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Modellistica numerica per la Modellistica numerica per la
circolazione atmosferica e la circolazione atmosferica e la
dispersione di inquinantidispersione di inquinanti
S. Trini Castelli
& D. Anfossi (ISAC – CNR) & E. Ferrero (DISTA – UNIPMN)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
METEOROLOGICAL CIRCULATION MODELS
Study of local, regional or global
meteorological phenomena Meteorological input for air pollution DISPERSION MODELS
physical models(wind tunnel, water flumes)
mathematical models
analytical models(exact analytical solution in simplified conditions)
numerical models(approximate numerical solutions using
numerical integration techniques)
diagnostic models(no time-tendency terms)
prognostic models(full time-dependent equations)
M o d e l l i n g …………….
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Purposes and applications
meteorological model:
description and forecast of atmospheric processes and circulation on different scales (synoptic, mesoscale, local)
dispersion model:
analysis and forecast of continuous (Industrial plants or areas) and accidental releases (e.g. Chernobyl (long range), Seveso (short range))
environmental impact evaluation “real time” monitoring air concentration and ground deposition estimation measurement nets planning strategies processing for emissions downing
LONG RANGE
Synoptic and Planetary spatial scale
Time scale from weeks to months-years
ECMWF ANALYSES
LONG RANGE DISPERSION MODELS
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
driving
MILORD
ChernobylChernobyl
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Method for the Investigation of Long Range DispersionLagrangian Particle Stochastic model
(D. Anfossi, D. Sacchetti, S. Trini Castelli, 1995)
MILORD
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Method for the Investigation of Long Range DispersionLagrangian Particle Stochastic model
(D. Anfossi, D. Sacchetti, S. Trini Castelli, 1995)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
REGIONAL/LOCAL DISPERSION MODELS
REGIONAL AND MESOSCALE
Spatial scale from few tens to few hundreds km
Time scale from few hours to few weeks
REGIONAL METEOROLOGICAL MODELS
driving
METEOROLOGICAL MODEL
DISPERSION MODEL
kjijk
ij
ij
j
iji uΩεgδx
p
x
uu
x
uu
t
u213
θjj
j Sθuρxρx
θu
t
θ
00
1
30
0 ii
ij
j
iij
jj
jj δ
θ
θug
x
πuθ
x
uuu
x
Eu
x
Eu
t
E
etc….
Mean Flow Turbulence
Transport Diffusion
Closure
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Turbulence characteristics required by air pollution models(Eddy diffusivities, wind velocity variances, Lagrangian time scales)
are usually NOT provided directly by meteorological modelsBUT must be derived from their output using
wind and temperature fieldsand additional fields such as
turbulent kinetic energy,turbulent length scale,
mixing height,atmospheric surface layer parameters.
DISPERSION = TRANSPORT + DIFFUSION
(Mean wind) (Turbulence)
INTERFACING PARAMETERIZATION SCHEME !!!
