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Volume 14, issue 22 | Copyright

Special issue: CHemistry and AeRosols Mediterranean EXperiments (ChArMEx)...

Atmos. Chem. Phys., 14, 12031-12053, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 17 Nov 2014

Research article | 17 Nov 2014

Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

Y. Wang*,2,1, K. N. Sartelet1, M. Bocquet3,1, P. Chazette2, M. Sicard4,5, G. D'Amico6, J. F. Léon7, L. Alados-Arboledas8,9, A. Amodeo6, P. Augustin10, J. Bach4, L. Belegante11, I. Binietoglou6,11, X. Bush4, A. Comerón4, H. Delbarre10, D. García-Vízcaino4, J. L. Guerrero-Rascado8,9, M. Hervo12, M. Iarlori13, P. Kokkalis14, D. Lange4,5, F. Molero15, N. Montoux12, A. Muñoz4, C. Muñoz4, D. Nicolae11, A. Papayannis14, G. Pappalardo6, J. Preissler16,**, V. Rizi13, F. Rocadenbosch4,5, K. Sellegri12, F. Wagner16,***, and F. Dulac2 Y. Wang et al.
  • 1CEREA, joint laboratory École des Ponts ParisTech – EDF R&D, Université Paris-Est, 77455 Champs-sur-Marne, France
  • 2LSCE, joint laboratory CEA-CNRS-UVSQ, UMR8212, 91191 Gif-sur-Yvette, France
  • 3INRIA, Paris-Rocquencourt Research Center, Le Chesnay, France
  • 4Remote Sensing Laboratory, Universitat Politècnica de Catalunya, Barcelona, Spain
  • 5Centre de Recerca de l'Aeronàutica i de l'Espai – Institut d'Estudis Espacials de Catalunya, Universitat Politècnica de Catalunya, Barcelona, Spain
  • 6Consiglio Nazionale delle Ricerche-Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), Tito (Potenza), 85050, Italy
  • 7Laboratoire d'Aérologie, Université Toulouse III, Centre national de la recherche scientifique, Toulouse, France
  • 8IISTA, University of Granada, Autonomous Government of Andalusia, Av. del Mediterráneo s/n, 18006, Granada, Spain
  • 9Dpt. Applied Physics, University of Granada, Fuentenueva s/n, 18071, Granada, Spain
  • 10LPCA, Université du Littoral Côte d'Opale, 59140 Dunkerque, France
  • 11National Institute of R&D for Optoelectronics, 409 Atomistilor Str. 77125, Magurele, Ilfov, Romania
  • 12LaMP-CNRS, Observatoire de Physique de Globe, Clermont-Ferrand, France
  • 13CETEMPS, Dipartimento di Scienze Fisiche e Chimiche, Università Degli Studi, L'Aquila, Italy
  • 14NTUA, Physics Department, Laser Remote Sensing Laboratory, 15780 Zografou, Greece
  • 15CIEMAT, Department of Environment, 28040 Madrid, Spain
  • 16Geophysics Center of Evora, University of Evora, Rua Romao Ramalho 59, 7000 Evora, Portugal
  • *now at: Nansen Environmental and Remote Sensing Center, N-5006 Bergen, Norway
  • **now at: Centre for Climate and Air Pollution Studies, National University of Ireland
  • ***now at: Deutscher Wetterdienst, Hohenpeißenberg Meteorological Observatory, Albin-Schwaiger-Weg 10, 82383 Hohenpeißenberg, Germany

Abstract. 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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