Synopsis
Data assimilation: aims and basic concepts.- Bayesian estimation. Optimal interpolation. Statistical linear estimation.- Variational assimilation. Adjoint equations.- Control operators and fundamental control functions in data assimilation.- Solvability of variational data assimilation problems and iterative algorithms.- Fundamental control functions and error analysis.- A posteriori validation of assimilation algorithms.- to initialization.- Digital filter initialization.- Treating model errors in 3-D and 4-D data assimilation.- Observing the atmosphere.- Atmospheric modelling.- Operational implementation of variational data assimilation.- Quality control: methodology and applications.- Statistical assimilation of satellite data: method, algorithms, examples.- Theoretical impact assessment of satellite data on weather forecasts.- The correlation features of the inverse problem solution in atmospheric remote sensing.- Assimilation of remote sensing observations in Numerical Weather Prediction.- Research satellites.- The structure and evolution of the atmosphere.- to atmospheric photochemical modelling.- Ozone assimilation.- Multivariate chemical data assimilation.- Uses of ocean data assimilation and ocean state estimation.- Altimeter covariances and errors treatment.- Assimilation of hydrographic data and analysis of model bias.- Land surface processes.- Assimilation of land surface data.- Land data assimilation systems.- Reanalysis.
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