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This paper explores the potential of Local Ensemble Transform Kalman Filter(LETKF) by comparing the performance of LETKF with an operational 3D-Varassimilation system, Physical-Space Statistical Analysis System (PSAS), under aperfect model scenario. The comparison is carried out on the finite volumeGlobal Circulation Model (fvGCM) with 72 grid points zonally, 46 grid pointsmeridionally and 55 vertical levels. With only forty ensemble members, LETKFobtains an analysis and forecasts with lower RMS errors than those from PSAS.The performance of LETKF is further improved, especially over the oceans, byassimilating simulated temperature observations from rawinsondes andconventional surface pressure observations instead of geopotential heights. Aninitial decrease of the forecast errors in the NH observed in PSAS but not inLETKF suggests that the PSAS analysis is less balanced. The observed advantageof LETKF over PSAS is due to the ability of the forty-member ensemble fromLETKF to capture flow-dependent errors and thus create a good estimate of thetrue background uncertainty. Furthermore, localization makes LETKF highlyparallel and efficient, requiring only 5 minutes per analysis in a cluster of20 PCs with forty ensemble members.

Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM – perfect model experiments