Balance of the Background-Error Variances in the Ensemble Assimilation System DART/CAM

Abstract

his paper quantifies the linear mass?wind field balance and its temporal variability in the global data assimilation system Data Assimilation Research Testbed/Community Atmosphere Model (DART/CAM), which is based on the ensemble adjustment Kalman filter. The part of the model state that projects onto quasigeostrophic modes represents the balanced state. The unbalanced part corresponds to inertio-gravity (IG) motions. The 80-member ensemble is diagnosed by using the normal-mode function expansion. It was found that the balanced variance in the prior ensemble is on average about 90% of the total variance and about 80% of the wave variance. Balance depends on the scale and the largest zonal scales are best balanced. For zonal wavenumbers greater than k = 30 the balanced variance stays at about the 45% level. There is more variance in the westward- than in the eastward-propagating IG modes; the difference is about 2% of the total wave variance and it is associated with the covariance inflation. The applied inflation field has a major impact on the structure of the prior variance field and its reduction by the assimilation step. The shape of the inflation field mimics the global radiosonde observation network (k = 2), which is associated with the minimum variance reduction in k = 2. Temporal variability of the ensemble variance is significant and appears to be associated with changes in the energy of the flow. A perfect-model assimilation experiment supports the findings from the real-observation experiment.