Implementation and validation Implementation and validation

of a prognostic large-scale cloud andof a prognostic large-scale cloud and

precipitation scheme in ARPEGEprecipitation scheme in ARPEGE

(F.Bouyssel,Y.Bouteloup, P.Marquet)(F.Bouyssel,Y.Bouteloup, P.Marquet)

HIRLAM workshop on convection and clouds Tartu,Estonia, 24-26/01/2005

OutlookOutlook

1.1.Description of the scheme Description of the scheme

2.2.ValidationValidation

3.3.Conclusions and perspectivesConclusions and perspectives

Description of the large-scale Description of the large-scale

cloud and precipitation cloud and precipitation

schemescheme

GeneralitiesGeneralities

Developed by P. Lopez (QJRMS, 2002)

Designed for variational assimilation of cloud and RR obs

Prognostic var : Qc (cloud condensates) & Qp (precip water)

Semi-lagrangian treatment of the fall of precipitation

(Lopez,2002)

Specification of hydrometeor distributions:Concentration : N(D) = N0exp(-D) (Marshall-Palmer)Mass: M(D)=D

Fall velocity: V(D)=D

Condensation/evaporation of cloud water: Smith (1990)Triangular PDF Cloud water amount (Qc) and Cloud fraction (Neb)

Autoconversion: Kessler-type (1969)

Collection of cloud water by rain and snow:Integration of the collection eq. over N(D) and V(D)

Precipitation evaporation:Integration over N(D) of the equation:

Precipitation sedimentation:Originality: conservative semi-lagrangien treatment based on constant fall speeds for snow and rain (0.9 and 5 m/s)

00 c

c

cc

c

cAUTcAUTO q

f

qΗq

f

qKfP

D

)1)(1(4

K

FfRH

dt

DdM c

Partition of Qc into Ql,QiPartition of Qc into Ql,Qi

otherwise 0

16273for 2

exp1ice of Proportion

2

K.TTT

TTf

f

(1990)Smith 1

261

/

'2''' 22

p

svs

sssatcrit

CL

TqTqqRH

kmin ,

150

20exp4.099.04.0 and

99.0 with ,,

min,

max,max,min,

xx

RH

RHRHRHfRH

crit

critcritcritcrit

Tuning of RhTuning of Rhcritcrit

Sensitivity to fall speedsSensitivity to fall speeds

(Lopez,2002)

Sensitivity to main tunable parametersSensitivity to main tunable parameters

(Lopez,2002)

ValidationValidation

Diffusion on conservative variables (qDiffusion on conservative variables (qtt, , ll))

Validation on 4 months integration (T149C1.0)Validation on 4 months integration (T149C1.0)

Qliq Qice Qneb

Oper

New

Validation on 4 months integration (T149C1.0)Validation on 4 months integration (T149C1.0)

MEAN=8.53 W.m-2

RMS=25.4 W.m-2

MEAN=5.42 W.m-2

RMS=23.0 W.m-2

Oper

New

Validation on 4 months integration (T149C1.0)Validation on 4 months integration (T149C1.0)

MEAN=2.13 W.m-2

RMS=12.5 W.m-2

MEAN=0.72 W.m-2

RMS=11.8 W.m-2

Oper

New

Validation on 4 months integration (T149C1.0)Validation on 4 months integration (T149C1.0)

Preliminary results in T358C2.4 (forecasts only)Preliminary results in T358C2.4 (forecasts only)

Case study : 09/12/2004Case study : 09/12/2004

Oper

New

Case study : 09/12/2004Case study : 09/12/2004

Oper New

ConclusionsConclusions

PerspectivesPerspectives

ConclusionsConclusions

Importance of diffusion on conservative variableImportance of diffusion on conservative variable

Stability and acceptable sensitivity to time step lengthStability and acceptable sensitivity to time step length

Promising results to improve mountain precipitations Promising results to improve mountain precipitations

(intensity and localisation)(intensity and localisation)

Improvment of LWP in the TropicsImprovment of LWP in the Tropics

Overestimation of cloud condensate and cloudiness near Overestimation of cloud condensate and cloudiness near

the ground the ground

PerspectivesPerspectives

Modify snow meltingModify snow melting

Evaluation in assimilation experimentsEvaluation in assimilation experiments

Take into account turbulence to diagnose Qc and NebTake into account turbulence to diagnose Qc and Neb

Introduce further sophisticationsIntroduce further sophistications