Section: Application Domains
Air quality modelling implies studying the interactions between meteorology and atmospheric chemistry in the various phases of matter, which leads to the development of highly complex models. The different usages of these models comprise operational forecast, case studies, impact studies, etc , with both societal (e.g. public information on pollution forecast) and economical impacts (e.g. impact studies for dangerous industrial sites). A model lacks some appropriate data, notably emissions, for performing an accurate forecast and data assimilation techniques are recognised as crucial for the improvement of forecast's quality. These techniques, and notably the variational ones, are barely surfacing in atmospheric chemistry.
In this context, the Clime team is interested in different problems:
Definition of second order data assimilation for the design of optimal observation networks. Management of combinations of sensor types and deployment modes. Dynamic management of mobile sensors' trajectories.
Development of ensemble forecast methods for estimating the quality of the prediction, in relation with the quality of the model and of the observations. Sensitivity analysis with respect to model's parameters so as to identify physical and chemical processes, whose modelling must be improved.
Development of methodologies for super-ensemble forecast (different models, or different configurations of the same model). Investigation on how super-ensembles must be generated, with how many members and with which constraints?
How to estimate the source of an accidental release of pollutant, using observations and a dispersion model (from the near-field to the continental scale)? How to optimally predict the evolution of a plume? Hence, how to help people in charge to evaluate risks for the population?
Assimilation of satellite measurements of troposphere chemistery.
The activities of the Clime team in air quality are supported by the development of the Polyphemus air quality modelling system. This system has a modular design, which makes it easier to manage high level applications such as inverse modelling, data assimilation and ensemble forecast.