INTERFACING METEOROLOGICAL and DISPERSION MODELS
Boundary layerparameterisation MIRSinterfacing code:
R M S modelling system
Atmosphericcirculation model: RAMS
Lagrangian particledispersion model: SPRAY
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
(Regional Atmospheric Modeling System
Pielke et al., 1992)
(Method for Interfacing RAMS and SPRAY
Trini Castelli and Anfossi, 1997, Trini Castelli, 2000)
(Brusasca et al., 1989, Anfossi et al., 1998,
Tinarelli et al, 2000, Ferrero et al. 2001)
RMS modelling system
RAMS
MIRS
SPRAY
Fields of - WIND, TEMPERATURE, T.K.E., K (3 D)
TOPOGRAPHY, SURFACE FLUXES (2 D)
Fields of - WIND, K, SKEWNESS/KURTOSIS, & TL (3 D)
TOPOGRAPHY, PBL height (2 D)
Fields of - PARTICLE POSITIONS
G. L. CONCENTRATION
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle RicercheMIRS
Surface layer parameters
22
*''-''- wvwuu
** u
'w'-
*
*
gk
uL
2
from RAMS fluxes to…
from Louis (1979)parameterisation
BRi,
z0
zFmU2a2
2*u
B
0
h
2
** Ri,z
UR z
Fa
u
PBL height
Gryning and Batchvarova (1990) simplified - Batchvarova and Gryning (1991) complete model
Gradient Richardson number profile
Diffusion coefficient profile Turbulent kinetic energy profile External datasets
31
L
zuw i
Convective velocity scale
Variances and decorrelation time scales
212
2
1qu
22uv
2
1
2 qw
Coupling with Mellor-Yamada scheme
2ui
miLui
KT
Coupling with E-l or E- schemes E
x
uK
imiui 3
222
ui
uiLui C
T0
22
Hanna (1982) and Degrazia et al. (2000)parameterizations
Third and fourth moment of the vertical velocity Chiba (1978), Anfossi(1997)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Lagrangian particle models are three-dimensional models for the simulation of airborne pollutant dispersion, able to account for flow and turbulence space-time variations
Emissions in the atmosphere are simulated using a certain number of fictitious particles named ”computer particle”.
Each particle represents a specified pollutant mass.
It is assumed that particles passively follow the turbulent motion of air masses in which they are, thus it is possible to reconstruct the emitted mass concentration from their space distribution at a particular time
In these models the temporal evolution of the velocity particles released in the atmosphere, that is in turbulent conditions, is prescribed by the Langevin equation, where velocity fluctuations are considered a Markov stochastical process
tdWuxbdtuxatdu ),(),()( dttuUdx with
x = particle position; u = particle velocity fluctuation; = mean wind velocity; dW = stochastic fluctuationU dttdWtdW 2;0
SPRAY
deterministic term stochastic term incremental Wiener process dtai
)( ux, )()( tdWbiij
ux,j
dW
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
D Karlsruhe flat neut/unst
USA Idaho falls flat low wind
USA EPA-RUSVAL hill (wind tunnel) neutral
USA EPA-RUSVAL valley (wind tunnel) neutral
USA Indianapolis urban all stabilities
CH TRANSALP alpine region unstable
N Lillestrom flat – snow covered stable
DK Copenhagen flat coast unstable
D TRACT complex all stabilities
I Vado Ligure complex coast all stabilities
F Marseille complex coast all stabilities
I Turin urban/complex all stabilities
BR Cubatão very complex coast all stabilities
J Tsukuba and Ohi complex coast all stabilities
I Brenner Highway alpine region all stabilities
I-F Torino-Lione Highway alpine region all stabilities
Examples of R M S applications
The modelling system RMS: RAMS-MIRS-SPRAY
TRACTTRACT
BrazilBrazil
Ribeirão P ires
R.G. da Serra
Cubatão
São Vicente Santos
B O T
T O P
(k m )
(k m )S W S E
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
R M S
In collaboration with Dr. A. Kerr (USP)
In collaboration with Dr. J Carvalho (ULBRA)
R M S
Running on the highway!
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Courtesy of
The modelling system RMS: RAMS-MIRS-SPRAY
In collaboration with Drs. G Brusasca, G. Tinarelli, S. Finardi
Observed data speed (ms-1)
Observed data u (ms-1)
E-l simulation speed (ms-1)
E-l simulation u (ms-1)
MY82 simulation speed (ms-1)
MY82 simulation u (ms-1)
EPA – RUSVAL wind tunnel experimentRAMS sensitivity to turbulence closure R M S
MY82 closure + (1) + (3)
Ex
uK
imiui 3
222
(2) (3)
E-l closure + (2) + (3)
22 )21( qu 222 qwv
(1)2i
i
i
u
mLu
KT
Scatter plots of the RMS simulated concentrations against measurements
EPA-RUSVAL: closure scheme effect on dispersion R M S
Cumulative frequency distribution (c.f.d.) of normalized mean concentrations χ. Observed data: solid line; RMS with E-l closure: dotted line; RMS with MY82 closure: dashed line
Q
hCuχ c
2
C is the concentration corrected subtracting the background, Q is the tracer flow rate hc is a convenient length scale of the experiment
EPA-RUSVAL: concentration distribution R M S
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
OHI (Japan) nuclear plant site.Testing the effect of alternative turbulence closures(in collaboration with MHI Fluid Dynamics Lab., Dr. Ohba, Dr. Hara)
Testing the effect of alternative turbulence closures also on TRACT(in collaboration also with CESI, Dr. Alessandrini)
TRACTTRACTis back!is back!
