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Description ofDescription of the MésoNH-AROME the MésoNH-AROME microphysical scheme and its microphysical scheme and its
evaluation by remote sensing toolsevaluation by remote sensing tools
Jean-Pierre PINTYJean-Pierre PINTY, Evelyne RICHARD,, Evelyne RICHARD,
Jean-Pierre CHABOUREAU and Frank LASCAUX Jean-Pierre CHABOUREAU and Frank LASCAUX (LA/OMP),(LA/OMP),
Yann Yann SEITYSEITY (Météo-France) (Météo-France)
11stst AROME training course, Poiana-Brasov, Romania, 21-25 November 2005 AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
OutlookOutlook• Introduction• Common assumptions made in microphysical schemes• The current scheme developed and used in MésoNH
• Warm microphysics• Mixed-phase microphysics
• Examples and tools to evaluate the scheme• Conclusion and perspective
The explicit simulation of the Water Cycle is a major issue of many mesoscale studies and model
applications. Microphysical schemes are the key parametrisation to
follow the evolution of the condensed phases of water at high resolution in the atmosphere.
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Explicit cloud modelingExplicit cloud modelingThere are many cloud types to simulate ! Fogs, Extended cloud sheets, Cirrus, Cumulus clouds
Wide span of particle size: ~ 4 decades (µm cm) of particle habit: many ice particle types to consider
Microphysical fields describe discontinuous and sparse objects. Clouds have sharp boundaries ( microphysical fields are ≥0) which do not fit a regular grid system ( cloud fraction)
Many interactions involving clouds and precipitation:Dynamics, Radiation, Surface, Aerosols, Chemistry, Electricity What is the level of complexity really needed for a
microphysical scheme to cover all these applications ? 1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Choice of the microphysical variablesChoice of the microphysical variables
• State variables: Mixing ratios (mass of water / mass of dry air)• Assumptions about number concentrations: parameterization or
Aerosol Physics• Number of cloud and precipitation variables
2 water variables for warm clouds: Cloud water (droplets), Rain water (drops)
2, 3, 4, 5 ice variables for cold clouds: Cloud ice (pristine crystals), Snow (large crystals), Aggregates (assemblage of crystals), Graupel (rimed crystals), Hail (large heavily rimed crystals)
General case Mixed-phase microphysics with liquid & ice phases1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Common features of many microphysical Common features of many microphysical schemesschemes
• Limited number of water species (~ 6): 1 vap. + 2 liq. + 3 ice
• Size distribution:Mathematical (parametric) distribution law [0 < D < ∞]
• Mass-size and Fall speed-size relationships:Power law analytical integration
• Uncertainties about the representation of some processes and about the value of some bulk coefficients:
collision-sticking efficiencies of collection kernels autoconversion processes (onset of precipitating particles) adjustment to saturation (pure ice-phase clouds)
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Description of the microphysical scheme Description of the microphysical scheme of MésoNHof MésoNH
• Size distribution (n(D)): Generalized Gamma law
N is the total concentration g(D) is the normalized distribution lawλ is deduced from the mixing ratio (α, ν) are free shape parameters (Marshall-Palmer law: α=ν=1)
• Very useful p-moment formula
dD)D-(λexpDλν)Γ
αg(D)dDn(D)dD )(1
NN
λ
1
Γ(ν)
)αpΓ(νNdDn(D)DM(p)
p0
p
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Particle size distributions in Particle size distributions in microphysical schemesmicrophysical schemes
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Unnormalized Distribution Laws
Exponential decay : rain, snow, graupel, hail
Modal distribution : droplets, cloud ice
• Mass-Size relationship: m=aDb
• Fall speed-Size relationship: v=cDd . (ρ00/ρa)0.4
Category
Parameters
c
d
a
b
0.640.660.271.000.82
2071245.18008423.2e7speed
mass3
524
Cloud water
3.02.81.92.53
47019.60.020.82524
HailGraupelSnowflakeAggregate
Cloud
iceRain water
Microphysical characteristics (1)Microphysical characteristics (1)
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Foote-DuToit correction
The a, b, c and d coefficients (MKS units) are adjusted from ground or in situ measurements
Fall speed of the hydrometeorsFall speed of the hydrometeors
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Terminal velocity at P=700 hPa
rain
hail
graupel
snow
The total concentration of precipitating particles: rain, snow, graupel, is given by N=Cλx instead of fixed N0 value (N0=N.λ) as it is
often the case in classical Marshall-Palmer schemes
Microphysical characteristics (2)Microphysical characteristics (2)
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
N0
D
N(D)
decrease
dDD)-(λexpNn(D)dD
:Palmer-Marshall
)(0
Fixed value
1 23
Lin scale
Log
sca
le
N(D)
N0
D
decrease
dDD)-(λexp λn(D)dD
:lExponentia-- Gamma
)( N
1 2 3
Parameterized
Lin scale
Log
sca
le
0°C
Autoconversion
0°C
RimingAggregation
Collection Collection
Deposition
Freezing
Nucleation
Melting
gsrSedimentation Evaporation
Mixed-phase cloudsMixed-phase clouds
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
h
Additional part to include hail explicitly
Microphysical Scheme diagramMicrophysical Scheme diagram
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Vapor r_vDroplets r_cRaindrops r_rPristine ice r_iSnow/Agg. r_sGraupel r_gHail r_h
Keys
Vapor r_vDroplets r_cRaindrops r_rPristine ice r_iSnow/Agg. r_sGraupel r_gHail r_h
Keys
Microphysical SchemesMicrophysical Schemes
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005c
• Warm clouds: no ice phase « Kessler » scheme (1969)
• To simulate the microphysical processes inside pure warm clouds (stratus decks, shallow cumuli)
• To simulate the warm processes occuring in the low levels of deep convective clouds
• Mixed-phase clouds: additional ice phase with water-ice and ice-ice interactions full « Méso-NH » scheme (... 1998)
• To simulate ice clouds (cirrus)• To simulate heavily precipitating deep convective clouds
Aerosols
Cloud dropletsRaindrops
Activation
Autoconversion
Accretion
SedimentationEvaporation
Condensation
Water cycle in warm cloudsWater cycle in warm clouds
NOT CONSID
ERED
in the p
resen
t sch
eme
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005c
Warm microphysics: AutoconversionWarm microphysics: Autoconversion
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Formation of rain: some cloud droplets become raindrops collection growth of a few big droplets role of the turbulence, width of the droplet size distribution subject of very active research to include Nc, Dc, σc, εturb
colliding droplets precipitating drop
… a very crude but efficient parametrization
mkgc
s
with
ccMax
crit
critc
AUT
r
AUT
33
13
aa
.105.0rρa
10K
rρarρa,0.0Kt
)r(ρ
t
)r(ρ
(Time scale)-1 Threshold
Warm microphysics: AccretionWarm microphysics: Accretion
Growth of raindrops: raindrops collect droplets during their fall
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
1E
)2d(MEr4
Dd)D(nt
)D(
t
)r(ρ
t
)r(ρ
)DK(ρr
Dd)D(nD)D,DK(ρ6t
D
acc
acc
0
aa
a0
3cw
with
rm
m
rcrrrACC
c
ACC
r
ACC
rc
cccrcr
ACC
… the parametrization is based upon the collection kernel
Collection kernel: geometrical sweep-out
EDvD4
πD,DKDDbut
EEDvDvDD4
πD,DK
accr2rrcrc
stickcollrc2r
2crc
cDrd (ρ00/ρa)0.4
Warm microphysics: EvaporationWarm microphysics: Evaporation
Decay of raindrops: raindrops evaporate below cloud
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
v
Dk
EVA
v(D)DRe0.22,FwithReF1f
)0Shere()/rr(rS
P)(T,(T)e
TR
T(T)R
LP)(T,A
with
P)(T,A(D)fDS4tm(D)
wv,vsvsvwv,
vsw
v2
va
2v
w
wwv,
0
a Dd)D(nt
)D(m
t
)r(ρrrr
r
EVA
r
EVA
… a rather accurate parametrization
Evaporation rate: function of the undersaturationSv,w and of a ventilation coefficient
RH<100%or
Sv,w<0
Df
Drop Reynolds number
Warm microphysics: SedimentationWarm microphysics: Sedimentation
Fallout of raindrops: vertical flux of raindrops relative to the air
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Sedimentation rate from sedimentation flux
0
dba00
0.4
0
Dd)D(nD )ρρ(caSed_flux
Dd)D(n)D(m)D(vSed_flux
rrrrrr
rr
rrrrr
Sed_fluxt
)r(ρa
zr
SED
… no true size sorting effect (size shift toward large Dr)
Automatic time-splitting for numerical stability
Additional part to include hail explicitly
Microphysical Scheme diagramMicrophysical Scheme diagram
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
WA
RM
mic
roph
ysic
s
COLD microphysics
Ice nucleation processesIce nucleation processes(a very simplified treatment !)(a very simplified treatment !)
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Homogeneous nucleation: Spontaneous freezing of the cloud droplets when -35°C<T<-44°CandRainGraupel, T<-35°C
tVT)()VdtT)(exp(1
JJP HOM
tt
tHOM
Basics: Freezing probability P of a droplet of volume V
Integration over the droplet size distribution
M(3)
M(6)rρ
6,
rρ
t
)r(ρa
aacHOM
ci
HON
TJt
Min
Heterogeneous nucleation: Based on ice crystals growingon ice nuclei (IN)
… the treatment is more complex when Ni is a prognostic variable
.................................SSexp
Texprrrr
C5T.......,.........
C5TC2,...
22202
11
101
2
1
iNUNU
vsivswvsivNUNU
NU
NUIN
NN
NN
N
NN
NNMaxt
ttiIN
NUi
HEN
,0m
t
)r(ρ 0a
Basics: Meyers et al. (1992) as in Ferrier (1994)
Bergeron-Findeisen effectBergeron-Findeisen effectGrowth of pristine ice crystal atthe expense of cloud droplets
… maximum effect at T=-12°C
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
)()( TeTe swsi
droplet
_rateDepositionn_rateEvaporatio
ice
water vapor
Autoconversion of pristine ice crystalsAutoconversion of pristine ice crystals
Small pristine ice crystals do not grow by collection !
… pristine crystals (D<150µm) poorly rime or grow by agregation
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Hexagonal plates: (1) D=160 µm
Stellar crystals: (1) D=200 µm
Hexagonal columns: (1) L=70 µm
Computed dropcollection efficienciesafter Wang & Ji (1992)
Autoconversion of pristine ice crystalsAutoconversion of pristine ice crystals
Formation of snow: Some pristine ice crystals grow by deposition to get bigger than 100 ~ 200 µm
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
+ vapor
Ice crystal Snow particles
… modified threshold to enhance cirrus sheet precipitation
mkgi
s
with
iiMax
crit
criti
AUT
s
AUT
33
1t
3
aa
.1002.0rρa
)TT(025.0exp10K
rρarρa,0.0Kt
)r(ρ
t
)r(ρ
(Time scale)-1
Threshold
Dry growth of snow: snowflakes collect ice crystals when falling
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
T-T0.05exp25.0E
)2d(MEr4
Dd)D(nt
)D(
t
)r(ρ
t
)r(ρ
)DK(ρr
Dd)D(nDa)D,DK(tD
tagg
agg
0
aa
a0
ibi
i
with
m
m
sissss
AGG
i
AGG
s
AGG
si
iiisis
ACC
… a very crude but efficient parametrization
Collection kernel: geometrical sweep-out
EDvD4
πD,DKDDbut
EEDvDvDD4
πD,DK
aggs2ssisi
stickcollsi2s
2isi
cDsd (ρ00/ρa)0.4
Aggregation of pristine ice Aggregation of pristine ice snow snow
Growth by depositionGrowth by deposition
Snow and graupel grow (inside clouds) or decay (outside clouds):
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
νD
CC
C
v
SUBDEP
v(D)DRe;Sc,ReScwhere
ffff
D
with
P)(T,A(D)fDS4tm(D)
2131
2210
1
iiv,/
0
,,,,
/
,a
/Dd)D(n
t
)D(m
t
)r(ρgsgsgs
gs
SUBDEP
gs
SUBDEP
… a fully analytical parametrization
A generalization of the raindrop evaporation
Schmidt & Reynolds numbers
Snow Graupel
water vapor
Cap
acita
nce
Ventil
ation
Generalities about collection processesGeneralities about collection processes
Collection processes: based oncontinuous collection kernels(geometrical swept-out concept)
E)D(v)D(vDD4
π)D,DK( xyyyxxyx
2
yx )(
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
t
)r(ρ
t
)r(ρ
t
)r(ρ
Dd)D(nDd)D(n)D(m)D,DK(t
)r(ρ
Dd)D(nDd)D(n)D(m)D,DK(t
)r(ρ
aaa
0
yx
0
a
xxx
0
yyyyyyx
0
xa
][
][
y
COLL
x
COLL
z
