References
- Bishop C. Pattern Recognition and Machine Learning. Springer: New York, 2006
- Bocquet M. Inverse modelling of atmospheric tracers: non-Gaussian methods and second-order sensitivity analysis. Nonlin. Process. Geophys. 2008; 15: 127–143. 10.3402/tellusa.v64i0.17192.
- