  This paper explores the potential of Local Ensemble Transform Kalman Filter
(LETKF) by comparing the performance of LETKF with an operational 3D-Var
assimilation system, Physical-Space Statistical Analysis System (PSAS), under a
perfect model scenario. The comparison is carried out on the finite volume
Global Circulation Model (fvGCM) with 72 grid points zonally, 46 grid points
meridionally and 55 vertical levels. With only forty ensemble members, LETKF
obtains an analysis and forecasts with lower RMS errors than those from PSAS.
The performance of LETKF is further improved, especially over the oceans, by
assimilating simulated temperature observations from rawinsondes and
conventional surface pressure observations instead of geopotential heights. An
initial decrease of the forecast errors in the NH observed in PSAS but not in
LETKF suggests that the PSAS analysis is less balanced. The observed advantage
of LETKF over PSAS is due to the ability of the forty-member ensemble from
LETKF to capture flow-dependent errors and thus create a good estimate of the
true background uncertainty. Furthermore, localization makes LETKF highly
parallel and efficient, requiring only 5 minutes per analysis in a cluster of
20 PCs with forty ensemble members.
