Section: New Results
Monitoring network design
In this section, we report studies that are related to the evaluation of monitoring networks and to new monitoring strategies. This year, we studied the impact of using lidar observation for particulate matter forecasting.
Assimilation of ground versus lidar observations for PM forecasting
Participants : Yiguo Wang [CEREA] , Karine Sartelet [CEREA] , Marc Bocquet, Patrick Chazette [LSCE, France] .
This study  investigates the potential impact of future ground-based lidar networks on analysis and short-term forecasts of PM. To do so, an Observing System Simulation Experiment (OSSE) is built for PM data assimilation using optimal interpolation over Europe for one month in 2001. First, we estimate the efficiency of the assimilation of lidar network measurements in improving PM 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. It is found that the assimilation of lidar observations is more efficient at improving PM concentrations in terms of root mean square error and correlation after 12 hours of assimilation than the assimilation of AirBase measurements. Moreover, the spatial and temporal influence of the assimilation of lidar observations is larger and longer. In our experiments, the assimilation of lidar products improves PM forecast for 108 hours against 60 hours for AirBase assimilation. The results show a potentially powerful impact of the future lidar networks. Secondly, since a lidar is a very costly instrument, a sensitivity study on the number of required lidars is performed to help defining an optimal lidar network for PM forecast. The results suggest 12 lidar stations over western Europe, because a network with 26 lidar stations is more expensive and offers a limited improvement (less than of root mean square error on average) over the lidar network. A comparison of two networks with 12 lidar stations at different locations does not lead to substantial differences.