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| Titel |
Development of a custom OMI NO2 data product for evaluating biases in a regional chemistry transport model |
| VerfasserIn |
G. Kuhlmann, Y. F. Lam, H. M. Cheung, A. Hartl, J. C. H. Fung, P. W. Chan, M. O. Wenig |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 15, no. 10 ; Nr. 15, no. 10 (2015-05-21), S.5627-5644 |
| Datensatznummer |
250119746
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| Publikation (Nr.) |
 copernicus.org/acp-15-5627-2015.pdf |
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| Zusammenfassung |
In this paper, we present the custom Hong Kong NO2 retrieval
(HKOMI) for the Ozone Monitoring Instrument (OMI) on board the Aura
satellite which was used to evaluate a high-resolution chemistry
transport model (CTM) (3 km × 3 km spatial
resolution). The atmospheric chemistry transport was modelled in the
Pearl River Delta (PRD) region in southern China by the Models-3
Community Multiscale Air Quality (CMAQ) modelling system from
October 2006 to January 2007. In the HKOMI NO2 retrieval,
tropospheric air mass factors (AMFs) were recalculated using
high-resolution ancillary parameters of surface reflectance, a priori
NO2 and aerosol profiles, of which the latter two were taken
from the CMAQ simulation. We tested the influence of the ancillary
parameters on the data product using four different aerosol
parametrizations. Ground-level measurements by the PRD Regional Air
Quality Monitoring (RAQM) network were used as additional independent
measurements.
The HKOMI retrieval increases estimated tropospheric NO2
vertical column densities (VCD) by (+31 ± 38)%, when compared
to NASA's standard
product (OMNO2-SP), and improves the normalized mean bias (NMB) between
satellite and ground observations by 26 percentage points from −41 to
−15%. The individual influences of the parameters are
(+11.4 ± 13.4)% for NO2 profiles, (+11.0 ± 20.9)%
for surface reflectance and (+6.0 ± 8.4)% for the best aerosol
parametrization. The correlation coefficient r is low between ground
and satellite observations (r = 0.35). The low r and the remaining NMB
can be explained by the low model performance and the expected differences
when comparing point measurements with area-averaged satellite observations.
The correlation between CMAQ and the RAQM network
is low (r ≈ 0.3) and the model underestimates the NO2
concentrations in the northwestern model domain (Foshan and Guangzhou).
We compared the CMAQ NO2 time series of the two main plumes with
our best OMI NO2 data set
(HKOMI-4). The model overestimates
the NO2
VCDs by about 15% in Hong Kong and Shenzhen, while
the correlation coefficient is satisfactory (r = 0.56). In Foshan
and Guangzhou, the correlation is low (r = 0.37) and the model
underestimates the VCDs strongly (NMB = −40%). In
addition, we estimated that the OMI VCDs are also underestimated by
about 10 to 20% in Foshan and Guangzhou because of the
influence of the model parameters on the AMFs.
In this study, we demonstrate that the HKOMI NO2 retrieval
reduces the bias of the satellite observations and how the data set
can be used to study the magnitude of NO2 concentrations in
a regional model at high spatial resolution of
3 × 3 km2. The low bias was achieved with
recalculated AMFs using updated surface reflectance, aerosol profiles
and NO2 profiles. Since unbiased concentrations are
important, for example, in air pollution studies, the results of
this paper can be very helpful in future model evaluation studies. |
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