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| Titel |
The impact of observing characteristics on the ability to predict ozone under varying polluted photochemical regimes |
| VerfasserIn |
P. D. Hamer, K. W. Bowman, D. K. Henze, J.-L. Attié, V. Marecal |
| 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. 18 ; Nr. 15, no. 18 (2015-09-25), S.10645-10667 |
| Datensatznummer |
250120055
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| Publikation (Nr.) |
 copernicus.org/acp-15-10645-2015.pdf |
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| Zusammenfassung |
| We conduct analyses to assess how characteristics of observations of ozone
and its precursors affect air quality forecasting and research. To carry out
this investigation, we use a photochemical box model and its adjoint
integrated with a Lagrangian 4D-variational data assimilation system. Using
this framework in conjunction with pseudo-observations, we perform an ozone
precursor source inversion and estimate surface emissions. We then assess the
resulting improvement in ozone air quality prediction. We use an analytical
model to conduct uncertainty analyses. Using this analytical tool, we address
some key questions regarding how the characteristics of observations affect
ozone precursor emission inversion and in turn ozone prediction. These
questions include what the effect is of choosing which species to observe, of
varying amounts of observation noise, of changing the observing frequency and
the observation time during the diurnal cycle, and of how these different
scenarios interact with different photochemical regimes. In our investigation
we use three observed species scenarios: CO and NO2; ozone, CO, and
NO2; and HCHO, CO and NO2. The photochemical model was set up to
simulate a range of summertime polluted environments spanning NOx-(NO
and NO2)-limited to volatile organic compound (VOC)-limited conditions.
We find that as the photochemical regime changes, here is a variation in the
relative importance of trace gas observations to be able to constrain
emission estimates and to improve the subsequent ozone forecasts. For
example, adding ozone observations to an NO2 and CO observing system is
found to decrease ozone prediction error under NOx- and VOC-limited
regimes, and complementing the NO2 and CO system with HCHO observations
would improve ozone prediction in the transitional regime and under
VOC-limited conditions. We found that scenarios observing ozone and HCHO with
a relative observing noise of lower than 33 % were able to achieve ozone
prediction errors of lower than 5 ppbv (parts per billion by volume).
Further, only observing intervals of 3 h or shorter were able to
consistently achieve ozone prediction errors of 5 ppbv or lower across all
photochemical regimes. Making observations closer to the prediction period
and either in the morning or afternoon rush hour periods made greater
improvements for ozone prediction: 0.2–0.3 ppbv for the morning rush hour
and from 0.3 to 0.8 ppbv for the afternoon compared to only 0–0.1 ppbv for
other times of the day. Finally, we made two complementary analyses that show
that our conclusions are insensitive to the assumed diurnal emission cycle
and to the choice of which VOC species emission to estimate using our
framework. These questions will address how different types of observing
platform, e.g. geostationary satellites or ground monitoring networks, could
support future air quality research and forecasting. |
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