Comparison of hybrid four-dimensional data assimilation methods with and without the tangent linear and adjoint models for predicting the life cycle of Hurricane Karl (2010)

One method uses a mix of ensemble- and climate-based background error covariance in a four-dimensional variational data assimilation (4DVar) system that uses an adjoint model to assimilate observations over a time window (denoted E4DVar). The second method approximates the function of linearized models in 4DVar with perturbations generated from an ensemble forecast using the full nonlinear model (denoted 4DEnVar).

Ensemble perturbations in 4DEnVar provide a four-dimensional covariance, which is combined with a static climate-based covariance for performing the data assimilation. In cycling data assimilation experiments, analyses produced by both methods provide more accurate intensity forecasts than E3DVar, owing mostly to a better representation of moisture near the developing storm. Despite providing a computationally efficient alternative to E4DVar, predictions made from 4DEnVar analyses are less accurate than E4DVar for the tested case study.