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| Titel |
Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin |
| VerfasserIn |
Y. Wang, K. N. Sartelet, M. Bocquet, P. Chazette, M. Sicard, G. D'Amico, J. F. Léon, L. Alados-Arboledas, A. Amodeo, P. Augustin, J. Bach, L. Belegante, I. Binietoglou, X. Bush, A. Comerón, H. Delbarre, D. García-Vízcaino, J. L. Guerrero-Rascado, M. Hervo, M. Iarlori, P. Kokkalis, D. Lange, F. Molero, N. Montoux, A. Muñoz, C. Muñoz, D. Nicolae, A. Papayannis, G. Pappalardo, J. Preißler, V. Rizi, F. Rocadenbosch, K. Sellegri, F. Wagner, F. Dulac |
| 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. 22 ; Nr. 14, no. 22 (2014-11-17), S.12031-12053 |
| Datensatznummer |
250119163
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| Publikation (Nr.) |
 copernicus.org/acp-14-12031-2014.pdf |
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| Zusammenfassung |
| This paper presents a new application of assimilating lidar signals to
aerosol forecasting. It aims at investigating the impact of a ground-based
lidar network on the analysis and short-term forecasts of aerosols through a
case study in the Mediterranean basin. To do so, we employ a data
assimilation (DA) algorithm based on the optimal interpolation method
developed in the Polair3D chemistry transport model (CTM) of the
Polyphemus air quality
modelling platform. We assimilate hourly averaged normalised range-corrected
lidar signals (PR2) retrieved from a 72 h period of intensive and
continuous measurements performed in July 2012 by ground-based lidar systems
of the European Aerosol Research Lidar Network (EARLINET) integrated into the
Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) network
and an additional system in Corsica deployed in the framework of the
pre-ChArMEx (Chemistry-Aerosol Mediterranean Experiment)/TRAQA (TRAnsport à
longue distance et Qualité de l'Air) campaign. This lidar campaign was
dedicated to demonstrating the potential operationality of a research network
like EARLINET and the potential usefulness of assimilation of lidar signals
to aerosol forecasts. Particles with an aerodynamic diameter lower than
2.5 μm (PM2.5) and those with an aerodynamic diameter higher
than 2.5 μm but lower than 10 μm (PM10–2.5) are
analysed separately using the lidar observations at each DA step. First, we
study the spatial and temporal influences of the assimilation of lidar
signals on aerosol forecasting. We conduct sensitivity studies on algorithmic
parameters, e.g. the horizontal correlation length (Lh) used in
the background error covariance matrix (50 km, 100 km or 200 km), the
altitudes at which DA is performed (0.75–3.5 km, 1.0–3.5 km or
1.5–3.5 km a.g.l.) and the assimilation period length (12 h or 24 h). We
find that DA with Lh = 100 km and assimilation from 1.0 to
3.5 km a.g.l. during a 12 h assimilation period length leads to the best
scores for PM10 and PM2.5 during the forecast period with reference
to available measurements from surface networks. Secondly, the aerosol
simulation results without and with lidar DA using the optimal parameters
(Lh = 100 km, an assimilation altitude range from 1.0 to
3.5 km a.g.l. and a 12 h DA period) are evaluated using the level 2.0
(cloud-screened and quality-assured) aerosol optical depth (AOD) data from
AERONET, and mass concentration measurements (PM10 or PM2.5) from
the French air quality (BDQA) network and the EMEP-Spain/Portugal network.
The results show that the simulation with DA leads to better scores than the
one without DA for PM2.5, PM10and AOD. Additionally, the
comparison of model results to evaluation data indicates that the temporal
impact of assimilating lidar signals is longer than 36 h after the
assimilation period. |
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