COLL
yyyxxxxxy
COLL
COLL
Collection+Conversion of precipitating particles: tabulated integrals
+
Snow Rain Graupel
… but the treatment is even more complicated in the case of partial conversion (function of particle size)
Snow riming processSnow riming process
Conversion threshold: Ds > Dslim = 7 mm as in Farley et al. (1989)
Splitted snow size distribution
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
or
1E and EDvD4
πDKD,DKwith
D)dD(Dn)dD(D)n(D)mD,K(D
t
rρ
D)dD(Dn)dD(D)n(D)mD,K(D
t
rρ
rimrims2sssc
ss0 cccccscga
0 ss0 cccccscsa
lim
s
lim
s
s
RIM
sRIM
… a continuous conversion of snow by riming
+
Snow Droplet Snow Snow Droplet Graupel
+
Conversion when Dr > Drlim : based on the density of a mixture of a
snowflake and a raindrop and leading to
Snow collection processSnow collection process
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
or
Drvr~Dsvs as integrals normalized latedwith tabu
)dD(DnD
)dD(D)n(D)mD,K(Dt
rρ
)dD(DnD
)dD(D)n(D)mD,K(Dt
rρ
0 ssrrrrrsrga
0 ss0 rrrrrsrsa
lim
r
lim
r
s
COL
sCOL
… large collected raindrops convert snow into graupel
+
Snow Rain Snow Snow Rain Graupel
+
ρρρsgsr
0.5 DfD slimr
Raindrop freezing: falling raindrops capture pristine ice crystals to form graupel particles
Raindrop contact freezingRaindrop contact freezing
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
… collection and simultaneous conversion
1E and EDvD4
πDKD,DKwith
t
rρ
t
rρ
t
rρ
)dD(Dn)dD(D)n(D)mD,K(Dt
rρ
)dD(Dn)dD(D)n(D)mD,K(Dt
rρ
cfrcfrr2rrri
iaraga
0 rr0 iiiiiriia
0 ii0 rrrrrrira
CFRCFRCFR
rCFR
iCFR
Graupel growth: other effectsGraupel growth: other effects
takenberategrowthminimumThe must
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Dry growth ( Graupel)(Sum of collection rates)
Graupel with Tsurf < 0°C
Wet growth ( Hail) (Heat balance equation)
liquid film
Shedded drops
Graupel with Tsurf = 0°C
Wet growth and water shedding of the Wet growth and water shedding of the graupels graupels initiation of hailstones initiation of hailstones
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Heat balance equation: Musil (1970), Nelson (1989)
sircg t
m
t
m
t
m
Mass budget:
Wet growth rate of the graupel
vtvs
v
vvtaggtsi,
sirctm e)(Te
TR
P)(T,LT)(T)(Tfπ4T)(Tc
t
m
t
m)(TL
DkC
Heat due tocollected
water
Heat due tocollected
ice
dissipated Heatby the graupel
… water shedding rate is computed raindrops
A simple A simple wayway to initiate hail ? to initiate hail ? (…this (…this rrhh extension is still under test) extension is still under test)
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
A realistic « graupel hail » conversion rate can be parameterized from the theoretical
Dry growth rate of the graupel Wet growth rate of the graupel
WetDry
Dry*
t
rt
r g
hg
h
Graupel tendencybefore conversion
Weighting factorwith Dry/Wet rates
… to be improved to avoid a continuous conversion into hail
Hail rate
Melting of the ice particles (T>0°C)Melting of the ice particles (T>0°C)
Pristine ice crystals: instantaneously melted into cloud droplets
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Graupel particles raindrops: heat budget equation taking into account the fall and the collection capability of the particles
