An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part II: 4DEnVar and Hybrid Variants

Abstract

Abstract This work describes the formulation of a hybrid four-dimensional ensemble--variational (4DEnVar) algorithm and initialization options utilized within the National Centers for Environmental Prediction global data assimilation system. Initialization schemes that are proposed for use are the tangent-linear normal mode constraint, weak constraint digital filter, and a combination thereof. An observing system simulation experiment is carried out to evaluate the impact of utilizing hybrid 4DEnVar with various initialization techniques. The experiments utilize a dual-resolution configuration, where the ensemble is run at roughly half the resolution of the deterministic component. It is found that by going from 3D to 4D, analysis error is reduced for most variables and levels. The inclusion of a time-invariant static covariance when used without a normal mode–based strong constraint is found to have a small, positive impact on the analysis. The experiments show that the weak constraint digital filter degrades the quality of analysis, due to the use of hourly states to prescribe high-frequency noise. It is found that going from 3D to 4D ensemble covariances has a relatively larger impact in the extratropics, whereas the original inclusion of ensemble-based covariances was found to have the largest impact in the tropics. The improvements found in going from 3D to 4D covariances in the hybrid EnVar formulation are not as large as was found in Part I from the original introduction of the hybrid algorithm. The analyses generated by the 4D hybrid scheme are found to yield slightly improved extratropical height and wind forecasts, with smaller impacts on other variables and in general in the tropics.