CFMIP/GCSS BLWG workshop 2009/06/08-12 A comparison between bin and bulk models in the case of...
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Transcript of CFMIP/GCSS BLWG workshop 2009/06/08-12 A comparison between bin and bulk models in the case of...
CFMIP/GCSS BLWG workshop 2009/06/08-12
A comparison between bin and bulk models
in the case of boundary layer clouds observed during RICO
Kozo Nakamura, Yasushi Fujiyoshi, Kazuhisa Tsuboki, Naomi Kuba
(JAMSTEC)
CFMIP/GCSS BLWG workshop 2009/06/08-12
AimAim : To improve the parameterization schemes used in warm bulk
cloud microphysics model.
Question? For the better simulation, • Should we divide water drops into more than 2 categories including
drizzle as one of the categories ?• Should we use 3 (or more) variables for each group?
Method : Using the results of a bin scheme model, we will develop a new bulk parameterization scheme.
Case : RICO intercomparison case for the first case.
(The scheme will be fitted for several cases in future. )
CFMIP/GCSS BLWG workshop 2009/06/08-12
Model setting ( RICO LES intercomparison )
grid size Δ x= Δ y= 100 m , Δ z= 40m number of grids 128 x 128 x 100 →Domain 12.8km×12.8km× 4.0km
θ,qv,u,v : shown in following figures
Horizontally cyclic B. C.Bottom B. C. SST 299.8 K=T air + 0.6℃Forcing : subsidence : w = -0.005 at 2260m constant divergence below. horizontal drying and heating.Analysis : t =20 ~ 24 hrs.
1 moment bulk MESO-NH SAM JAMSTEC Utah EULAG 2DSAM2 moment bulk DALES UCLA WVU COAMPS UKMO RAMSBin AMS@NOAA SAMEX DHARMA
Fields of trade wind congestus typical cloud base 600 m typical cloud top ~ 2000-3000 m
ExampleRF-09
17 Dec 042004
From http://www.knmi.nl/samenw/rico/
CFMIP/GCSS BLWG workshop 2009/06/08-12
Results of RICO intercomparison (20-24hr)
15 models :
Open circles
1-moment bulk models
red circles
variables : QC, QR
2-moment bulk models
green circles
var : QC, QR, NC, NR
Bin models
blue circles
var : Q1- Q? , N1-
Surface precipitation ( W m -2)
Inte
gra
ted
liq
uid
wa
ter
(g
m-2
)
From http://www.knmi.nl/samenw/rico/
CFMIP/GCSS BLWG workshop 2009/06/08-12
falling rain from
upper grid
physical process in 1-moment bulk model
water vapor
Temp
cloud droplets
cloud amount
evaporation condensation
Ⅰ auto-conversion (without rain) 1. condensational growth 2. collision between
cloudsⅡcollision-coalescence R+C⇒R
heatQsat
falling rain to lower grid
grid model
liquid water is divided into two groups not falling cloud and falling rain
rain drops
CFMIP/GCSS BLWG workshop 2009/06/08-12
Results of RICO intercomparison Autoconversion
scheme red marks with
numbers2 moment bulk models green marksBin models blue marks
CReSS 1-moment bulk
closed red marks with capital letters
Surface precipitation ( W m -2)
Inte
gra
ted
liq
uid
wate
r(
gm
-2)
From http://www.knmi.nl/samenw/rico/
0 0.5 1 1.5 2qcgkg0
0.5
1
1.5
2
2.5
3
cqdtd
01x^3ggk
s
Mod. Berry
Kessler
Berry
cloud water ( g/kg )Con
vers
ion
rate
(m
g/k
g/s
)
CFMIP/GCSS BLWG workshop 2009/06/08-12
Model : CReSSthe Cloud Resolving Storm Simulator developed by Dr. Tsuboki and his colleagues
Basic equations non-hydrostatic, compressible equations
advective form
Spatial discretization finite difference scheme (2,4,3)
Topography terrain following coordinate
Temporal scheme mode splitting
Slow mode - explicit scheme
Fast mode - Horizontal Explicit Vertical Implicit scheme
Cloud physics – bulk scheme ⇒ bin scheme for warm rain
vapor, cloud, rain, cloud-ice(2) snow(2) graupel(2).
Turbulence - Smagorinsky scheme or Deardorff scheme
Cloud physics – bulk scheme ⇒ bin scheme for warm rain
71 bins (radius of drops covers from 1μ m to 3.5 mm)
Ratio of mass between the adjacent bin is sqrt(2).
CFMIP/GCSS BLWG workshop 2009/06/08-12
1) PCASP data was used and assumed that the RH in the instrument was 0.8x the ambient RH2) The measured wet sizes were converted to dry sizes using Kohler theory and an assumed composition of ammonium sulfate.3) The dry size distributions were averaged over all sub-cloud legs on RF12 (Jan 11)4) A bimodal lognormal was fitted to the spectra5) rg1=0.03 μm, sig1=1.28, n1=90 (cm-3), rg2=0.14 μm, sig2=1.75 n2=15 (cm-3) By courtesy of Dr Hongli Jiang and Dr. Margreet van Zanten
Aerosol size distribution and activated CCN.
vertical velocity a b S(%) NC(cm-3)
w < 24.0 4710 x w1.19 1090w + 33.2 0.2 17
24.0 < w < 50.0 11700 w-1690 10600 w -1480 0.4 55
50.0 < w < 100.0 4300 w1.05 2760 w0.755 0.5 75
100.0 < w < 300.07730 – 15800exp(-1.08w)
6030 – 24100 exp(-1.87w)
1.0 104
300.0 < w 1140 w -741 909 w -56.2 2.0 105
bSN
SNaN
c
cd
)(
)( Parameterization by parcel model. Kuba and Fujiyoshi (2006)
observed size distribution of CCN.
