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| Titel |
Assimilation of ground versus lidar observations for PM10 forecasting |
| 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 ; 13, no. 1 ; Nr. 13, no. 1 (2013-01-11), S.269-283 |
| Datensatznummer |
250017550
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| Publikation (Nr.) |
 copernicus.org/acp-13-269-2013.pdf |
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| Zusammenfassung |
| This article investigates the potential impact of future ground-based lidar
networks on analysis and short-term forecasts of particulate matter with a
diameter smaller than 10 μm (PM10). To do so, an Observing
System Simulation Experiment (OSSE) is built for PM10 data assimilation
(DA) using optimal interpolation (OI) over Europe for one month from 15 July
to 15 August 2001. First, using a lidar network with 12 stations and
representing the "true" atmosphere by a simulation called "nature run",
we estimate the efficiency of assimilating the lidar network measurements in
improving PM10 concentration for analysis and forecast. It is compared
to the efficiency of assimilating concentration measurements from the AirBase
ground network, which includes about 500 stations in western Europe. It is
found that assimilating the lidar observations decreases by about 54% the
root mean square error (RMSE) of PM10 concentrations after 12 h of
assimilation and during the first forecast day, against 59% for the
assimilation of AirBase measurements. However, the assimilation of lidar
observations leads to similar scores as AirBase's during the second forecast
day. The RMSE of the second forecast day is improved on average over the
summer month by 57% by the lidar DA, against 56% by the AirBase DA.
Moreover, the spatial and temporal influence of the assimilation of lidar
observations is larger and longer. The results show a potentially powerful
impact of the future lidar networks. Secondly, since a lidar is a costly
instrument, a sensitivity study on the number and location of required lidars
is performed to help define an optimal lidar network for PM10 forecasts.
With 12 lidar stations, an efficient network in improving PM10 forecast
over Europe is obtained by regularly spacing the lidars. Data assimilation
with a lidar network of 26 or 76 stations is compared to DA with the
previously-used lidar network. During the first forecast day, the
assimilation of 76 lidar stations' measurements leads to a better score (the
RMSE decreased by about 65%) than AirBase's (the RMSE decreased by about
59%). |
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