Data assimilation is the combination of information from observations and models of a particular physical system in order to get the best possible estimate of the state of that system. The technique has wide applications across a range of earth sciences, a major application being the production of operational weather forecasts. Others include oceanography, atmospheric chemistry, climate studies, and hydrology.
Data Assimilation for the Earth System is a comprehensive survey of both the theory of data assimilation and its application in a range of earth system sciences. Data assimilation is a key technique in the analysis of remote sensing observations and is thus particularly useful for those analysing the wealth of measurements from recent research satellites.
This book is suitable for postgraduate students and those working on the application of data assimilation in meteorology, oceanography and other earth sciences.

Inhalt

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.