow regimes such as, steadyaxisymmetric, steady oblique, transient oscillating, and chaotic wakes are observed. Depending on the ambient temperature and the relative humidity conditions, various extent
of supersaturated air parcels are formed inside the wake, which can activate aerosols.
References[1] Devenish B. J. et al., QJRMS. 138(667), 1401-1429, (2012).
[2] Grabowski W. W. and Wang L., Ann. Rev. F. Mec. 45(1), 293-324, (2013).
[3] Bhowmick T. and Iovieno M., Fluids 4(3), 144, (2019).
[4] Prabhakaran P. et al., PRL. 119, 128701, (2017).
This project has received funding from the Marie - Sklodowska Curie Actions (MSCA) under theEuropean Union’s Horizon 2020 research and innovation programme (grant agreement no. 675675)

Extreme-scale computing for turbulence andturbulent mixing: status and future needs
Yeung, P.K.1 , Ravikumar, K.1 and Sreenivasan, K.R.2
1

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332,
USA
2
Departments of Physics and Mechanical Engineering and the Courant Institute ofMathematical Sciences, New York University, New York, NY 11201, USA
pk.yeung@ae.gatech.edu

Turbulence in clouds and many other natural settings involve complex interactions among
nonlinear advective transport, molecular diﬀusion, buoyancy forces, phase change, vapor
droplet dynamics, and other factors. From a numerical point of view, the conduct of direct numerical simulations based on exact fundamental equations addressing all of thesecomplexities and can be helpful to modeling is clearly a daunting yet worthwhile goal, thatwould require computational resources at the so-called exascale, and beyond. However,in view of the importance of the small scales, one possible strategy may be to ﬁrst obtain
results of highest ﬁdelity possible for the fundamental problem of passive scalars in homogeneous isotropic turbulence [1], and then to introduce various additional complexities.
With this perspective, in this talk we will review recent results on ﬁne-scale intermittency
and extreme events [2] for the velocity ﬁeld at up to 18432 3 grid resolution, and up to92163 for scalar ﬁelds, obtained using a GPU-optimized pseudo-spectral code [3] on the
200 Petaﬂop/second Summit supercomputer at the Oak Ridge National Laboratory. We
will discuss brieﬂy numerical issues and future research needs for several extensions, suchas problems where molecular diﬀusivities involved are much larger or much smaller thanthe ﬂuid kinematic viscosity, where diﬀerential diﬀusion may be important, and where
the scalars are active and modify the turbulence, as would be expected in clouds. Finally,
we will also brieﬂy address a question about the usefulness of short simulations at thelargest feasible problems sizes allowed by state-of-the-art computational platforms.
References[1] T. Gotoh and P. K. Yeung, “Passive scalar transport in turbulence: A computational
perpsective,” in Ten Chapters in Turbulence (P. A. Davidson, Y. Kaneda, and K. R.
Sreenivasan, eds.), Cambridge University Press, 2013.
[2] P. K. Yeung, Sreenivasan. K. R., and S. B. Pope. Eﬀects of ﬁnite spatial and temporal resolution on extreme events in direct numerical simulations of incompressible
isotropic turbulence. Phys. Rev. Fluids 3:064603, 2018.
[3] K. Ravikumar, D. Appelhans, and P. K. Yeung. GPU acceleration of extreme
scale pseudo-spectral simulations of turbulence using asynchronism. In Proceedings of The International Conference for High Performance Computing, Networking and Storage Analysis (SC’19), Denver, CO, USA. ACM, New York, NY, USA,https://doi.org/10.1145/3295500.3356209, 2019.

Subgrid-scale models based on coherent structures
Hiromichi Kobayashi1
1Department of Physics, Hiyoshi Campus, Keio University, 4-1-1 Hiyoshi, Kohoku-ku,
Yokohama 223-8521, Japanhkobayas@keio.jp

Large-eddy simulation (LES) is a powerful tool to solve the unsteady and complicatedturbulent phenomena, such as a ﬂow in a gas turbine, Rayleigh-Bénard convection, andcloud turbulence. In LES, resolved scale or grid-scale (GS) eddies are directly computed
and unresolved scale or subgrid-scale (SGS) eddies are modeled through the SGS stresstensor τij = ui uj − ui uj . The modeled SGS stress tensor is termed as the SGS model.
So far, a lot of SGS models are proposed and are divided into two groups. One is thegroup including the eddy viscosity model such as the Smagorinsky model. The model is
proportional to velocity strain tensor and is an isotropic model. However, it gives theenergy transfer only from GS to SGS, namely, forward scatter. The other is the similarity
model derived from a scale-similarity concept u′i u′j = (ui −ui )(uj −uj ). The latter model isanisotropic and gives forward scatter and backscatter (from SGS to GS). The backscatteryields a negative viscosity and it often causes the numerical instability.
A model parameter in the eddy viscosity model was modeled using the Q value that
corresponds to the second invariant of velocity gradient tensor [1]. The positive Q value
exhibits eddy structures. The energy transfer from GS to SGS takes place around thecoherent structures. The SGS model based on the coherent structures is simple and stable.
The model parameter becomes automatically zero in laminar ﬂows owing to annihilationof eddies, so that the laminar or turbulent transition can be reproduced. Moreover, nowall-damping function is needed. Consequently, the model has been applied to a lot of
ﬂows around complicated geometries such as a ﬂow around/over many buildings.
The above model is useful, but relatively ﬁne resolution is needed enough to resolve nearwall eddy structures. Recently, a new concept to stabilize the backscatter is proposed
for the similarity model [2]. The eddy viscosity model only dissipates the eddies, but thesimilarity model can strengthen the eddies. The anisotropic scale-similarity model shows
good results even in very coarse grid-resolution. Some models show good performancewith free empirical model parameter [3, 4]. In this talk, I introduce the new concept andthe prospect to use it for the passive scalar model with coherent structures.
References[1] H. Kobayashi, The subgrid-scale models based on coherent structures for rotatinghomogeneous turbulence and turbulent channel ﬂow, Phys. Fluids 17, 045104 (2005).
[2] K. Abe, An improved anisotropy-resolving subgrid-scale model with the aid of a scale
similarity modelling concept, Int. J. Heat Fluid Flow 39, 42 (2013).
[3] H. Kobayashi, Improvement of SGS model using scale-similarity model based on analysis of SGS force and SGS energy transfer, Int. J. Heat Fluid Flow 72, 329 (2018).
[4] M. Klein, S. Ketterl, L. Engelmann, A. Kempf and H. Kobayashi, Regularised, parameter free scale similarity type models for Large Eddy Simulation, Int. J. Heat andFluid Flow 81, 108496 (2020)