Snow/aggregates graupel raindrops: heat budget equation andconversion into graupel
T=0°C
Pristine crystals
mixed-phase~ graupel
Graupel Snow/Aggregate
Sedimentation of ice particlesSedimentation of ice particles
Same as for the raindrops, the pristine ice crystalsare weakly precipitating (McFarqhar & Heymsfield)
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
0
,,,,,,,,,, Dd)D(n)D(m)D(vSed_flux gsigsigsigsigsi
Sed_fluxt
)r(ρ ,,a
zgsi
SED
Automatic time-splitting for numerical stability
Additional part to include hail explicitly
Microphysical Scheme diagramMicrophysical Scheme diagram
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
WA
RM
mic
roph
ysic
s CND/EVA DEP/SUB
COLD microphysics
Implicit adjustment to saturationImplicit adjustment to saturation
Saturation mixing ratio: using a barycentric formula ‘no’ supersaturation rr
(T)rr(T)rrr *
i*c
isatv,
*i
wsatv,
*ciw
satv,
Cond/Evap+Dep/Subl rates: Variational adjustment
rr
rrΔr
rr
rrΔr3
(T)rrr2
0F(T)T1
r(T)rrr
r(T)Lrr
r(T)L)TT(CF(T)
*i
*c
*i
vi*i
*c
*c
vc
iwsatv,
*vv
*v
iwsatv,*
i*c
*i
vi*i
*c
*c
vw*
ph
andGet
Compute
assuchFind
and andvapor
crystal droplet crystal droplet
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Temporal steppingTemporal stepping
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
• Sedimentation processes• Warm processes• Slow mixed-phase processes (nucleation, auto-
conversion, aggregation, deposition, etc.)• Fast mixed-phase processes (riming, collections,
melting, etc.)
• Saturation ajustment is the last integrated process
Microphysical Scheme of MésoNHMicrophysical Scheme of MésoNH
• Warm processes (Kessler scheme)• Light and Heavy riming rates of the snowflakes by
the cloud droplets and by the rain drops and the conversions into graupel particles
• Wet/Dry growth modes of the graupels• Melted particles are considered as graupels• Possible extension to simulate a « hail » phase
• Sedimentation (1st order upstream scheme)• Processes are integrated one-by-one after carefully
checking the availability of the sinking categories• On-line budgets
Summary
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
• Onset of precipitating drops and precipitating ice: Simulation of extended cloud sheets of moderate lifetime
(Sc, Ci) ?
• Physics of the ice phase: Collection efficiencies ? Supercooling of water ? Ice nucleation ?
Which ice crystal characteristics to choose ?
Present uncertainties …Present uncertainties …
snow graupel
needle
column
plate bullet
dendriterosette
(Pruppacher and Klett, 1997)
10-100 m
1-10 mm
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
W (m/s)
rc (kg/kg)
U (m/s)
rr (kg/kg)
WarmWarm 2D Orographic Cloud 2D Orographic Cloud
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Mixed-phaseMixed-phase 2D Orographic Cloud 2D Orographic CloudW (m/s)
rc + ri (kg/kg)
U (m/s)
rr + rs + rg (kg/kg)
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
2D Tropical Squall Line2D Tropical Squall Line
Cloud water
Rain water
Snow
Graupel
Cloud iceU
W
Dynamics:Flat terrain+R/S
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
How does the flow over complex terrain modify the growth mechanisms of precipitation particles?
