A 4DEnVar-based Ensemble Four-Dimensional Variational (En4DVar) Hybrid
Data Assimilation System for Global NWPs: System Description and Primary
Tests
Abstract
This study developed an ensemble four-dimensional variational (En4DVar)
hybrid data assimilation (DA) system. Different from most of the
available En4DVar systems that adopted ensemble Kalman Filter class or
ensemble DA approaches to produce ensemble covariances for their hybrid
background error covariances (BECs), it used a four-dimensional
ensemble-variational (4DEnVar) system to obtain the ensemble covariance.
The localization scheme for 4DEnVar applied orthogonal functions to
decompose the correlation matrix so that it was implemented easily and
rapidly. In terms of analysis quality and forecast skill, the En4DVar
system was evaluated in the single-point observation experiments and
observing system simulation experiments (OSSEs) with sounding and
cloud-derived wind observations, using its standalone four-dimensional
variational (4DVar) and 4DEnVar components as references. The
single-point observation experiments visually verified the explicit
flow-dependent characteristic of the BEC due to the introduction of the
ensemble covariance from the 4DEnVar system. The OSSE-based sensitivity
experiments revealed different contributions of the weight for the
ensemble covariance in the En4DVar system to the forecasts in the
Northern and Southern Extratropics and Tropics. A much higher weight for
the ensemble covariance in a properly inflated hybrid covariance helped
En4DVar produce the most reasonable analysis. The forecast initialized
by En4DVar is overall better than by 4DVar and 4DEnVar, although the
quality of En4DVar analysis is between those of 4DVar and 4DEnVar
ensemble mean analyses. It indicates that the flow-dependent ensemble
covariance provided by 4DEnVar dominantly contributes to the
improvements in the En4DVar-initialized forecast, with certain but
necessary constraint from the balanced climatological covariance.