April 2011
Message 25

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[Met-jobs] PhD position in applied mathematics

From Vivien Mallet <>
Date Mon, 11 Apr 2011 17:58:19 +0200

PhD Thesis: Coupling Ensemble Forecast and Data Assimilation -- Application to 
Air Quality Simulation

Nowadays, air quality forecasts are carried out using chemistry-transport 
models. Based on meteorological forecasts, these models compute pollutant 
concentrations (like ozone over Europe) for a few days ahead. Shortcomings in 
the forecasts originate from the high uncertainties in the input data to the 
models (meteorological fields, emissions, ...) and in the physical formulation 
of the models (turbulence, chemistry, ...). In such a context, forecasting 
should not rely on a single model. Instead, a forecast should be based on an 
ensemble of models that should account for all uncertainty sources.

In order to reduce the uncertainties, data assimilation methods take advantage 
of ground-based and satellite observations. These assimilation methods actually 
merge the information contained in a numerical model and the information 
brought by the observations, so as to produce the estimate of the model state 
that minimizes the error variance. Several such methods are appropriate for 
high-dimensional systems like air quality models. They naturally apply to a 
single model.

Meanwhile, better forecasts have been produced by ensemble methods in which the 
forecasts of several models are linearly combined. The weights of the linear 
combination may be determined by machine learning algorithms, based on past 
observations and forecasts. This approach is often referred to as ensemble 

The objective of the PhD thesis is to develop methods that combine the two 
approaches: data assimilation and ensemble forecasting. A proper theoretical 
framework will be needed for these new methods. Application to air quality 
forecast will probe their efficiency. The first research advances in this 
direction are extremely promising.

Further information:

Contact: Vivien Mallet (, +33 01 39 63 55 76)

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