Difference between revisions of "Adjoint model"
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Latest revision as of 17:21, 26 January 2012
adjoint model
A model composed of adjoint equations that maps a sensitivity gradient vector, ∇_{x}J(t_{0}) = 𝗟^{T}∇_{x}J(t_{1}) , from a forecast time, t_{1}, to an earlier time, t_{0}, which can be the initial time of a forecast trajectory.
J is some scalar measure of the forecast, 𝗟^{T} is a linear adjoint operator, and x is the model state vector. An adjoint model can provide a firstorder (tangent linear) approximation to sensitivity in a nonlinear model.
See adjoint equation, adjoint sensitivity, tangent linear equation.
See adjoint equation, adjoint sensitivity, tangent linear equation.
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