3D Orographic precipitation (MAP)3D Orographic precipitation (MAP)
32km : 150x1508km : 145x1452km : 150x150over 51 levels
8 km
2 km
Monte Lema
S Pol
Ronsard
ECMWF32 km
3 Dopplerradars ( )
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
(Keil et Cardinali, 2003)
IOP8 (F<1)
IOP2a(F>1)
(F: Froude number)
3D Orographic precipitation (MAP)3D Orographic precipitation (MAP)
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
IOP2a: 09/17/9918 UTCUnstable Flow over
IOP8: 10/20/9912 UTCStableBlocked flow
SnowGraupel
Hail
Cloud Rain
IceIOP2a
IOP2a ( Strong convection)- Deep system- Large amount of hail and graupel
Mean vertical distribution of the hydrometeors
IOP8 ( Stratiform event)- Shallow system- Large amount of snow
IOP8
Snow
Explained by analysing the dominant microphysical processes
3D Orographic precipitation (MAP)3D Orographic precipitation (MAP)
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
MesoNH with grid_nesting (V. Ducrocq)
Arpège-> MésoNH 10km -> MésoNH 2.5km
IC : Mesoscale surface data reanalysis
BC : Aladin 3h Forecasts
12-22 TU Nîmes radar cumulated rainfall
> 300 mm
« Gard » flash flood (8 Sept. 2002)« Gard » flash flood (8 Sept. 2002)
= 260 mm
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Arome 60s
304 mm
MésoNH 4s12-22 TU Nîmes radar cumulated rainfall
> 300 mm
274 mm
Single model 2.5Km
IC : Mesoscale surface data reanalysis
BC : Aladin 3h Forecasts
« Gard » flash flood (8 Sept. 2002)« Gard » flash flood (8 Sept. 2002)
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
MésoNH verifying toolboxMésoNH verifying toolbox
• Simulation of radar observations (Ze, VDop, ZDR, …):based on the Rayleigh diffusion approximation Z ~ NDe
6
• Simulation of satellite radiances and BTs (VIS, IR, MW): based on highly accurate radiative transfer scheme coupled to Mie/T-matrix diffusion codes or using a fast algorithm such as RTTOV
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Radar reflectivities Radar reflectivities (MAP IOP2a 2 km)(MAP IOP2a 2 km)
Radar obs. (T+11h)MesoNH (hail) MesoNH (hail)
2000m 2000m
6000m 6000m
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Satellite analysis of storms over GermanySatellite analysis of storms over Germany SEVIRI 10.8 µm BT (MSG) AMSUB 183+/- 1 GHz (NOAA15)
MesoNH simulation (Δx=10km) with RTTOV results after 17 hours
Deep cirrus clouds Unresolved convectionConvective event of interestfully resolved at Δx=2.5km
SEVIRI 10.8-12 µm BT (MSG)
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
Satellite analysis of cirrus producedSatellite analysis of cirrus producedby deep convection over Brazilby deep convection over Brazil
SEVIRI 8.7-10.8 µm BTD (MSG) MesoNH simulation (Δx=30 km) with RTTOV results after 21 hours
•1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
ConclusionConclusion
• The simulation of cloud cover and precipitation fields seems promising at high resolution even without a sophisticated initialization procedure.
• The same scheme has also potential applications in chemistry and atmospheric electricity (not shown here).
• Cloud schemes still need to be improved and tuned for the
purpose of operational applications (this should be a new concern of the cloud modeling community!)
• There is a great interest in using routine radar & satellite data to assess the quality of cloud schemes.
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005
PerspectivesPerspectives
• Fractional cloud cover and subgrid scale precipitation. • Interactions between convection schemes (implicit
clouds) and microphysical schemes (explicit clouds) to simulate the lifetime of tropical anvil outflow.
• Scale dependence of microphysical schemes. • Computation of various ground radar and satellite
products for a multispectral and active/passive evaluation of the microphysical scheme.
1st AROME training course, Poiana-Brasov, Romania, 21-25 November 2005