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Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Modellistica numerica per la
circolazione atmosferica e la
dispersione 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
M o d e l l i n g …………….
physical models
(wind tunnel, water flumes)
numerical models
(approximate numerical solutions using
numerical integration techniques)
diagnostic models
(no time-tendency terms)
mathematical models
analytical models
(exact analytical solution in
simplified conditions)
prognostic models
(full time-dependent equations)
METEOROLOGICAL CIRCULATION MODELS
Study of local, regional or global
meteorological phenomena
Meteorological input for air
pollution DISPERSION MODELS
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
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
LONG RANGE
Synoptic and Planetary spatial scale
Time scale from weeks to months-years
ECMWF ANALYSES
driving
LONG RANGE DISPERSION MODELS
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
MILORD
Chernobyl
Method for the Investigation of Long Range Dispersion
Lagrangian Particle Stochastic model
(D. Anfossi, D. Sacchetti, S. Trini Castelli, 1995)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
MILORD
Method for the Investigation of Long Range Dispersion
Lagrangian 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 AND MESOSCALE
Spatial scale from few tens to few hundreds km
Time scale from few hours to few weeks
REGIONAL METEOROLOGICAL MODELS
driving
REGIONAL/LOCAL DISPERSION MODELS
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
METEOROLOGICAL MODEL
Mean Flow
Turbulence
 u j ui   uj ui  p
ui




  gδ13  2εijk Ω j uk 
t
x j
x j
xi
θ
θ 1 
 u j

ρ0 uθ   S θ
t
x j ρ0 x j
etc
….
Closure
u
uθ 
E
E
E
π 
 u j
 uj
 uj ui i  θ0 uj
 g i δi 3
t
x j
x j
x j
xi
θ0
Transport
Diffusion
DISPERSION MODEL
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
INTERFACING METEOROLOGICAL and DISPERSION MODELS
DISPERSION
=
TRANSPORT
(Mean wind)
+
DIFFUSION
(Turbulence)
Turbulence characteristics required by air pollution models
(Eddy diffusivities, wind velocity variances, Lagrangian time scales)
are usually NOT provided directly by meteorological models
BUT must be derived from their output using
wind and temperature fields
and additional fields such as
turbulent kinetic energy,
turbulent length scale,
mixing height,
atmospheric surface layer parameters.
INTERFACING PARAMETERIZATION SCHEME !!!
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
R M S modelling system
Atmospheric
circulation model:
Boundary layer
parameterisation
interfacing code:
Lagrangian particle
dispersion model:
RAMS
MIRS
(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,
SPRAY
Tinarelli et al, 2000, Ferrero et al. 2001)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
RMS modelling system
RAMS
Fields of - WIND, TEMPERATURE, T.K.E., K (3 D)
TOPOGRAPHY, SURFACE FLUXES (2 D)
MIRS
Fields of - WIND, K, SKEWNESS/KURTOSIS,  & TL (3 D)
TOPOGRAPHY, PBL height (2 D)
SPRAY
Fields of - PARTICLE POSITIONS
G. L. CONCENTRATION
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
MIRS
from RAMS fluxes to…
Surface layer
parameters
from Louis (1979)
parameterisation
u* 
- u' w'  - v' w'
2
2
*  -
' w'
u*
 u*2
L
gk*
z

a
u   U F  , Ri 
R
z

 z

u 2  a 2U 2 F m  , Ri 
*
 z0 B 
2
*
h
*
B
0
13
 z 
w  u   i 
 L 
Convective velocity scale
Gradient Richardson number profile
Diffusion coefficient profile
PBL height
Turbulent kinetic energy profile
External datasets
Gryning and Batchvarova (1990) simplified - Batchvarova and Gryning (1991) complete model
Variances and
decorrelation
time scales
2
2
1  1  2  v   u
 
q
 2 
 w2   1q 2
Coupling with
Mellor-Yamada scheme
2
u
Coupling with E-l
or E- schemes
 ui2  2 K mi
Hanna (1982) and Degrazia et al. (2000)
parameterizations
Third and fourth moment of the vertical velocity
TLui 
K mi
 ui2
u 2
 E
xi 3
TLui
2 ui2