Universal property of velocity statistics in log-regionof high-Re-number turbulent boundary layer
Yoshiyuki Tsuji1 , Atsushi Ido2 , and Michio Nishioka3
1

Nagoya University, Chikusa-ku Furo-cho, Nagoya city, Aichi 464-8603, Japan
2
RTRI wind tunnel center, Umegahara Maibara-shi, Shiga 521-0013, Japan
3
Osaka Prefecture University, Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japany-tsuji@energy.nagoya-u.ac.jp
High Reynolds number turbulent boundary layer measurement was performed in the Railway Technical Reserach Institute’s Large-scale low-noise wind tunnel in Maibara Japan.
The momentum thickness Reynolds number is up to approximately 8 × 104 . We focus on
the log-law proﬁle in both mean velocity near the wall and turbulence intensity in outerregion. The logarithmic relation of streamwise mean velocity as the result of the overlap
between the inner small scales and outer large scales is
U+ =1
ln y + + B,κ(1)

where U is the mean velocity, y is the distance from the wall, κ is called the von Kármánconstant, B is an additive constant, and superscript + denotes the nondimensional quantity normalized by friction velocity and kinematic viscosity. With regards to streamwise
Reynolds stress, Townsend [1] and Perry et al. suggested a suﬃciently high-Reynolds number presupposition that the logarithmic variation in streamwise (and spanwise) Reynolds
stress has a log-law relation as eq. (1), which is��u+ u+ = B1 − A1 ln(y/δ)(2)

where u is the streamwise velocity ﬂuctuation, δ is the boundary-layer thickness , A1 is the
Townsend-Perry constant, B1 is an additive constant, and bracket denotes the averaged
value. This logarithmic variation is derived by the attached-eddy hypotheses and thekx−1 law for the energy spectrum of the streamwise turbulent velocity, where kx is thestreamwise wavenumber. In this presentation, we study once again the relations Eq.(1)and Eq.(2) based on the high-Re number measurements. The probability density function(pdf) of a streamwise velocity component is also studied. From analyzing the data up toRθ ∼ 80000, it is found that pdfs have self-similar proﬁles in the log-law region of mean
velocity. Pdf proﬁles asymptote to the universal shape very close to the Gaussian, but are
positively skewed at the core region, indicating smaller values in the tail parts. In the loglaw region of turbulence intensity, pdf is positively skewed slightly. These characteristics
are summarized depending on the Reynolds number.
1 A. A. Townsend The Structure of Turbulent Shear Flow (Cambridge University
Press, Cambridge) 1980

Turbulence Kinetic Energy Dissipation Rate inclouds: new estimates
Szymon P. Malinowski 1 and Marta Wac�lawczyk1
1

Faculty of Physics, University of Warsaw, Pasteura 5, 02-073 Warsaw, Poland
malina@fuw.edu.pl

In this work we discuss various approaches to estimate the turbulence kinetic energy(TKE) dissipation rate from one-dimensional low resolution time series e.g. records
from airborne measurements of wind velocity ﬂuctuations. We focus on recently proposed
methods of the TKE dissipation-rate retrievals based on zero crossings, recovering themissing part of the spectrum and variance of velocity derivative. These methods are tested
on synthetic turbulence signals, fully resolved turbulence ﬁelds from direct numericalsimulations (DNS) and real data from airborne measurements in clouds. Results arecompared to standard retrievals using power spectra and structure functions. In-depth
error analysis is performed. Errors due to ﬁnite averaging window, cut-oﬀ frequencies
and diﬀerent ﬁtting ranges are discussed. Anisotropy of turbulence due to buoyancy isshown to inﬂuence retrievals based on the vertical velocity component. We demonstratealso, that diﬀerences between results of the methods can indicate deviations from theKolmogorov’s theory or the presence of external intermittency, that is the existence ofalternating laminar/turbulent ﬂow patches. Interstingly, the new estimates may be used
to determine the level of external intermittency in the recorded signal, which might be ofparticular interest in cloud studies. Finally we will show preliminary results of the metaanalysis of turbulence in marine convective clouds. We will show diﬀerences of turbulence
proprties in cumulus and stratocumulus clouds.
References[1] M. Waclawczyk, J. Kopec and S.P. Malinowski, Novel approaches to estimating the
turbulent kinetic energy dissipation rate from low- and moderate-resolution velocity
ﬂuctuation time series, Atmos. Meas. Tech. 10 , 4573 (2017).
[2] E.O. Akinlabi, M. Wac�lawczyk, J.P. Mellado and S.P. Malinowski, Estimating Turbulence Kinetic Energy Dissipation Rates in the Numerically Simulated Stratocumulus
Cloud-Top Mixing Layer: Evaluation of Diﬀerent Methods, J. Atmos. Sci., 76, 1471
(2019).
[3] M. Waclawczyk, A.S. Gozingan, J. Nzotungishaka, M. Mohammadi and S.P. Malinowski, Comparison of diﬀerent techniques to calculate properties of atmosphericturbulence from low-resolution data, Atmosphere, acceptted, (2020).

