ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-13-269-2013 Assimilation of ground versus lidar observations for PM<sub>10</sub> forecasting Wang Y. 1 2 Sartelet K. N. 1 Bocquet M. 1 3 Chazette P. 2 CEREA, joint laboratory Ecole des Ponts ParisTech - EDF R&D, Université Paris-Est, 77455 Champs sur Marne, France LSCE, joint laboratory CEA-CNRS, UMR8212, 91191 Gif-sur-Yvette, France INRIA, Paris-Rocquencourt Research Center, Le Chesnay, France 11 01 2013 13 1 269 283 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/13/269/2013/acp-13-269-2013.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/13/269/2013/acp-13-269-2013.pdf

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 (PM<sub>10</sub>). To do so, an Observing System Simulation Experiment (OSSE) is built for PM<sub>10</sub> 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 PM<sub>10</sub> 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 PM<sub>10</sub> 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 PM<sub>10</sub> forecasts. With 12 lidar stations, an efficient network in improving PM<sub>10</sub> 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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