Modellistica numerica per la circolazione atmosferica e la dispersione di inquinanti S. Trini...

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

http://www.cnr.it/



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 …………….

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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

http://www.cnr.it/

LONG RANGE

Synoptic and Planetary spatial scale

Time scale from weeks to months-years

ECMWF ANALYSES

LONG RANGE DISPERSION MODELS



driving

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http://www.clipart.com/en/close-up?o=762556&memlevel=C&a=c&q=astronaut&s=31&e=60&show=&c=&cid=&findincat=&g=&cc=&page=2

MILORD

ChernobylChernobyl



Method for the Investigation of Long Range DispersionLagrangian Particle Stochastic model

(D. Anfossi, D. Sacchetti, S. Trini Castelli, 1995)

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MILORD



Method for the Investigation of Long Range DispersionLagrangian Particle Stochastic model

(D. Anfossi, D. Sacchetti, S. Trini Castelli, 1995)

http://java.ei.jrc.it/atmes2/1.htm

http://www.cnr.it/



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

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http://www.clipart.com/en/close-up?o=885584&memlevel=C&a=c&q=drive&s=1&e=30&show=&c=&cid=&findincat=&g=&cc=&page=1

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



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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

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Boundary layerparameterisation MIRSinterfacing code:

R M S modelling system

Atmosphericcirculation model: RAMS

Lagrangian particledispersion model: SPRAY



(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)

http://www.cnr.it/

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



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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)



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

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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

http://www.cnr.it/

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



R M S

In collaboration with Dr. A. Kerr (USP)

In collaboration with Dr. J Carvalho (ULBRA)

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R M S

Running on the highway!



Courtesy of

The modelling system RMS: RAMS-MIRS-SPRAY

In collaboration with Drs. G Brusasca, G. Tinarelli, S. Finardi

http://www.cnr.it/

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



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



R M S

Regional down to

local scale

R M S



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Low wind case, September 1999

Wind velocity at 10 m Wind velocity at 150 m



R M S

http://www.cnr.it/

start: 09.02.2000 11 GMT (12 LST) end: 10.02.2000 15 GMT (16 LST)

R M S



Foehn case, February 2000

http://www.cnr.it/

30.06.2000 12:00

SPEED (m/s) TEMPERATURE (K)



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30.06.2000 18:00

SPEED (m/s) TEMPERATURE (K)



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Comparison with observations: time evolution of wind speed and temperature at the surface



http://www.cnr.it/

31/5/2001 00:00 - 1/6/2001 00:00 (Sicily coast)3-D particles and g.l. concentrations – hourly imagines

Courtesy of



http://www.cnr.it/



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.)

http://www.cnr.it/



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

http://www.cnr.it/



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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



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

Modellistica numerica per la circolazione atmosferica e la dispersione di inquinanti S. Trini...

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