Continued Development of 4D-Variational Data Assimilation and Adjoint-Based Methods of Sensitivity Analysis and Applications Using ROMS

2007 ◽  
Author(s):  
Andrew M. Moore ◽  
Ralph F. Milliff ◽  
Hernan G. Arango
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Variational Data Assimilation

scholarly journals Some Aspects of Sensitivity Analysis in Variational Data Assimilation for Coupled Dynamical Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
Sergei Soldatenko ◽  
Peter Steinle ◽  
Chris Tingwell ◽  
Denis Chichkine
Keyword(s):  
Sensitivity Analysis ◽  
Dynamical Systems ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Computational Cost ◽  
Variational Data Assimilation ◽  
Practical Implementation ◽  
Parameter Uncertainties ◽  
Sensitivity Functions ◽  
Sensitivity Analysis Method

Variational data assimilation (VDA) remains one of the key issues arising in many fields of geosciences including the numerical weather prediction. While the theory of VDA is well established, there are a number of issues with practical implementation that require additional consideration and study. However, the exploration of VDA requires considerable computational resources. For simple enough low-order models, the computational cost is minor and therefore models of this class are used as simple test instruments to emulate more complex systems. In this paper, the sensitivity with respect to variations in the parameters of one of the main components of VDA, the nonlinear forecasting model, is considered. For chaotic atmospheric dynamics, conventional methods of sensitivity analysis provide uninformative results since the envelopes of sensitivity functions grow with time and sensitivity functions themselves demonstrate the oscillating behaviour. The use of sensitivity analysis method, developed on the basis of the theory of shadowing pseudoorbits in dynamical systems, allows us to calculate sensitivity functions correctly. Sensitivity estimates for a simple coupled dynamical system are calculated and presented in the paper. To estimate the influence of model parameter uncertainties on the forecast, the relative error in the energy norm is applied.

On the variational data assimilation problem solving and sensitivity analysis

2017 ◽  
Vol 335 ◽  
pp. 311-326 ◽  
Author(s):  
Rossella Arcucci ◽  
Luisa D'Amore ◽  
Jenny Pistoia ◽  
Ralf Toumi ◽  
Almerico Murli
Keyword(s):  
Sensitivity Analysis ◽  
Problem Solving ◽  
Data Assimilation ◽  
Variational Data Assimilation
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