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• W2539400319 abstract "The basic principle of forecast error correction using dynamic data assimilation (DDA) is to alter or move the model trajectory towards a given set of (noisy) observations in such a way that the weighted sum of squared forecast errors (which is also the square of the energy norm of the forecast errors) is a minimum. We have classified the various sources of forecast error in Sect. 1.5.2 A little reflection reveals that there are essentially two ways of altering the solution of a dynamical system: (1) changing the control consisting of the initial/boundary conditions and the parameters of the dynamical model, and (2) by adding an explicit external forcing (a form of state dependent control) which will in turn force the model solution towards the desired goal. The 4D-VAR and FSM based deterministic framework are designed to alter the model trajectory to correct the forecast errors by iteratively adjusting the control (initial/boundary conditions and parameters) using one of the well established algorithms for minimizing the square of the energy norm of the forecast errors. Refer to Chaps. 2 and 4 of this book and Lewis et al. (2006) for details. Identification of errors in the dynamics and associated adjustments go beyond correcting control. Generally, the correction terms added to the constraints force the model forecast to more closely fit the observations either empirically (so-called nudging process) or optimally (a process that involves a least squares fit of model to observation). Lakshmivarahan and Lewis (2013) have comprehensively viewed the work on nudging. In this chapter we address the process of optimally adjusting the constraints to fit the observations. The most powerful method to accomplish this task is Pontryagin’s minimum principle (PMP). It is the centerpiece of this chapter. At the end of the chapter, we also consider an optimal method used in meteorology developed by Derber (1989) and applied to hurricane tracking by DeMaria and Jones (1993)." @default.
• W2539400319 created "2016-10-28" @default.
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• W2539400319 date "2016-10-22" @default.
• W2539400319 modified "2023-09-27" @default.
• W2539400319 title "Forecast Error Correction Using Optimal Tracking" @default.
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• W2539400319 doi "https://doi.org/10.1007/978-3-319-39997-3_5" @default.
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