Download A Schönbein-módszerrel mért budapesti történeti ózon adatok

Sensitivity study of a coupled dispersion - weather
prediction model with case studies
1Gyongyosi
1Weidinger
2Ivanyi
A.Z.
(1), T.
(1), Zs.
(2)
1Department of Meteorology, Eötvös Loránd University, 1117 Budapest Pázmány P. s. 1/A,
2Regional Environmental Center for Central and Eastern Europe
www.rec.org
Introduction
2.2 Operational products yielded by the Eta model
he implementation of the NIRE mesoscale circulation atmospheric
dispersion model was carried out in year 2000 at the Eotvos University,
Budapest, Hungary. The model dynamics has been tested for artificial initial
and boundary conditions. Some necessary changes have been introduced
into the model code for the sake of numerical stability. The dispersion
model has been coupled to a numerical weather prediction model
(NCEP/ETA). The concentration of a contaminant has been calculated with
the coupled modeling system. A case study for a recent situation in the
Carpathian Basin is presented below.
The
main
characteristics
of
the
NIRE
On a daily basis meteorological fields are posted on the World Wide Web for
meteorological application. Predicted distribution of surface temperature, surface
wind and mass field, Lifted Condensation Level (LCL), vertical temperature
gradient of the convective boundary layer, integrated sensible heat flux and Kindex of instability are displayed on forecast maps. Diagrams representing time
variation of selected variables in some selected locations (Budapest,
Nyíregyháza, Szeged, Győr, Pécs, are also presented. An example for the
above mentioned fields and diagrams are given on Fig. 4 and 5 below.
model
The NIRE mesoscale circulation model was developed at the National
Institute for Resource and Environment (NIRE, Tsukuba, Japan) between
1989 and 1999 for the calculation of the dispersion of atmospheric
contaminants. The mesoscale circulation model solves the hydrostatic
primitive equations with the anelastic Boussinesq-approximation. The fields
are discretized on a terrain following staggered variable resolution vertical
coordinate and an Arakawa C grid in the horizontal plane. A first order
turbulence closure is assumed, the vertical turbulent diffusivity is assumed
to be a function of the Richardson number. Vertical diffusion is solved with
an implicit scheme (trapezoidal method). Horizontal diffusion is introduced
only for the sake of numerical stability. In the surface layer, the MoninObukhov similarity theory is considered. The surface fluxes are calculated
with the assumption of the surface energy balance equation. In the soil layer
thermal conductivity equation is considered.
1.1
Boundary
conditioning
and
numerical
Figure 4. Derivative model fields
plotted on the World Wide Web
– vertical temperature gradient
of the convective boundary
layer
Figure 1. The elevation of the surface in the model domain of the standard
run model integration (Carpathian Basin). Model boundary coincides with
steep slopes and elevated terrain resulting in high numerical instability.
At the 2 February 2006 a cold surface inversion layer
was present in the Carpathian Basin (inversion
case). This cold inversion was broken up by a severe
cold front, and after the passing of the cold front,
neutral conditions were present in the PBL with an
unstable surface layer in the daytime (convection
case). Temperature and dew point soundings at 12Z
on the 2 February and the 6 February are plotted on
Fig. 8. Temperature profiles for the inversion and
convection cases were evaluated and they showed
good agreement with rawinsonde measurements.
The effect of a single large stack was investigated in
the middle of the model domain with the following
emission data: 200 m high elevated point source.
Intensity: 280 m3/s @ 3000C total emission of 1000
t/day CO2. Typical CO2 profiles in the vicinity of the
source in inversion and convection condition are
plotted on Fig. 9, respectively. In case of inversion we
got extremely high concentration near surface even
in the daytime while in case of convection in the
daytime we got much cleaner air. These results are in
a good agreement with what we would expect.
Figure 8. Temperature and dewpoint soundings at the
inversion (up) and convection (down).
integration
At the lateral boundary a flow relaxation zone is introduced: an artificial
function insures the smooth transition into the prescribed boundary values.
At the top, a sponge layer is considered: an artificially enlarged diffusivity is
assumed in the vicinity of the upper boundary.
Model initialization is performed with dynamic initialization. Integration of the
model is started well before the considered time, so model variables can
spin-up according to the internal model dynamics. Time integration is
performed with a Leap-frog scheme, an Euler forward step is introduced
each 20th step to adjust the numerical mode to the physical mode.
Figure 5. Diagram representing time variation of derivative model output at a certain
location – K-index in Pécs
Figure 2. Mixing layer depth by different cloud amounts (0 and 8 oktas)
1.2 Surface parameterization of sources of contaminants
2.3 Wind energy research and prediction with the Eta model
Water vapor and carbon dioxide are considered. H2O is treated as a passive
scalar, at surface saturation conditions are considered. The calculation of vapor
is relevant only in the energy balance equation (latent heat). No clouds and
condensation are calculated: cloud amount is an input variable for the model
calculation.
For CO2, vegetation is a sink or source depending on the relative balance of the
respiration and photosynthesis. CO2 emission or uptake are calculated for each
vegetation mosaics and numerical value of surface flux is synthesized for each
grid point. Anthropogenic sources are also assumed. Area sources are heating
and traffic in large urban area. Point sources are contribution of large stacks.
