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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 4 | Copyright
Nonlin. Processes Geophys., 15, 645-659, 2008
https://doi.org/10.5194/npg-15-645-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  05 Aug 2008

05 Aug 2008

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

J. Liu1, E. J. Fertig2, H. Li3, E. Kalnay4, B. R. Hunt4, E. J. Kostelich5, I. Szunyogh5, and R. Todling6 J. Liu et al.
  • 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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