C0ui
Chiba (1978), Anfossi(1997)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
SPRAY
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
du(t )  a( x, u) dt  b( x, u) dW t 
x = particle position; u = particle velocity fluctuation;
U = mean wind velocity; dW = stochastic fluctuation
ai ( x, u) dt deterministic term

with
 
dx  U  u t dt
dW t   0 ; dW 2 t   dt
bij (x,u)dWi (t ) stochastic term
dW j incremental Wiener process
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Examples of R M S applications
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
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
RMS
RibeirãoPires
R.G.daSerra
TOP
TRACT
BOT
In collaboration with Dr. J Carvalho (ULBRA) ( km)
Cubatão
SãoVicente
Brazil
Santos
In collaboration with Dr. A. Kerr (USP)
SW
( km)
The modelling system RMS: RAMS-MIRS-SPRAY
SE
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
RMS
Courtesy of
Running on the highway!
In collaboration with
Drs. G Brusasca, G. Tinarelli,
S. Finardi
The modelling system RMS: RAMS-MIRS-SPRAY
EPA – RUSVAL wind tunnel experiment
RAMS sensitivity to turbulence closure
RMS
Observed data speed (ms-1)
E-l simulation speed (ms-1)
MY82 simulation speed (ms-1)
Observed data u (ms-1)
E-l simulation u (ms-1)
MY82 simulation u (ms-1)
RMS
EPA-RUSVAL: closure scheme effect on dispersion
(1)
u2  (1  2  )q 2
v2

 2w
 q
(2)
2
MY82 closure + (1) + (3)
u2i  2 K mi
u 2
 E
xi 3
(3)
TLui 
K mi
u2i
E-l closure + (2) + (3)
Scatter plots of the RMS simulated concentrations against measurements
EPA-RUSVAL: concentration distribution
Cu  hc2
χ
Q
RMS
C is the concentration corrected subtracting the background,
Q is the tracer flow rate
hc is a convenient length scale of the experiment
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
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)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
RMS
TRACT
is back!
El-ISO
El-SMA
MY
MY-Hanna
Testing the effect of alternative turbulence closures also on TRACT
(in collaboration also with CESI, Dr. Alessandrini)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
RMS
Regional down to
local scale
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
Wind velocity at 10 m
RMS
Wind velocity at 150 m
Low wind case, September 1999
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
RMS
Foehn case,
February 2000
start: 09.02.2000 11 GMT (12 LST)
end: 10.02.2000 15 GMT (16 LST)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
SPEED (m/s)
30.06.2000 12:00
TEMPERATURE (K)
Istituto di Scienze dell’Atmosfera e del Clima - Torino
Consiglio Nazionale delle Ricerche
SPEED (m/s)
30.06.2000 18:00
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
NUMBERS AND NUMERICS!
RAMS  parallel versions 5.0, 6.0 :
parallel efficiency 68% — 90 % (Tremback C., personal communication)
n. of processors
computer hardware
model 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-TO
AlphaServer DS20E Tru64 Unix
microprocessor 21264 - 833MHz CPU (2!)
‘Parallel’ present at DFG-UNITO
(Prof. G. Boffetta)
3 Server TYAN GX28 2GB RAM
CPU AMD Opteron 244 (2 x 3 = 6)
Networking Gb Ethernet
‘Parallel’ next future at
ISAC-TO + DFG-UNITO
5 Server TYAN GX28 2GB RAM
CPU 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
Mellor-Yamada 1982
dE 
E
 KE
Pε
dt z
z
In RAMS
Level 2.5: B.L approximation,
horizontal homogeneity
 u  2  v  2  g

P  K m        K h
z
 z   z   0
K m  S ml (2E)
1
K h  S hl (2E)
2
l1, 1, l2 ,  2   l  A1, B1, A2 , B2 
l
 u u 

Sm , Sh , S E  f  , , , E, l , A1, A2 , B1 , B2 , C 
 z z z

kz
1 + kz
1
2E 

3
2
1
K E  S E l (2E)
2
l  0.1
l
1
2
 z Edz
 Edz
(A1,A2,B1,B2,C)=(0.92, 16.6, 0.74, 10.1, 0.08)
From MIRS to SPRAY
u2  (1  2  )q 2
w3  0.6
v2  q 2
 2w  q 2
TLi 
*3
w z
 0.1 w3
k L
From Chiba (1978)
Km
i2
q 2  2E
1
A
  2 1
3
B1
Istituto di Scienze dell’Atmosfera e del Clima - Torino
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E- l isotropic
dE

E

KE
Pε
dt x j
x j
In RAMS
P  uiu j
ui
x j
c E 3 / 2

l
 δi,3 gαuiθ 
 u
u j  2

  Eδ
i
uiuj   K m 

 x j xi  3 ij


l
kz
1 + kz
θ
uθ    K h
i
x
i
l  0.1
l
K m  c μ E 1 / 2l
 z Edz
 Edz
K h  αh K m
From MIRS to SPRAY
 u2  2 K m
i
 ui 2
 E
xi 3
K E  αE K m
TLu i 
Km
 u2
i
3
w* z
w  0.6
 0.1 w3
k L
3
(K-theory)
From Chiba (1978)