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Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM – perfect model experiments
1University of California, Berkeley, CA, USA
2Johns Hopkins University, Baltimore, MD, USA
3Shanghai Typhoon Institute, Shanghai, China
4University of Maryland, College Park, MD, USA
5Arizona State University, AZ, USA
6NASA-GSFC Global Modeling and Assimilation Office, Greenbelt, MD, USA
Abstract. This paper compares the performance of the Local Ensemble Transform Kalman Filter (LETKF) with the Physical-Space Statistical Analysis System (PSAS) under a perfect model scenario. PSAS is a 3D-Var assimilation system used operationally in the Goddard Earth Observing System Data Assimilation System (GEOS-4 DAS). The comparison is carried out using simulated winds and geopotential height observations and the finite volume Global Circulation Model with 72 grid points zonally, 46 grid points meridionally and 55 vertical levels. With forty ensemble members, the LETKF obtains analyses and forecasts with significantly lower RMS errors than those from PSAS, especially over the Southern Hemisphere and oceans. This observed advantage of the LETKF over PSAS is due to the ability of the 40-member ensemble LETKF to capture flow-dependent errors and thus create a good estimate of the evolving background uncertainty. An initial decrease of the forecast errors in the Northern Hemisphere observed in the PSAS but not in the LETKF suggests that the LETKF analysis is more balanced.
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Citation: Liu, J., Fertig, E. J., Li, H., Kalnay, E., Hunt, B. R., Kostelich, E. J., Szunyogh, I., and Todling, R.: Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM – perfect model experiments, Nonlin. Processes Geophys., 15, 645-659, 2008. Bibtex EndNote Reference Manager
