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)


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. )


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/


　 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 ( Ｗｍ -2)

Inte

gra

ted

liq

uid

wa

ter

（ｇ

ｍ-2

）



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


　 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


Inte

gra

ted

liq

uid

wate

r（

ｇｍ

-2）


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

）


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).


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.


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)


　 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


Inte

gra

ted

liq

uid

wa

ter

（ｇ

ｍ-2

）



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


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


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）


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


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

　　：


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


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


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.


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 。


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

cfmip/gcss blwg workshop 2009/06/08-12 a comparison between bin and bulk models in the case of...

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