Dry and moist mesoscale convection and machinelearning
Jörg Schumacher1
1Institute of Thermodynamics and Fluid Mechanics, Technische Universität Ilmenau,
D-98684 Ilmenau, Germany
joerg.schumacher@tu-ilmenau.deConvection processes with and without phase changes are ubiquitous in geo- and astrophysical ﬂuid dynamics. Important examples comprise atmospheric boundary layers,
shallow cloud formation, or solar convection. In the present work, we focus on a numerical investigation of these processes by direct numerical simulations without small-scaleparametrizations. As a paradigm for the applications, we take dry and moist RayleighBénard convection in horizontally extended domains. In this setting, the formation of
turbulent superstructures can be observed – a regular patterning of highly chaotic turbulence on its largest scales that typically exceed the layer height. We present models
of dry and moist Boussinesq convection and some ﬁrst simulation results. A comprehensive study of these ﬂows requires new data reduction techniques. We discuss therefore
the application of supervised machine learning algorithms to analyse and even to predict
convective turbulence. In detail, we take one speciﬁc and eﬃcient implementation of recurrent neural networks (RNN), the reservoir computing model. Our work demonstrates
that this RNN model is capable to model the large-scale structure and low-order statistics
of turbulent convection.

Homogeneous turbophoresis of small heavy particlesJérémie Bec and Robin Vallée
MINES ParisTech, PSL Research University, CNRS, CEMEF, Sophia-Antipolis, France
jeremie.bec@mines-paristech.fr
Cloud droplets have inertia and thus detach from the surrounding turbulent airﬂow to form
uneven distributions and to cluster. Such particles have the tendency to drift out fromexcited turbulent regions since faster particles ﬂy longer distances before equilibration.
This fundamental phenomenon, called turbophoresis, has essentially been observed ininhomogeneous turbulent ﬂow where particles concentrate in regions of low kinetic energy(see, e.g., [1]). We show here that this eﬀect is also present in homogeneous turbulent
ﬂows and we illustrate and quantify the importance of instantaneous inhomogeneities on
the inertial-range spatial distribution of particles.
Phenomenological arguments show that particles dynamics can be modeled as an advectiondiﬀusion process where the density ρ� (x, t) coarse-grained over a scale � solves
�
�
�
τp
2
∂t ρ� + u · ∇ρp = ∇ κ� ρp , with κ� (x, t) =∇2 p(x� , t) d3 x� ,(1)
3� |x� −x|<�
u and p being the ﬂuid velocity and pressure ﬁelds, respectively. The diﬀusion coeﬃcient
appear here inside the Laplacian as a consequence of the ejection process at the basis ofparticles detachment from the ﬂuid ﬂow.

This approach is tested against the results of 3D direct numerical simulations in which
Lagrangian, heavy, point-like particles are suspended in a incompressible, homogeneous,isotropic, statistically stationary turbulent ﬂow at Rλ ≈ 460. We ﬁnd that the diﬀusive
approximation (1) gives very good handle on particle dispersion and concentration atinertial scales. In particular, it predicts that the particle distribution uniquely depends
on a scale-dependent Peclet number P e(�) = � u� /κ� whose scale dependence relates tothe anomalous scaling properties of the pressure ﬁeld.
Finally, our approach explains the apparition of voids in the particle distribution with sizes
spanning the full inertial range, as observed in 2D [2] or in-situ cloud measurements [3].
Such voids are tracked numerically using the Delaunay triangulation of the set of particles.
As a consequence of turbophoretic eﬀects, their volumes V are shown to follow a non trivial
probability distribution with a power law p(V) ∝ V −α . The exponent α is again related
to the ﬂuid velocity scaling properties.
References[1] S. Belan, I. Fouxon and G. Falkovich, Localization-delocalization transitions in turbophoresis of inertial particles, Phys. Rev. Lett. 112, 234502 (2014).
[2] G. Boﬀetta, F. De Lillo and A. Gamba, Large scale inhomogeneity of inertial particles
in turbulent ﬂows, Phys. Fluids 16, L20 (2004).
[3] K. Karpinska et al. Turbulence-induced cloud voids: observation and interpretation,
Atmos. Chem. Phys. 19, 4991 (2019).

Estimation of Time-Course Core Temperature and Water Loss in
Human Models with Urban Micrometeorology Prediction
Akimasa Hirata1, Ryo Onishi2, Toshiki Kamiya1, and Sachiko Kodera1
1

Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology,
Gokiso, Showa-ku, Nagoya 466-8555, Japan
2
Center for Earth Information Science and Technology, Japan Agency for Marine-Earth
Science and Technology, Yokohama, Japanahirata@nitech.ac.jp

Ambient condition may change rapidly and notably over time in urban areas. Conventional
indexes for ambient conditions, such as heat index and wet bulb globe temperature (WBGT),
are useful only in stationary conditions. To estimate the risks of heat-related illness (heat stroke)or to manage the risk, human thermophysiological responses should be followed for ambientcondition in the time domain. Here, we develop an integrated computational method for
estimating the time course of core temperature and water loss by combining micrometeorology
and human thermal response. We firstly utilize an urban micrometeorology prediction to
reproduce the environment surrounding walkers. The temperature elevations and sweating in astandard adult and child are then estimated for meteorological conditions. With thecomputational method, we estimate the body temperature and thermophysiological responses
for an adult and child walking along a street with two routes (sunny and shaded) in Tokyo on 7
August 2015. The difference in the core temperature elevation in the adult between the tworoutes was 0.11 °C, suggesting the necessity for a micrometeorology simulation. The
differences in the computed body core temperatures and water loss of the adult and child were
notable, and were mainly characterized by the surface area-to-mass ratio. The computational
techniques will be useful for the selection of actions to manage the risk of heat-related illness
and for thermal comfort.