The plume rise of large stacks is calculated with the concawe equation.
10 minute wind measurements made on a wind power turbine in Western
Hungary (Mosonmagyaróvár) have been compared to model estimates made
with the Eta model showing good agreement promising the opportunity of
model prediction of available wind energy (Fig. 6).
Figure 3. Time evolution of average CO2 concentration in the model domain
The
Meteorological
driver
for
the
dispersion
model
1.3 Implementation of the NIRE model
1.4 Sensitivity studies of the NIRE model
In the evaluation of the model, Planetary Boundary Layer (PBL) characteristics
were studied. The time variation of mixing layer depth and potential temperature
were investigated. On Fig. 2 vertical cross sections of potential temperature is
plotted versus time by different cloud amounts to demonstrate the sensitivity of
boundary layer to input parameters. The average concentration of CO2 is also
plotted for a 48 hours period. It shows a clear diurnal variation with daytime
lows and nocturnal -- early morning peaks (Fig. 3).
The NCEP/ETA model has been developed by the EMS and the NWS/NOAA. It is a
limited area model for numerical weather prediction. It solves the hydrostatic primitive
equations, though a non-hydrostatic option is available. It uses a modified terrain
following vertical coordinate system, (the eta coordinate) that is a modified sigma vertical
independent variable which guarantee approximately horizontal surfaces near steep
terrain too, separating lee flow near topography. The model uses state of the art
surface and PBL parameterizations.
2.1 Adaptation of the ETA model
The NCEP/ETA model is being run at the Department of Meteorology at Eotvos University
since May 2005. Initial and boundary conditions are downloaded from NCEP every morning
and a 48 hours lead time forecast is performed daily. Resulting output forecast data fields are
stored for research and development purposes. The output fields of the ETA are the input
initial and boundary conditions for the NIRE dispersion model. Prior to the daily integration of
the model for the Central European region some dynamical tests has been performed. The
non-hydrostatic (NH) option of the model has also been evaluated. The model equations in the
NH option are hydrostatic, and non-hydrostatic effects are to be parameterized. Though small
scale effects are more non-hydrostatic, the inclusion of the NH option has minor impact on the
solution itself. In the mass filed order of -5 relative NH departure was to be observed. In the
wind filed NH departure was significant only in the vicinity of orography. For this reason the NH
option in the operational run is not implemented. As input data of the model are the output of
the GFS global model -- which contains already initialized fields -- digital filter initialization was
not used.
Windspeed [m/s]
65 m meas
2.
1 km resolution surface land cover and land use parameters are taken from the
IGBP database from the USGS. Elevation data is generated using the SRTM
satellite data. A sensitivity test of the model dynamics proved strong non-linear
interaction with topography at lateral boundary. As the model domain is located
in the Carpathian Basin where elevated and steep terrain coincides with lateral
boundary (see Fig. 1.), some changes in the boundary conditioning were
necessary for the sake of numerical stability. However, by super-adiabatic
stratification and extremely strong wind conditions the model performed poorly,
and numerical instability arose.
Measured and modelled windspeed at 115 m
Measured and modelled windspeed at 65 m
9
8
7
6
5
4
3
2
1
0
0:00
115 m meas
ETA65
115 m ETA
12
10
Windspeed [m/s]
1.
4. A case study
3:00
6:00
9:00
12:00
15:00
18:00
21:00
LT
0:00
8
6
4
2
0
0:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
LT
0:00
Figure 6. Measured and calculated wind speed at 65
m and 115 m calculation made with the NCEP/ETA
model 48 hours lead time (15 January 2006).
Figure 9. CO2 concentration: Time
evolution of surface concentrations
(upper panel), concentration vertical
profiles (left), time evolution of vertical
distribution in inversion (upper right)
and convection (lower right).
3. Coupling of the NIRE to
the ETA model
For the sake of numerical
stability of the dispersion
model (NIRE) in the case of
super-adiabatic conditions, the
temperature
profile
was
adjusted to adiabatic lapse
rate in unstable cases (Fig. 7).
In strong wind conditions the
flow relaxation term was
changed to a sine-shape
function
to
overcome
erroneous lateral boundary
wave generation. At top
boundary the sponge layer
was enlarged for numerical
stability.
Conclusion and future works
Figure 7. Adiabatic adjustment of potential
temperature profile: real (white) and
artificially
adjusted
(green)
potential
temperature vertical profiles.
The NIRE mesoscale dispersion model was able to provide realistic meteorological conditions in
case of suitable initial and lateral boundary conditions taken from the ETA model.
The modular structure of the NIRE model makes it suitable for PBL tests.
The coupled modeling system was able to calculate concentrations for different extreme
meteorological conditions.
Among our future planes are an introduction of newer parameterization schemes into the CO2
sub-model, and performance of further sensitivity and case studies. We want to develop nonlocal closure with the introduction of a transiliation matrix for unstable cases to handle
convection for a better estimate of concentrations by neutral and unstable conditions.
We want to run the coupled modeling system on a daily basis for a long time period to generate
an estimate of the annual variation of the surface fluxes and to estimate the carbon budget in the
Carpathian Basin.