Contribution de Y. WANG, K.N. SARTELET, M. BOCQUET, and P. CHAZETTE:

Assimilation of surface versus lidar observations for PM10 forecasting

In air quality, aerosols have an impact on regional and globe climates as well as on ecological equilibrium and human health. Thus their accurate prediction is important. Data assimilation (DA) is an analysis technique which uses observations to reduce the uncertainties in input data of the model, and improve the forecast. In general, in situ surface measurements are assimilated. However, they do not provide information on the vertical profile. Thanks to the new generation of portable lidar systems developed over the past years, one can now carry out spatially denser observations of aerosol optical properties in the mid and lower troposphere. In order to investigate the potential impact of future ground-based lidar network LEONET ( on analysis and short-term forecasts of particulate matter with a diameter smaller than 10 um (PM10), an Observing System Simulation Experiment (OSSE) is built for PM10 DA using optimal interpolation over Europe for one month in 2001. First, using a lidar network with 12 stations, we will introduce the estimation of the efficiency of assimilating the lidar network measurements in improving PM10 concentration 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. We find that assimilating the lidar observations decreases by about 54% the root mean square error (RMSE) of PM10 concentrations after 12 hours 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, we find that 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, we will introduce a sensitivity study on the number and location of required lidars to help defining an optimal lidar network for PM10 forecast. With 12 lidar stations, an efficient network in improving PM10 forecast over Europe is obtained by regularly spacing the lidars.

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