(a)

(b)

Figure 1. (a)Three­dimensional distribution of instantaneous air temperature for 14:30 JST on 7 August
2015 and (b) resultant distribution of surface temperature of adult and child for shade and sunny side.

References[1] T. Kamiya, R. Onishi, S. Kodera, and A. Hirata, “Estimation of Time-Course Core
Temperature and Water Loss in Realistic Adult and Child Models with Urban
Micrometeorology Prediction,” Int’l J. Env. Res. & Pub. Health, vol. 16, no. 24, 5097, 2019.

XXV ICTAM, 23-28 August 2020, Milano, Italy

The Advanced Max Planck CloudKite: High-Resolution Airborne Measurements in the
Atmosphere
Gholamhossein Bagheri∗, Freja Nordseik, Philipp Höhne, Oliver Schlenczek, Marcel Schröder, JohannesGüttler, and Eberhard Bodenschatz
Max Planck Institute for Dynamics and Self-Organization (MPI-DS), Göttingen, Germany
Summary The insufﬁcient understanding of the clouds remains a main source of uncertainty in weather and climate models. To makeprogress it is vital to quantify small and large scale atmospheric conditions and ﬂows, and to connect them with a detailed investigationof the cloud microphysics. This task was the main objective of EUREC4 A ﬁeld campaign, which took place in the Atlantic Ocean andin the vicinity of the Barbados Cloud Observatory (BCO) in January-February 2020. One of the major in-situ atmospheric platforms
for EUREC4 A was the Max Planck CloudKite (MPCK), which is a hybrid balloon-kite aerostat equipped with a host of state-of-theart instruments to measure three-dimensional wind velocity, cloud droplet size/spatial distributions, and atmospheric state parameters.
Here we present the MPCK+ and the (to-be-obtained) preliminary results collected by MPCK+ during the EUREC4 A.

INTRODUCTION
Clouds are composed of water drops and/or ice crystals suspended in a highly turbulent ﬂow. The microphysics ofcloud particles is governed by nucleation and growth, condensation, evaporation and sublimation, combined with collisionand sedimentation. The complex interactions coupling large scale mechanisms O(100 m) to those of the small scales
O(100 nm) makes clouds very challenging to fully understand. As clouds impact hugely the energy budget of the Earth,
the role of clouds remains one of the big conundrums of weather and climate science [2, 1]. As an example, it is notunderstood in a quantitative way how rain develops in warm clouds and how it links to environment in which the clouds
form, including the chemical environment [3].

Figure 1: Schematic visualization of the MPCK+ operation on the R/V MARIA S MERIAN during EUREC4 A. Inset of the top ﬁgureshows Finite-Element-Method analyses of the guiding frame installed on the a-beam at the back of the vessel in order to make suremain line doesn’t hit any of the ship’s structures while balloon is ﬂying.

The Advanced Max Planck CloudKite (MPCK+)The MPCK+, as a hybrid aerostat-kite platform constitutes a new category of aerial platforms that can carry relatively
heavy payloads (∼50 kg up to ∼2 km MSL) while being operated in a TAS range of 0 m s−1 to 30 m s−1 . The low TAS
operational capacity of the MPCK+ combined with the possibility of launching it from a mobile vessel means that it canfollow the cloud in a semi-Lagrangian mode and capture the ﬁne details of cloud evolution in time with exceptionallyhigh spatial and temporal resolution. The MPCK+ is capable of lifting a net payload of 75kg when operating at an altitudeof 1km above the sea level. The instrument box onboard the MPCK+ is designed for simultaneous acquisition of cloudmicrophysics and cloud/atmospheric turbulence, which includes instruments to detect cloud particle size, shape, and∗Corresponding author. E-mail: gbagher@gwdg.de.

XXV ICTAM, 23-28 August 2020, Milano, Italy

spatial distributions and the atmospheric state (turbulence, temperature, and humidity). The battery powered instrument
box is remotely controlled from a ground station. With a fully charged battery, it can collect about 1 − 2 hour of data withimaging instruments and ∼ 8 hours with non-imaging instruments.
The MPCK+ instrument box is carefully designed to maximize the overlap between probing volumes of various
instruments, see Fig. 2. This makes it possible to resolve the coupling between cloud microphysics and turbulence.
Furthermore, the low wind speed typically encountered by tethered aerostat combined with the high frame rate of ourimaging instruments allows us to capture much smaller inter-frame-distances than those obtained on an aircraft. This
allows us to track cloud evolution in time and space with an unmatched level of detail. Cloudkite measurements can shedlight on the impact of turbulence on the spatial distribution of drops (e.g. existence of clustering, local drop concentrations)and the way they interact with turbulence, which are central to understanding of rain initiation problem, cloud evolution
and radiative characteristics of clouds. Main scientiﬁc instruments onboard the MPCK+ are:
• Particle Image/Tracking Velocimetry (PIV/PTV): sampling frequency of 15Hz and probing volume of 23cm ×15cm × 0.3cm, scans 1.6 l s−1 . Output data is 2D particle velocity and spatial distribution.
• In-line Holography: sampling speed of 75fps and probing volume of 1.5cm × 1.5cm × 10cm for drops >6 µm,
scans 1.7 l s−1 . Output data is particle size and 3D spatial distribution.
• Fast Cloud Droplet Probe (FCDP): measures cloud droplet distributions and concentrations in the range of 2–
50µm.
• Laser Doppler Velocimetry: 1D particle velocity measurement <20 kHz.
• Hot-/cold-wires: velocity and temperature measurements at 8 kHz.
• Standard Pitot tube: 100 Hz wind speed measurements.
• Multi-hole Pitot tube: 100 Hz Wind velocity measurements.
• Static pressure, relative humidity and temperature measurement sensors.
Installed PIV and holography systems provide spatially continuous measurements at wind velocities <3 m s−1 and <1 m s−1 ,respectively.

