Adjoint models
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adjoint model
A model composed of adjoint equations that maps a sensitivity gradient vector, ∇xJ(t0) = 𝗟T∇xJ(t1) , from a forecast time, t1, to an earlier time, t0, which can be the initial time of a forecast trajectory.
J is some scalar measure of the forecast, 𝗟T is a linear adjoint operator, and x is the model state vector. An adjoint model can provide a first-order (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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