P1253814552qkUpY

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Modelli Lagrangiani
D. Anfossi , S. Trini Castelli, G. Belfiore
Istituto di Scienze dellAtmosfera e del Clima
C.N.R. - Torino
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1. Studio e applicazione delle tecnologie
consolidate a scenari reali, corrispondenti a
periodi recenti e confronto dei dati modellistici
con le misure effettuate dall'Agenzia Regionali
per l'Ambiente. (ALPNAP)
2. Studio di nuovi approcci modellistici relativi
alla chiusura delle equazioni fondamentali
momenti di ordine elevato nei modelli Euleriano e
sperimentazione di nuovi approcci di tipo
Lagrangiano (MSS).
3. Analisi dell'influenza dell'input
meteorologico dei modelli di dispersione tramite
l'interfacciamento di codici meteorologici
prognostici (HARMO12).
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MICRO SWIFT SPRAY MSS
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Model system MSS
MicroSpray
MicroSwift
prognostic (mass consistent) wind interpolator
over complex terrain accounting for complex
terrain and buildings
LPD model derived from SPRAY it accounts for
the presence of buildings, other obstacles,
complex terrain, and possible occurrence of low
wind speed
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M S S
allows taking into account
negatively, positively or neutral emissions in
presence of obstacles
any kind of source configuration, with emission
in any direction and any initial velocity
dispersion of dense and/or light gas, accidental
releases and possible terrorist attacks in urban
areas.
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from Venetsanos et al. (2003), Journal of
Hazardous Materials Accidental release of
hydrogen in Stockholm
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plume phase
gravity spreading
normal dispersion as passive scalar
gravity spreading
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Equations
five unknowns rp, up, vp, wp, b
where
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entrainment velocity
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Plume spread at ground
When a dense plume reaches the ground an
horizontal momentum is generated by the weight of
the plume itself that tends to spread the
plume This heavy gas induced outflow velocity
depends on the bulk properties of the dense
plume, i.e. how density varies in 3 D and
requires all the particles position to be
accounted for
Thus the movement of each particle depends on the
characteristics of the ensemble A hybrid
algorithm used
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To each particle is assigned an horizontal speed
where g is randomly picked from a uniform
0-360 distribution g is chosen at emission
time and kept by the particle
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How to compute Hbulk and rbulk ?
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Trial 8 Instantaneous Release Downrange
Courtesy of Dr. Jim McQuaid
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Trial 8 Instantaneous Release Overhead
Courtesy of Dr. Jim McQuaid
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SPRAY
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Chlorine accident - Macdona, TX, USA
June 28, 2004 two trains collision
Picture from Railroad Accident Report
NTSB/RAR-06/03
HARMO12-2008
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MSS results compared to the Macdona accident
simulations from six widely-used models (Hanna,
2007). Continuous lines indicate present
results, vertical bars show the variability (max,
min) of the six models, circles locate their
median
Left graph refers to the Cl2 concentration versus
distance right graph plots Cl2 cloud width and
height, both to the model-simulated concentration
of 20 ppm versus distance
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MERCURE - MSS

• Building
• Xo 50 m from release
• H 47 m, Lx 23.3 m, Ly 26.2 m

initial momentum directed vertically, w 1.14
m/s emission height
10 m initial density ratio (plume/air)
2.0 initial emission diameter 2.17
m gas emission rate 10 kgs-1 neutral
stratification, logarithmic wind profile
low wind at 10 m 1.5 m/s higher wind at
10 m 5 m/s
2 flow regimes
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MERCURE
Wind(z10m) 1.5 m/s Iso-surface 0.01 kg/kg
MSS
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MERCURE
Wind(z10m) 5.0 m/s Iso-surface 0.01 kg/kg
MSS
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MERCURE
Wind(z10m) 5.0 m/s Vertical cut
MSS
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MicroSpray dense-gas model evaluation
(TIsland-Ist TIsland-Cont BurroCoyote)
SCIPUFF dense-gas model evaluation
Paris 16th October 2007
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Plot plan of the Kit Fox site

• consists of 52 trials
• with CO2 gas releases, puffs continuous plumes 
• samplers (one reading per second) were installed
  at four arcs
• 25, 50, 100, and 225 m
• 8 masts with wind speed measurements

HARMO12-2008
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Validation of MSS against Kit Fox field data

• Statistical evaluation of comparisons between
  observations and predicted data includes
  geometric mean bias (MG),
• geometric variance (VG)
• factor of 2 (FA2)

Kit Fox experiment Overall URA Continuous URAPuff ERP Continuous ERPPuff
Kit Fox experiment 52 experiments 12 experiments 21 experiments 6 experiments 13 experiments
MG 1.04 1.42 0.95 1.19 0.87
VG 1.20 1.20 1.15 1.25 1.29
FA2 88 92 99 83 83
HARMO12-2008
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CONCLUSIONS
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Downscaling from mesoscale to local scale
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RAMS-MIRS configuration
Example of a typical configuration for a
simulation of the meteo fields using the
prognostic code RAMS up to 1 km resolution, 4
nested domains
grid 1 64 km horizontal resolution grid 2
16 km horizontal resolution grid 3 4 km
horizontal resolution grid 4 1 km
horizontal resolution Vertical grid vertical
stretched layers, 0 15/20000 m, first layer 50
m depth (first level at 25 m) RAMS is
initialised with the ECMWF (0.5o lat/lon)
analysis fields. Nudging at the lateral
boundaries of the outer grid every 6 hours.
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Downscaling from RMS to MINERVE mass consistent
model
Regional scale
Simulation of the meteo fields using the
diagnostic code MINERVE up to 100 m resolution,
in subdomains typically 10-20 km x 10-20 km size
Local scale

• MINERVE gets as input the hourly RAMS 3D gridded
  dynamical and thermal fields and
• - interpolates the mean input fields on its 3D
  computational domain
• performs and objective analysis application of
  mass conservation in every domain cell

• Advantages of RAMS?MINERVE downscaling
• possibility of including local measurements
• possibility of including more detailed topograhy
  data

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An example of how RAMS_MIRS MINERVE works for
wind field in complex terrain from ALPNAP Alpine
Space Project
MINERVE
RAMS
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What is this work about
For its nature, MINERVE is not designed to
account for the prognostic turbulence fields, and
the Lagrangian turbulent variables are thus
calculated in SPRAY from parameterisations
defined for flat terrain (ex. Hanna, 1982). In
this work we investigate whether a proper
interpolation from the coarser-resolution
prognostic 3D-gridded turbulence fields, like
diffusion coefficients, turbulent kinetic energy
and its dissipation, might be used in complex and
inhomogeneous terrain. In this way, the
shortcoming of using parameterised turbulent
fields might be overcome by coupling MINERVE with
a module, which calculates the turbulence fields
on the high-resolution diagnostic grid by
interpolating from the coarser prognostic grid.
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What we compare here
RAMS is run with four nested grids, where the
third (G3) and the fourth (G4) grids have
respectively 1 km and 250 m resolution. RAMS
fields on G4 at 250 m are considered the truth
versus which to test other two combinations.
The G3 turbulence fields from the 1-km grid are
bilinearly interpolated on the 250-m mesh points,
originating the turbulence dataset G3_INTP to be
checked as an alternative to flat-terrain
parameterisations. A downscaling of the mean
flow to 250 m with MINERVE, using in input the
1-km resolution grid RAMS G3 fields, is done.
MINERVE wind fields at 250 m are then used to
calculate the surface layer and boundary layer
parameters entering the turbulence calculation in
the standard configuration, that is applying the
Hanna (1982) parameterisation We consider three
different turbulence closure schemes in RAMS
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The turbulence closures used in RAMS_MIRS
The MY 2.5 scheme (as in RAMS)
Vertical diffusion coefficients from the TKE
equation in boundary layer approximation
with
Horizontal diffusion coefficients from the
deformation scheme as in El-anis
with
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The turbulence closures used in RAMS_MIRS
The EL_(iso)anis scheme
Vertical diffusion coefficients from the 3D TKE
(E) equation
with
Horizontal diffusion coefficients from a
deformation scheme
with
?0 air density, Cx dimensionless coefficient, ?x
grid spacing S2 horizontal strain rate, KA
user-specified coefficient of order 1.
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The case considered
North-West Italian Alpine region around Torino
Altitudes G4 970 m G3, 4 points NW
772 m NE 598 m SE 780 m SW 939 m
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Distributions of TKE for G3_INTP and G4 values
(h lt 1450 m)
MY2.5 EL_iso
EL_anis
Dashed blue values interpolated from Grid
3 Solid orange values calculated on Grid 4
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A critical case in complex terrain, TKE
15 GMT
MY 2.5 EL-anis
Red RAMS G3_intp Blue RAMS G4 Green (RAMS G3
mean flow ?) MINERVEHanna
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Conclusions on the turbulence closure analisys
Interpolated values of TKE from 1 km resolution
grid (G3_INTP) result to be overall
representative of the TKE values simulated on a
250 m grid (G4). The spread between the two
sets of TKE values, G3_INTP and G4 are probably
mainly due to the fact that the G3 points, on
which the interpolation procedure is applied, may
be characterized by even significantly different
altitudes The methodology seems to be feasible,
also in complex terrain and in critical locations
and asks for further investigation.
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Grazie per la vostra attenzione