Figure 2: The MPCK+ instrument box 3D-CAD model. Inset shows a photo of the box without the front cover.

References[1] Stevens B., and Boney S. What Are Climate Models Missing? Science 340: 1053-1054,2013.
[2] Bodenschatz E. Can we understand clouds without turbulence? Science 327: 970-971, 2010.
[3] Pumir A., and Wilkinson M. Collisional Aggregation Due to Turbulence. Annu. Rev. Condens. Matter Phys. 7: 141-170,2016.
[4] Bony S., Stevens B., Ament F., Bigorre S., Chazette P., Crewell S., Delano J., Emanuel K., Farrell D., Flamant C., Gross S., Hirsch L., KarstensenJ., Mayer B., Nuijens L., Ruppert J.H., Sandu I., Siebesma P., Speich S., Szczap F., Totems J., Vogel R., Wendisch M., Wirth M. EUREC4A: A
Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation. Surv. Geophys. 38: 15291568, 2017.

Predicting the morphology of ice particles in deep
convection using the super-droplet method
Shin-ichiro Shima12 , Yousuke Sato23 , Akihiro Hashimoto4 , and Ryohei
Misumi5
1

Graduate School of Simulation Studies, University of Hyogo, Kobe, Japan
2
RIKEN Center for Computational Science, Kobe, Japan3
Department of Earth and Planetary Sciences, Faculty of Science, Hokkaido University,
Sapporo, Japan
4
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
5
National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japans shima@sim.u-hyogo.ac.jp
In this presentation, we summarize the main results of Shima et al. (2019) [1]. The superdroplet 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 ormass-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 velocitydimension 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-momentbulk model. Although the model still has room for improvement, these results strongly
support the eﬃcacy of the particlebased modeling methodology to simulate mixed-phase
clouds.
15

t = 3000 sz [km]

10−7

mixing ratio [log10 (kg kg−1)]
−6
−5
−4
−3

−2
cloud
rain
cloud icegraupel
snow

5

0
0

10

20

30
x [km]

40

50

60

Fig. 1: Two-dimensional simulation of a single deep convective cloud using the new
SDM model for mixed-phase clouds. Adapted from Shima et al. (2019) [1].
References[1] S. Shima, Y. Sato, A. Hashimoto, and R. Misumi, Geosci. Model Dev. Discuss.,
https://doi.org/10.5194/gmd-2019-294, 1-83, (2019).

MRI adiabatic-expansion-type cloud chamber experiments:
measurements of atmospheric aerosols to act as CCN and INP
Takuya Tajiri1, Narihiro Orikasa1, Yuji Zaizen1, Wei-Chen Kuo1, andMasataka Murakami12
1

Meteorological research Institute, Japan meteorological Agency,
Nagamine 1-1, Tsukuba, 305-0052, Japan
2
Institute for Space-Earth Environmental Research, Nagoya University,
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japanttajiri@mri-jma.go.jp
Cloud simulation chamber was built at the Meteorological Research Institute (MRI) to
investigate the details of fundamental processes of cloud formation [1]. It was designed as anadiabatic-expansion-type cloud chamber covering temperatures from 30 to −100°C, pressuresfrom 1030 to 30 hPa, and an evacuation rates corresponding to ascent rates from 0 to 30 m s −1.
For a better understanding of the aerosol effects on clouds, it is crucially important to reveal
the relationship between the physico-chemical properties of aerosol, such as dry particle size
distribution, cloud condensation nuclei (CCN) and ice nucleating particles (INP) abilities, andthe microphysical properties of cloud, such as initial droplet size distribution and partitioning
water substances into supercooled droplets and ice crystals. Aerosol particles determine aspects
of inside cloud and affect precipitation forecast through precipitation efficiency.
MRI’s cloud chamber was used previously to investigate the ability of surrogates ofatmospheric mineral dust [1, 2], biological material [3] and metal oxides to act as INPs [4] andto investigate the abilities of hygroscopic and glaciogenic seeding materials to act as CCN and
INPs, along with their effects on the initial microphysical structures of clouds. We also havebeen conducted the ground based monitoring of atmospheric aerosols using CCN counter, IN
counter and aerosol instruments for several years at the MRI in Tsukuba. Despite the presenceof various types of aerosol particles in terms of their concentrations, size distributions, andexternally/internally mixed with other materials, INP number concentrations were usually very
low. In this talk, the possibility of atmospheric aerosol particles to act as efficient CCN and
INPs and their role in natural cloud formation processes will be discussed.
References[1] T. Tajiri, K. Yamashita, M. Murakami, A. Saito, K. Kusunoki, N. Orikasa and L. Lilie, A
novel adiabatic-expansion-type cloud simulation chamber, J. Meteor. Soc. Japan, 91, 687–
704 (2013).
[2] N. Hiranuma, et al., A comprehensive laboratory study on the immersion freezing behaviorof illite NX particles: A comparison of 17 ice nucleation measurement techniques, Atmos.
Chem. Phys., 15, 2489–2518 (2015).
[3] N. Hiranuma, O. Möhler, K. Yamashita, T. Tajiri, A. Saito, A. Kiselev, N. Homann, C.
Hoose, E. Jantsch, T. Koop, and M. Murakami, Ice nucleation by cellulose and its
potential contribution to ice formation in clouds, Nat. Geosci., 8, 273–277 (2015).
[4] T. H. Kuo, M. Murakami, T. Tajiri and N. Orikasa, Cloud Condensation Nuclei and
Immersion Freezing Abilities of Al2O3 and Fe2O3 Particles Measured with theMeteorological Research Institute’s Cloud Simulation Chamber, J. Meteor. Soc. Japan,
97, 597–614 (2019).

