Abstract
The theory of statistical linear estimation (Best Linear Unbiased Estimate, or BLUE – the term Best Linear Unbiased Estimator is also used), upon which a large number of presently existing assimilation algorithms are based, has been described in chapter Variational Assimilation (Talagrand).
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Bennett, A.F., 1992. Inverse Methods in Physical Oceanography, Cambridge University Press, Cambridge, UK, 346pp.
Cañizares, R., A. Kaplan, M.A. Cane, D. Chen and S.E. Zebiak, 2001. Use of data assimilation via linear low-order models for the initialization of El Niño – Southern Oscillation predictions. J. Geophys. Res., 106, 30947–30959.
Chapnik, B., G. Desroziers, F. Rabier and O. Talagrand, 2006. Diagnosis and tuning of observational error statistics in a quasi-operational data assimilation setting. Q. J. R. Meteorol. Soc., 132, 543–565, doi:10.1256/qj.04.102.
Daley, R., 1992. The lagged innovation covariance: A performance diagnostic for atmospheric data assimilation. Mon. Weather Rev., 120, 178–196.
Daley, R., 1993. Estimating observation error statistics for atmospheric data assimilation. Ann. Geophysicae, 11, 634–647.
Desroziers, G., P. Brousseau and B. Chapnik, 2005. Use of randomization to diagnose the impact of observations on analyses and forecasts. Q. J. R. Meteorol. Soc., 131, 2821–2837, doi:10.1256/qj.04.151.
Desroziers, G. and S. Ivanov, 2001. Diagnosis and adaptive tuning of observation-error parameters in a variational assimilation. Q. J. R. Meteorol. Soc., 127, 1433–1452.
Elbern, H., A. Strunk, H. Schmidt and O. Talagrand, 2007. Emission rate and chemical state estimation by 4-dimensional variational inversion. Atmos. Chem. Phys., 7, 3749–3769.
Fisher, M., 2003. Estimation of Entropy Reduction and Degrees of Freedom for Signal for Large Variational Analysis Systems, Technical Memorandum No 397, Research Department, ECMWF, Reading, UK, 18pp., available at the address http://www.ecmwf.int/publications/library/do/references/show?id=83951
Girard, D., 1987. A fast Monte-Carlo Cross-Validation Procedure for Large Least Square Problems with Noisy Data, Technical report RR 687-M, IMAG. Université de Grenoble, Grenoble, France, 22pp.
Hollingsworth, A. and P. Lönnberg, 1989. The verification of objective analyses: Diagnostic of analysis system performance. Meteorol. Atmos. Phys., 40, 3–27.
Kailath, T., 1968. An innovations approach to least-squares estimation. Part I: Linear filtering in additive white noise. IEEE Trans. Automat. Contr., AC-13(6), 646–655.
Ménard, R. and L.-P. Chang, 2000. Assimilation of stratospheric chemical tracer observations using a Kalman filter. Part II: chi2-validated results and analysis of variance and correlation dynamics. Mon. Weather Rev., 128, 2672–2686.
Mitchell, H., C. Charette, C. Chouinard and B. Brasnett, 1990. Revised interpolation statistics for the Canadian data assimilation procedure: Their derivation and application. Mon. Weather Rev., 118, 1591–1614.
Muccino, J.C., N.F. Hubele and A.F. Bennett, 2004. Significance testing for variational assimilation. Q. J. R. Meteorol. Soc., 130, 1815–1838, doi:10.1256/qj.03.47.
Rabier, F., N. Fourrié, D. Chafaï and P. Prunet, 2002. Channel selection methods for infrared atmospheric sounding interferometer radiances. Q. J. R. Meteorol. Soc., 128, 1011–1027.
Rodgers, C.D., 2000. Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific Publishing Co. Ltd, London, UK, 238pp.
Sadiki, W. and C. Fischer, 2005. A posteriori validation applied to the 3D-VAR Arpège and Aladin data assimilation systems. Tellus, 57A, 21–34.
Talagrand, O., 1999. A posteriori evaluation and verification of analysis and assimilation algorithms. In Proceedings of Workshop on Diagnosis of Data Assimilation Systems (November 1998), ECMWF. Reading, England, 17–28, available at the address http://www.ecmwf.int/publications/library/do/references/show?id=87283
Talagrand, O. and F. Bouttier, 2000. Internal diagnostics of data assimilation systems. In Proceedings of Seminar on Diagnosis of Models and Data Assimilation Systems (September 1999), ECMWF. Reading, UK, pp. 407–409.
Wahba, G., D. Johnson, F. Gao and J. Gong, 1995. Adaptive tuning of numerical weather prediction models: Randomized GCV in three and four dimensional data assimilation. Mon. Weather Rev., 123, 3358–3369.
Acknowledgments
The author thanks numerous colleagues, in particular F. Bouttier, B. Chapnik and G. Desroziers, for stimulating discussions. B. Chapnik provided Fig. 2.
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Talagrand, O. (2010). Evaluation of Assimilation Algorithms. In: Lahoz, W., Khattatov, B., Menard, R. (eds) Data Assimilation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74703-1_8
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