El-ISO El-SMA MY MY-Hanna
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
R M S
Regional down to
local scale
R M S
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Low wind case, September 1999
Wind velocity at 10 m Wind velocity at 150 m
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
R M S
start: 09.02.2000 11 GMT (12 LST) end: 10.02.2000 15 GMT (16 LST)
R M S
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Foehn case, February 2000
30.06.2000 12:00
SPEED (m/s) TEMPERATURE (K)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
30.06.2000 18:00
SPEED (m/s) TEMPERATURE (K)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Comparison with observations: time evolution of wind speed and temperature at the surface
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
31/5/2001 00:00 - 1/6/2001 00:00 (Sicily coast)3-D particles and g.l. concentrations – hourly imagines
Courtesy of
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
NUMBERS AND NUMERICS!
RAMS parallel versions 5.0, 6.0 : parallel efficiency 68% — 90 % (Tremback C., personal communication)
n. of processorscomputer hardwaremodel configuration
SPRAY versions 3.! : parallelization in process at AriaNet(Brusasca G., Tinarelli G., Finardi S., Morselli M.G.)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
NUMERICS AND COMPUTERS!
Past to present at ISAC-TOAlphaServer DS20E Tru64 Unixmicroprocessor 21264 - 833MHz CPU (2!)
‘Parallel’ present at DFG-UNITO(Prof. G. Boffetta)
3 Server TYAN GX28 2GB RAMCPU AMD Opteron 244 (2 x 3 = 6)Networking Gb Ethernet
‘Parallel’ next future atISAC-TO + DFG-UNITO
5 Server TYAN GX28 2GB RAMCPU AMD Opteron 244 (2 x 5 = 10)Networking Myrinet 2000 Fiber
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
21
E)2(lSK mm 21
E)2(lSK hh 21
E)2(lSK EE
z
g
z
v
z
u
0
22
hm KKP
1
23
2E
εPz
EK
zdt
dEE
In RAMS
l
kk
lz+1
z
dzE
dzEz1.0l
(A1,A2,B1,B2,C)=(0.92, 16.6, 0.74, 10.1, 0.08)
22112211 ,,,,,, BABAlll
CBBAAlEzz
u
z
ufSSS Ehm ,,,,,,,,,,, 2121
22 )21( qu 22 qv 22 qw Eq 22
1
123
1
B
A
2i
mLi
KT
From MIRS to SPRAY
3*
3 1.06.0
3
wL
z
k
ww From Chiba (1978)
Level 2.5: B.L approximation, horizontal homogeneity
Mellor-Yamada 1982
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
E- l isotropic
In RAMS
l
Ec 2/3
εPx
EK
xdt
dE
jE
j
θiugαi,δjxiu
juiuP
3
ijEδixju
jxiu
mKjuiu 3
2
ix
θKθiu h
(K-theory)
l
kk
lz+1
z
lEcK /
μm21
mhh KαK mEE KαK
From MIRS to SPRAYE
x
uK
i
imui 3
222
2
i
i
u
mLu
KT
3*
3 1.06.0
3
wL
z
k
ww From Chiba (1978)
dzE
dzEz1.0l