Bridging the Gap between LES and DNS:
A New Subgrid-Scheme for LES
with Lagrangian Cloud Microphysics
Fabian Hoﬀmann1,2
1

2

CIRES, University of Colorado, Boulder, Colorado, USA
NOAA/Earth System Research Laboratory, Boulder, Colorado, USAfabian.hoﬀmann@noaa.gov

Cloud physics is a multi-scale problem. Relevant processes range from the organization ofcloud ﬁelds (tens of kilometers) to the entrainment-mixing process (hundreds of meters tocentimeters) and the size of individual hydrometeors (less than a millimeter). Representing these scales in a single model and studying their interactions is inherently diﬃcult.
In this talk, I will introduce a new subgrid-scheme for large-eddy simulation (LES) models
coupled with Lagrangian cloud microphysics (LCM). The subgrid-scale model is based on
the linear-eddy model (LEM), an economical, one-dimensional representation of turbulent
compression and folding [1]. The LEM enables the successful representation of inhomogeneous and ﬁnite rate mixing processes with results comparable to direct numericalsimulation (DNS) [2].
In the ﬁrst part of the talk, basic concepts and the coupling of LES, LEM, and LCM will
be introduced. Subsequently, I will present applications of this new approach, covering
warm-phase shallow cumuli and stratocumuli, as well as ﬁrst applications for mixed-phase
clouds [3, 4]. In agreement with theory, clouds susceptible to inhomogeneous mixing
show a reduced droplet number concentration and stronger droplet growth. Stratocumulus entrainment rates tend to be lower using the new approach compared to simulations
without it, indicating a more appropriate representation of the entrainment-mixing process. Finally, the Wegner-Bergeron-Findeisen-Process, leading to a rapid ice formation in
mixed-phase clouds, is also decelerated.
All in all, this new modeling framework bridges the gap between LES and DNS, i.e., it
represents all cloud-physically relevant scales and their interactions in a single model,
granting new insights that have been impossible before.
References[1] A. Kerstein, A linear-eddy model of turbulent scalar transport and mixing, Combust.
Sci. Technol., 60, 391 – 421 (1988).
[2] S. Krueger, Representing the eﬀects of entrainment and mixing on warm-cloud microphysics in large-eddy simulations, 2016 Fall Meeting, San Francisco, CA, Amer.
Geophys. Union, Abstract A33I-0374.
[3] F. Hoﬀmann, T. Yamaguchi, and G. Feingold, Inhomogeneous mixing in Lagrangian
cloud models: Eﬀects on the production of precipitation embryos, J. Atmos. Sci., 76,
113 – 133 (2019).
[4] F. Hoﬀmann, and G. Feingold, Entrainment and mixing in stratocumulus: Eﬀects
of a new explicit subgrid-scale scheme for large-eddy simulations with particle-based
microphysics, J. Atmos. Sci., 76, 1955 – 1973 (2019).

Fragmentation in clouds and effects of intermittency in the sheared turbulence on a droplet
acceleration; direct simulations and stochastic models on residual scales.
Mikhael Gorokhovski
Laboratoire de Mécanique des Fluides et d’Acoustique, École Centrale de Lyon, CNRSUniversité Claude Bernard Lyon 1-INSA Lyon, Ecully, France
Measurements in [1] showed that in the planetary boundary layer, which is occupied by marine
stratocumulus clouds, the Reynolds number is high ( Re  103 104 ) and the effects ofintermittency of turbulent dynamics on small-scales may be significant. The intermittency is
manifested by strong jumps in the velocity record, associated with thin (of order ofKolmogorov’s scale) and intense (of order of rms-velociy) vortical structures. A cloud droplet
may vigorously respond to those structures. This may result in strong preferential sampling of
the flow (tendency to droplets clustering), in the long-range correlations of the intensity of thedroplet acceleration and in the heavy tails in its acceleration statistics. Our question is how acloud droplet may respond to such intense flow structures in the case when clouds are subjectedto the shear flow, and how to model the intermittency effects in this case if the turbulence isunder-resolved? The answer on this question represents the first part of our discussion. To thisend, we performed DNS of homogeneously sheared turbulence loaded by heavy pointwise
particles at the moderate Stokes number, which can be thought of as droplets, and we focusedon the particle acceleration statistics. Apart from our previous findings for the acceleration ofheavy particles in the isotropic box turbulence (such as the significant separation in times instatistics of the direction and the norm of the acceleration, with the lognormality of the latter),
we observed that the regeneration cycle, induced by the mean shear, may lead to pseudo-cyclicbursts of averaged acceleration norm, and also to the preferential alignment of the particle
acceleration vector with the direction of long-lived vortical structures stretched along thepositive (extensive) axes of the strain. The latter effect is in contrast to the Maxey mechanismof preferential sampling of the fluid velocity gradient field [2]. It is interesting that the observedalignment is stronger if the Stokes number is increased. Such a large range of scales interaction
may be relevant to the cloud large-scale properties, such as precipitation formation. On the other
hand, when the shear turbulence is under-resolved, we need to simulate the effect ofintermittency on residual scales. Two possible approaches are these: (i) to simulate theintermittency effects by stochastic process along the particle trajectory with the local parameters
taken from the resolved velocity field [3]; or (ii) to develop such a SGS model that the LES
velocity field is no more invariant to the local Reynolds number (since this number is theparameter of intermittency structures). In [4], we proposed such approach referred to as LESSSAM (stochastic subgrid acceleration model), and we will illustrate the efficiency of thisapproach in prediction of statistical effects observed in DNS of homogeneous shear with heavy
particles.
The second part of the talk is also relevant to marine stratocumulus clouds. We will discuss on
the possible application of fragmentation [5] with variable frequency [6] to predict statisticallythe cloud breakup [7]. In [6], the kinetic evolution equation of fragmentation is represented in
the continuity form (renormalization of the fragmentation equation), and the new self-similarsolutions (or intermediate asymptotics) have been obtained in the case when the frequency offragmentation is a power function of the size of parent fragments. If to assume the physicalparameters of the cloud breakup, such as gravitational potential energy, thickness of the cloud
and the turbulent energy decay, one may express those self-similar solutions by elementaryfunctions. Surprisingly, these solutions show that the evolution of the cloud fragments
distribution goes with time to the well-known universal Nukiyama–Tanasawa distributionfunction. It would be interesting now to compare this result with LES simulations.
1. H Siebert, RA Shaw, Z Warhaft, 2010: Statistics of Small-Scale Velocity Fluctuations
and Internal Intermittency in Marine Stratocumulus Clouds, J. Atmos. Sci., 67, 262273
2. M. R. Maxey, 1987: The gravitational settling of aerosol particles in homogeneous
turbulence and random flow fields. J. Fluid Mech. 174:441465
3. M. Gorokhovski and R. Zamansky, 2018: Modeling the effects of small turbulent
scales on the drag force for particles below and above the Kolmogorov scale, Phys.
Rev. Fluids 3, 034602
4. V. Sabelnikov, A. Barge and M. Gorokhovski, 2019: Stochastic modeling of fluid
acceleration on residual scales and dynamics of suspended inertial particles inturbulence, Phys. Rev. Fluids 4, 044301
5. Gorokhovski M.A. and Saveliev V.L. (2008), Statistical universalities in fragmentation
under scaling symmetry with a constant frequency of fragmentation. Phys. D: 41,085405
6. V.L. Saveliev and M.A. Gorokhovski (2012) Renormalization of the fragmentation
equation: Exact self-similar solutions and turbulent cascades, Phys. Rev. E. 061112
7. R. Wood, 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 2373–2423

