Environmental Engineering Reference
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of aquatic and terrestrial ecosystems and radiative forcing (absorbing and scat-
tering of solar radiation, Kaufman et al., 2002). The CTMs are used to assess the
effects of future changes in gas, aerosol and aerosol precursor emissions and to
study the impact of source pollutants on air quality elsewhere. Uncertainties in the
emission inventories (De Meij et al., 2006), aerosol dynamics, meteorological
factors, the impact of horizontal resolution of meteorology on model calculations
(Menut et al., 2005) and the fact that the formation of aerosols are known to be
nonlinearly dependent on meteorological parameters (Haywood and Ramaswamy,
1998), all contribute to uncertainties in the calculated gas and aerosol con-
centrations. A good estimate of meteorological variables in the meteorological
datasets is therefore crucial for calculating gas and aerosol impacts on air quality
and climate change, and evaluating coherent reduction strategies. The objective of
this study is to evaluate the impact of meteorological input data on calculated gas
and aerosol concentrations. We use two different meteorological models (MM5
and WRF) together with the chemistry transport model CHIMERE. We focus on
the Po valley area (Italy) for January and June 2005.
2. Method
The CHIMERE model (Bessagnet et al., 2004) is used to simulate air quality over
the Po valley area for January and June 2005 based on the meteorological data sets
provided by MM5 and WRF. We start our study by evaluating the meteorological
parameters T, RH, wind direction and wind speed by comparing them with
meteorological observations for the year 2005. Then we evaluate the calculated
aerosol (PM10) and ozone (O 3 ) concentrations, using the CHIMERE model with
MM5 and WRF results as input data, by comparing the model calculated concen-
trations with measurements.
3. Results
Summarizing the analysis of the annual averaged statistics shows that the Ts are
mainly underestimated (less by WRF) and RHs are in general overestimated by
WRF and underestimated by MM5. WRF output follows better the hourly pattern
of RH. The Po valley is characterized by low wind speeds, which makes it diffi-
cult to simulate the wind field with the prognostic meteorological models. This has
been confirmed with our study. The wind speed is overestimated by both models.
Both models reproduce well the prevailing wind direction. The rainfall is in
general overestimated however the MM5 output shows lower rainfall values. The
hit rate statistics are in general better for WRF.
In Fig. 1 we compare the potential temperature gradient (ptg) profile between
10 and 200 m at the hours 0.00, 06.00, 12.00 and 18.00 h for the whole year.
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