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| Titel |
Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign |
| VerfasserIn |
Y. Wang, K. N. Sartelet, M. Bocquet, P. Chazette |
| 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 ; 14, no. 7 ; Nr. 14, no. 7 (2014-04-08), S.3511-3532 |
| Datensatznummer |
250118571
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| Publikation (Nr.) |
 copernicus.org/acp-14-3511-2014.pdf |
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| Zusammenfassung |
| In this study, we investigate the ability of the chemistry transport model
(CTM) Polair3D of the air quality modelling platform
Polyphemus to simulate lidar backscattered profiles from model
aerosol concentration outputs. This investigation is an important
preprocessing stage of data assimilation (validation of the observation
operator). To do so, simulated lidar signals are compared to hourly lidar
observations performed during the MEGAPOLI (Megacities: Emissions, urban,
regional and Global Atmospheric POLlution and climate effects, and Integrated
tools for assessment and mitigation) summer experiment in July 2009, when a
ground-based mobile lidar was deployed around Paris on-board a van. The
comparison is performed for six different measurement days, 1, 4, 16, 21,
26 and 29 July 2009, corresponding to different levels of pollution and
different atmospheric conditions. Overall, Polyphemus well reproduces
the vertical distribution of lidar signals and their temporal variability,
especially for 1, 16, 26 and 29 July 2009. Discrepancies on 4 and 21 July
2009 are due to high-altitude aerosol layers, which are not well modelled. In
the second part of this study, two new algorithms for assimilating lidar
observations based on the optimal interpolation method are presented. One
algorithm analyses PM10 (particulate matter with diameter less than
10 μm) concentrations. Another analyses PM2.5 (particulate
matter with diameter less than 2.5 μm) and PM2.5–10
(particulate matter with a diameter higher than 2.5 μm and lower
than 10 μm) concentrations separately. The aerosol simulations
without and with lidar data assimilation (DA) are evaluated using the Airparif (a
regional operational network in charge of air quality survey around the Paris
area) database to demonstrate the feasibility and usefulness of
assimilating lidar profiles for aerosol forecasts. The evaluation shows that
lidar DA is more efficient at correcting PM10 than
PM2.5, probably because PM2.5 is better modelled than PM10.
Furthermore, the algorithm which analyses both PM2.5and PM2.5–10
provides the best scores for PM10. The averaged root-mean-square error (RMSE) of PM10 is
11.63 μg m−3 with DA (PM2.5 and PM2.5–10), compared
to 13.69 μg m−3 with DA (PM10) and
17.74 μg m−3 without DA on 1 July 2009. The averaged RMSE of
PM10 is 4.73 μg m−3 with DA (PM2.5 and
PM2.5–10), against 6.08 μg m−3 with DA (PM10) and
6.67 μg m−3 without DA on 26 July 2009. |
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