Vortex cluster structures and particle motions inhigh Reynolds number turbulence
Takashi Ishihara1
1

Graduate School of Environmental and Life Science, Okayama University,
Tsushima-naka, Kita-ku, Okayama 700-8530, Japan
takashi ishihara@okayama-u.ac.jp

To elucidate the processes of rapid rain drop growth in turbulent clouds and planetesimalformation in protoplanetary disks, it is important to properly understand the motions ofinertial particles in high-Reynolds number (Re) turbulence. Although the Reynolds numbers of the realistic turbulence phenomena are too high to be attained by direct numericalsimulations (DNSs) of the Navier-Stokes equations, large-scale DNSs of turbulence using
recent supercomputers may provide many hints to understand such complex ﬂow phenomena. It has been shown that in high Re turbulence micro-scale vortices form clustersof various sizes ranging from the Taylor micro-scale to the integral length scale [1, 2, 3].
On the other hand, it is known that particles with large inertia are not so sensitive tothe details of micro-scale vortices. Therefore, the motions (clustering and collisions) ofparticles with large inertia in the vortex clusters in high Re turbulence, which are thekey to understand the realistic phenomena, may be analysed by using appropriate coarse
graining to the large-scale DNS data.
In this paper, ﬁrstly the vortical cluster structures observed in large-scale DNSs of turbulence with the number of grid points up to N 3 = 122883 and the Taylor microscale
Reynolds number up to Rλ ∼ 2300 are reviewed [1, 2, 3]. Secondly, some results of the
DNS data analyses to see the relationships between the motions of inertial particles andthe vortical cluster structures are presented [4, 5].
The computational resources of the K computer provided by the RIKEN Advanced Institute for Computational Science through the HPCI System Research project (Projects
ID:hp190076, hp190084, and hp190185) were partly used in this study. This study isalso partly supported by “Joint Usage/Research Center for Interdisciplinary Large-scale
Information Infrastructures” (Project ID: jh190068).
References[1] T. Ishihara, T. Gotoh and Y. Kaneda, Study of HighReynolds Number Isotropic
Turbulence by Direct Numerical Simulation, Annu. Rev. Fluid Mech. 41, 165 (2009).
[2] T. Ishihara, Y. Kaneda and J.C.R. Hunt, Thin Shear Layers in High Reynolds Number Turbulence–DNS Results, Flow Turbulence Combust 91, 895 (2013).
[3] T. Ishihara, A. Uno, K. Morishita, M. Yokokawa and Y. Kaneda, Vortex Clusters
and Their Time Evolution in High- Reynolds-Number Turbulence, APS DFD Meeting
Abstracts (2016). http://meetings.aps.org/link/BAPS.2016.DFD.G3.2[4] Y. Sakurai and T. Ishihara, Relationships between Small-scale Fluid Motions andInertial Particle Clustering in Turbulence, J. Phys. Soc. Japan, 87, 093401 (2018).
[5] Y. Sakurai, H. Shiraishi and T. Ishihara, Distribution and motion of inertial particles around high vorticity regions in coarse-grained homogeneous isotropic turbulence,(Accepted in Japan Society of Civil Engineering), (2020) (in Japanese).

