Titlebook: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV); Seon Ki Park,Liang Xu Book 2022 The Editor(s) (if applic

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Conclusions and Recommendations,er geophysical fluid flows. Data assimilation is the process whereby the uncertainty in initial conditions?is reduced by the astute combination of model predictions and real-time data. This chapter reviews recent findings from investigations on the impact of chaos?on data assimilation methods: for t

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Chiara Trabacchi,Martin Stadelmannate of the system based on available observations, the so-called filtering?problem. Standard filtering?solutions are computed recursively as successive cycles of alternating time-update (forecast) and observation-update (analysis) steps. This path is however not the only recursive way to compute the

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Motilin

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Climate Change Adaptation in Africad, the second-order adjoint?method among them. General sensitivity analysis?for the? optimality system?is presented. Using the Hessian, the sensitivity of the optimal solution and its functionals?is studied with respect to observations and uncertainties in model parameters. Numerical examples for jo

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Alebachew Adem,Karl Deering,Samuel Molla best estimate of the current state of the atmosphere. Appropriately specifying observation error statistics is necessary to obtain an optimal analysis. Observation error can originate from instrument error as well as the error of representation. While representation error is most commonly associate

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