Simplistic Overview of Reanalysis Data Assimilation Methods

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Simplistic Overview of Reanalysis Data Assimilation Methods
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Data assimilation is the process of combining observations from a wide variety of sources and forecast output from a weather prediction model. The resulting analysis is considered to be the 'best' estimate of the state of the atmosphere at a particular instant in time. The process of combining the observational and model information is accomplished within a Bayesian statistical framework where probability distributions associated with observations and forecasts are combined with dynamical constraints.

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Key Figures

Figure 1 . Representation of four basic strategies for data assimilation, as a function of time. The way the time distribution of observations ("obs") is processed to produce a time sequence of assimilated states (the lower curve in each panel) can be sequential and/or continuous. (Source ECMWF: figure and caption)

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Earth system components and main variables
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Key Publications
  1. Courtier, P., J.-N. Thépaut and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-VAR, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367-1387.
  2. Courtier, P., E. & coauthors, 1998: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). Part 1: formulation. Quart. J. Roy. Meteor. Soc., 124, 1783-1807.
  3. Daley, R., 1991: Atmospheric Data Analysis. Cambridge Atmospheric and Space Science Series, Cambridge University Press. ISBN 0-521-38215-7, 457 pages.