Reseach of aerosol cloud interaction using the global
scale simulation with explicit representation of cloud
microphysics
Yousuke Sato1,2 Daisuke Goto3 , Kentaroh Suzuki4 , Takuro Michibata5 ,Toshihiko Takemura5 , Hirofumi Tomita2 and Teruyuki Nakajima6
1

Fuculty of Science, Hokkaido University, Kita-ku, Sapporo 060-0810, JapanRIKEN Center for Computatioinal Science, Chuo-ku, Kobe, 6500047, Japan
3
National Institute for Environmental Studies, Onogawa, Tsukuba, 305-8506, Japan
4
Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwanoha,
Kashiwa, 277-8568, Japan
5
Research Institute for Applied Mechanics, Kyushu University, Kasuga koen, Kasuga,
816-8580, Japan
6
Japan Aerospace Exploration Agency, Tsukuba, 305-8505, Japan2

yousuke.sato@sci.hokudai.ac.jp
In this presentation, we overview the research of aerosol cloud interaction (ACI) using global scale model with explicit representation of cloud microphysics, called Nonhydrostatic ICosahedral Atmospheric Model(NICAM). Using NICAM and K computer,
global scale simulation for 3 years with grid spacing of 14 km and for 2 weeks with grid
spacing of 3.5 km were conducted. From the results of the simulation, we estimated thesusceptibility (λ) deﬁned as:
[

]

[

]

λ = d Log10 (LW P ) /d Log10 (Na ) ,(1)

where LWP and Na is liquid water path and number concentration of aerosol, respectively.
λ has been believed the positive based on the literature. However, recent satellite observational(Fig.1a) indicated that the λ is not always positive. Our global scale simulation
reasonably reproduced the distribution of λ (Fig. 1b) due to the explicit representationof clouds process, although traditional GCM estimate λ as positive (Fig. 1c).
In the presentation, we also introduce other results of NICAM targeting on ACI.
A-Train ( c)a

GCRM ( )

b

60N

60N

60N

30N

30N

30N

0

0

0

30S

30S

30S

60S

60S
average : -0.174

0

60E

120E

60S
average : -0.220

180

120W

60W

0

GCM ( )

c

60E

120E

average : 0.620
180-2.0 -1.6 -1.2 -0.8 -0.4 -0.2 -0.1

120W
0

0.1

λ, λ c

60W
0.2

0

60E

0.4 0.8 1.2

1.6 2.0

120E

180

120W

60W

Fig. 1: Susceptibility λ estimated by (a)A-Train satellite,(b)GCRM, and (c)GCM[1]References[1] Y.Sato, D. Goto, T. Michibata, K. Suzuki, T. Takemura, H. Tomita, and T. Nakajima, Aerosol eﬀects on cloud water amounts were successfully simulated by a global
cloud-system resolving model, Nat. Comm. 9, 985, doi:10.1038/s41467-018-03379-6(2018).

Banquet
Date : March 4, 18:30­20:30
Place: Hotel Mielparque Nagoya

JR Chikusa St.

Hotel
Mielparque

JR Tsurumai St.
NITech(1) NITech → JR Trusumai St.
(2) JR Tsurumai St. → JR Chikusa St. (￥150)(3) JR Chikusa St. → Hotel Mielparque(Just in front of the Entrance No. 1)

Campus Map
Hall (1F, Building 4)

JR Tsurumai St.

Lunch Map
⑫
⑬

①

⑪

④

I1
Auditorium

③

②

⑦

⑧
⑩ ⑨

⑤⑥

① 大学生協会館 (レストラン, コンビニ) / University hall Cafeteria (1F, 2F) & food shop (1F)
② 大学生協コンビニ＆軽食(2F) / University convenience store & restaurant (2F, light meal)③ Café Sala (cafe)④ さぼてん （定食） / Saboten (restaurant, set meal)⑤ カフェハウスリーベ （喫茶） / Café hause LIEBE (Cafe)⑥ みやこ （うどん, 定食） / Miyako (restaurant, udon noodle & set meal )⑦ サークルＫサンクス （コンビニ） / Circle­K Sankus (convenience store)⑧ 古味煮亭 （定食） / Komini­tei (restaurant, a set meal)⑨ 清水庵曙店 （そば, うどん） / Shimizu­an (restaurant, buckwheat noodles (soba), udon )⑩ カフェドカプチーノ （喫茶） / Cafe’d Cappuccino (Cafe)⑪ ソレイユ（レストラン、喫茶） 名大病院最上階 / Soleiyu (restaurant, cafe) in Nagoya Uni. Hospital (14F)⑫ イオン千種ショッピングセンター （レストラン街，フードコートあり） / AEON Chikusa shopping center(many restaurants & food court)⑬ 浩養園（ビアガーデン，レストラン） / Kouyouen (Beer restaurant)

Restaurant with vegetarian menu①④②③
NITechTsurumaiSt.

① インド亭 イオン千種店 （Indo­tei in Aeon town Chikusa）
・・・インドカレー（Indian curry restaurant）② ムガルパレス （MUGAL PALASE）
・・・インドカレー（Indian curry restaurant）③ 気晴亭（Kiharu­tei）・・・トンカツ（Pork cutlet restaurant）
マクロビオティック料理あり（with macrobiotic menu）④ ORIBIO Cafe Dining Nagoya
・・・ベジタリアンレストラン（Vegetarian restaurant）

How to access Internet using wireless LAN
If you want to access Internet on your PC via our campus network,
please receive a sheet stating security key and ID/PW from an
information clerk, and try to access by following the below procedure.

1. Connect to the access point of wireless LAN
Setting information
ESSID (SSID)

Network confirmation

5GHz band (IEEE802.11a/n)

MAINS­5G

2.4GHz band (IEEE802.11g)

MAINS­2G

Setting for security(choice)

WPA2­PSK ＋ CCMP（AES）

Security Key (pass phrase)

Please see a sheet

WPA­PSK ＋ TKIP（RC4）

2. Execute WEB authenticationi.
ii.

Activate WEB browser (Internet explore, Safari, etc...)
You can see the WEB authentication page below

← ID
← PW
← click here (log­on)iii.

Enter the ID/password in the blank and click log­onNote: If you do not access Internet during 10～15 minutes, the WEB authentication
is automatically canceled (need to execute WEB authentication again) .

Organized by
Toshiyuki Gotoh
Takeshi Watanabe
Takashi Naitoh
Izumi Saito
Tatsuya Yasuda
Graduate School of Engineering
Nagoya Institute of Technology
Tel: +81­(0)52­735­5377
URL : http://comphys.web.nitech.ac.jp/cloud3