CFMIP/GCSS BLWG workshop 2009/06/08-12
1 moment bulk
2 moment bulk
Bin
CReSS-bin
QC QR t=20 ~ 24hr ( 15 models+1 )
雨水混合比 (g/kg)Rain water (mg/kg)Cloud water (mg/kg)
CFMIP/GCSS BLWG workshop 2009/06/08-12
Results of RICO intercomparison1-moment bulk models red marks with numbers2-moment bulk models green marksBin models blue marks
CReSS 1-moment bulkclosed red marks with capital letters
CReSS Bin model closed blue mark
Surface precipitation ( W m -2)
Inte
gra
ted
liq
uid
wa
ter
(g
m-2
)
From http://www.knmi.nl/samenw/rico/
CFMIP/GCSS BLWG workshop 2009/06/08-12
Vertical profiles of cloud processes 33/71
Cond.
Eva.
Auto1>0 C → R(cond)Too large? Auto1<0 C → R (eva)
Auto2 C + C→E
Coalescence
R + C→R
Cloud water(mg/kg/s*1.e5)
hei
gh
t (k
m)
Rain water(mg/kg/s*1.e6)
t=20-24hr. boundary between C&R is 47.9μ m . i<34
CFMIP/GCSS BLWG workshop 2009/06/08-12
Vertical Profiles of cloud processes Cond.
Eva.
Auto1>0 C → R(cond)Too large? Auto1<0 C → R (eva)
Auto2 C + C→E
Collision R + C→R
Rain water(mg/kg/s*1.e6)
t=20-24hr
hei
gh
t (k
m)
Mod. Berry modelRain water
t=20-24hr. boundary between C&R is 47.9μ m
CFMIP/GCSS BLWG workshop 2009/06/08-12
autoconversion2 in terms of QcColor indicates the
group of number concentration of cloud 。
Brown : the maximum number concentration group
light blue, purple, blue, green
Red : the smallest number concentration group .
( for the same mixing ratio,
the small number concentration, the larger conversion rate)
cloud water ( g/kg )
auto
con
vers
ion
2(
mg
/kg
/s)
CFMIP/GCSS BLWG workshop 2009/06/08-12
Autoconversion(Qc, Nc)Averaged over each
group ⇒ColorsBrown : the
maximum number concentration group
light blue, purple, blue,
green : the smallest number concentration group .
Black : total average.
cloud water ( g/kg )au
toc
on
v(
mg
/kg
/s)
Autoconversion rate used in the bulk model.
Kessler
Berry
Modified Berry
CFMIP/GCSS BLWG workshop 2009/06/08-12
Parameterization of each process 1independent variables (assuming 2-moment bulk scheme)
cloud related Qc, Nc, average mass of droplet, radius⇒ rain related Q⇒ R, NR, average mass of drop, radius
environment T, θ, Qv⇒ 、 Qv-Qsat 、A、 p 、 e 、 rh 、 w 。
Process ( mass & number ) variables condensation to cloud cloud & environment
evaporation from cloud cloud & environment
autoconv1( c -> r ) cloud & environment
autoconv1 ( r -> c ) rain & environment
autoconv2 cloud (& environment)
collision-coalescence cloud, rain & environment
:
CFMIP/GCSS BLWG workshop 2009/06/08-12
Parameterization of each process 2An example
autoconv( c -> r ) cloud & general
Previously proposed formula (examples).
Assumed formula in this work
⇒ Searching the combination of variables which gives largest correlation coefficient.
),(99899.137.21exp50139.2
5372.573.40expChen 2 cc
c
ccc rg
n
qE
r
ErEn
87.0)(Lee 173.0184.2363.0 corqqqe vsvc
)log(logloglog 3322110 xaxaxaay
CFMIP/GCSS BLWG workshop 2009/06/08-12
Parameterization of each process 3Results of fitting parameters (few examples)
708.0Auto1 18.267483.11 corrhrc cauto
906.0Auto2 5087.456286.02 cormqc ccauto
92846.0CondC 45268.054485.0 coreenc vsvccondc
Simulation results of the bulk model using these parameters
○ Conversion from cloud to rain is very small, because the large number of small value occurrence determines the fitting parameter.
○ Rain does not develop as in the bin model.
○ We need some sophisticated technique to make a bulk parameterization scheme from the bin model results.
998.0Coal 60793.099413.0 corqqc rccoal
CFMIP/GCSS BLWG workshop 2009/06/08-12
Summary○ We applied CReSS-bin model to GCSS-BL WG RICO intercompari
son case.
○ Although the results show some difference from other model results, the results are within the range of the variation of the results of the models. (We need to compare the results with observational results and other bin model results. )
○ We need some sophisticated technique to make a bulk parameterization scheme from the bin model results.
Future work
○ to develop a 2 ( or more ) -moment bulk scheme.
○ to apply the model to other cases and extend the model.
CFMIP/GCSS BLWG workshop 2009/06/08-12
K
B
N
Integrated Liquid water(gm-2)
1000
Observational estimate
0
Surfa
ce p
reci
pita
tion(
mm
day
-1)
1
Liquid water and surface precipitation
DYCOMS Ⅱt= 3~6 hr 。
CFMIP/GCSS BLWG workshop 2009/06/08-12
Physical process in bin modelLiquid drops are divided into groups (bins). Size distribution of liquid drops is indicated by the number concentration of each bin
equilibrium
number
remapping
mass conserva
tion
coalescence
remapping
rain
cloud
autoconversion : pink and orange (C+C->R)coalescence : orange (R+C->R)
boundary between cloud
and rain
Cond↑
Eva↓
radius
Change of bin boundary