Atmosphere and ocean systems can be simulated effectively by discrete numerical models and, provided that the initial states of the system are known, accurate forecasts of future dynamical behaviour can be determined. Complete information defining all of the states of the system at a specified time are, however, rarely available. Moreover, both the models and the measured data contain inaccuracies and random noise. In this case, observations of the system measured over an interval of time can be used in combination with the model equations to derive estimates of the expected values of the states. The problem of constructing a ‘state-estimator,’ or ‘observer,’ for these systems can be treated by using feedback design techniques from control theory. For the very large nonlinear systems arising in climate, weather and ocean prediction, however, traditional control techniques are not practicable and ‘data assimilation’ schemes are used instead to generate accurate state-estimates (see, for example, Daley, 1994; Bennett, 1992).
- Data Assimilation
- Extended Kalman Filter
- Assimilation Scheme
- Adjoint Model
- Forward Solution
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Nichols, N.K. (2003). Data Assimilation: Aims and Basic Concepts. In: Swinbank, R., Shutyaev, V., Lahoz, W.A. (eds) Data Assimilation for the Earth System. NATO Science Series, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0029-1_2
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