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Rosmond, Thomas; Xu, Liang (2006)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics
NAVDAS-AR is the 4DVAR extension of the United States Navy’s operational 3DVAR data assimilation system, NAVDAS (Naval Research Laboratory Atmospheric Variational Data Assimilation System), where AR stands for accelerated representer. Like NAVDAS, NAVDAS-AR is cast in observation space, and is an observation space formulation of the 4DVAR algorithm, in contrast to the European Centre for Medium-Range Weather Forecasts 4DVAR system, which is an analysis space algorithm. In this paper we show how an inner loop linear solution can be augmented by an outer loop iteration procedure that introduces non-linear effects in the form of a first-order Taylor series expansion. The non-linear problem is then solved iteratively as a sequence of linear problems. We show results of the outer loop strategy, where the first loop, the ‘linear’ solution, is modified in the second outer loop by both a Taylor series term in the calculation of the observation innovations, and a linearized form of the background model that is linearized about an analysis ‘trajectory’ from the solution of the first loop. We also show results of how the outer loop strategy allows better assimilation of observations of surface wind speed and precipitable water, compared to the operational NAVDAS.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Bennett, A. F. 2002. Inverse Modeling of the Ocean and Atmosphere. Cambridge University Press, Cambridge, 234 pp.
    • Bennett, A. F. and McIntosh, P. C. 1982. Open ocean modeling as an inverse problem: tidal theory. J. Phys. Oceanography 12, 1004-1018.
    • Bennett, A. F., and Thorburn, M. A. 1992. The generalized inverse of a nonlinear quasigeostrophic ocean circulation model. J. Phys. Oceanography 22, 213-230.
    • (B11) (B12) Chua, B. S. and Bennett, A. F. 2001. An inverse ocean modeling system. Ocean Modeling 3, 137-165.
    • Cohn, S., da Silva, A., Guo, J., Sienkiewicz, M. and Lamich, D. 1998. Assessing the effects of data selection with the DAO physicalspace Statistical Analysis System. Mon. Wea. Rev. 126, 2913- 2926.
    • Courtier, P. 1997. Dual formulation of four-dimensional data assimilation. Q. J. R. Meteorol. Soc. 123, 2449-2461.
    • Courtier, P., Thepaut, J. N. and Hollingsworth, A. 1994. A strategy for operational implementation of 4D-VAR, using an incremental appraoch. Q. J. R. Meteorol. Soc. 120, 1367-1387.
    • Coutier, P., Andersson, E., Heckley, W., Pailleux, J., Vasiljevic, D. and co-authors. 1998. The ECMWF implementation of three dimensional assimilation (3DVAR). Part 1: formulation. Q. J. R. Meteorol. Soc. 124, 1783-1807.
    • Daley, R. and Barker, E. 2001a. The NAVDAS Source Book. NRL/PJ/7530-01-441. Naval Research Laboratory. 163 pp.
    • Daley, R. and Barker, E. 2001b. NAVDAS - formulation and diagnostics. Mon. Wea. Rev. 129, 869-883.
    • Derber, J., Wu, H. W., Zupanski, M., Parrish, D., Purser, J. and coauthors. 1996. Variational assimilation at NCEP. Proceedings of the workshop on the non-linear aspects of data assimilation, 9-11 September 1996. ECMWF, Reading, UK.
    • Golub, G. H. and Van Loan, C. F. 1996. Matrix Computations, 3rd Edition. Johns Hopkins University Press, Baltimore, MD, 694 pp.
    • Hogan, T. and Rosmond, T. 1991. The description of the Navy Operational Global Atmospheric Prediction System'spectral forecast model. Mon. Wea. Rev. 119, 1786-1815.
    • Rabier, F., Thepaut, J. N. and Courtier, P. 1998. Extended assimilation and forecast experiments with a four-dimensional variational assimilation system. Q. J. R. Meteorol. Soc. 124, 1861-1887.
    • Rabier, F., Javinen, H., Klinker, E., Mahfouf, J.-F. and Simmons, A. 2000. The ECMWF implementation of four-dimensional variational assimilation. I: experimental results with simplified physics. Q. J. R. Meteorol. Soc. 126, 1143-1170.
    • Veerse, F. and Thepaut, J. N. 1998. Multiple-truncation incremental approach for four-dimensional variational data assimilation. Q. J. R. Meteorol. Soc. 124, 1880-1908.
    • Xu, L. and Daley, R. 2000. Towards a true four-dimensional data assimilation algorithm: application of a cycling representer algorithm to a simple transport problem. Tellus 52A, 109-128.
    • Xu, L. and Daley, R. 2002. Data assimilation with a barotropically unstable shallow water system using representer algorithms. Tellus 54A, 125-137.
    • Xu, L. and Rosmond, T. 2004. Formulation of the NRL Atmospheric Variational Data Assimilation System - Accelerated Representer (NAVDAS-AR). NRL/MR/7532-04-36. Naval Research Laboratory, 28 pp.
    • Xu, L., Rosmond, T. and Daley, R. 2005. Development of NAVDASAR. Formulation and initial tests of the linear problem. Tellus 57A, 546-559.
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