The contribution of lidar measurements to forecasting aerosol pollution events
Lidar measurement of aerosols and its importance in air monitoring

To summarize, it is preferable to combine observations and modelling to adopt an approach based on the synergy of investigative tools. As in the case of weather forecasting, data assimilation makes it possible to bring all the information together. The principle is illustrated in figure 10 for lidar measurements, but remains true whatever the observation. After being initialized for a specific period in the past, at time t for example, the model makes a forecast of particulate pollution. After a certain lapse of time Δt, the modeling results are likely to diverge significantly from reality. This divergence may be due to a number of factors, such as poor consideration of boundary conditions, surface emissions, pollutant injection heights and even computer...