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Simplistic Overview of Reanalysis Data Assimilation Methods

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.

Technical Notes

Most commonly, analyzed products are created using three-dimensional or four-dimensional (3DVAR, 4DVAR) variational data assimilation schemes. 3DVAR treats observations within some time interval about the target analysis time as occurring at the time of the analysis. 4DVAR uses the observations distributed about the target analysis time to estimate the value. (Very simplistically, think of a least squares fit of the observations.) An alternative 4DVAR approach is to use "nudging" where damped differences between the observations and the initial forecast guess, which is assumed to be 'perfect', are used to constrain the analysis toward the observations.

  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.

Key Figures

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Thumbnails

Captions
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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National Center for Atmospheric Research Staff (Eds). Last modified 11 Oct 2018. "The Climate Data Guide: Simplistic Overview of Reanalysis Data Assimilation Methods." Retrieved from https://climatedataguide.ucar.edu/climate-data/simplistic-overview-reanalysis-data-assimilation-methods.

Acknowledgement of any material taken from this page is appreciated. On behalf of experts who have contributed data, advice, and/or figures, please cite their work as well.

Simplistic Overview of Reanalysis Data Assimilation Methods

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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