A form of variational data assimilation in which adjoint equations are used to obtain gradients of a scalar measure of a forecast (J) with respect to model control variables. The complete assimilation generally involves an iterative procedure to reduce the cost-function in which a minimization algorithm is used to adjust initial conditions based on a sensitivity gradient (∇xJ) provided by the adjoint model. See adjoint sensitivity, variational objective analysis.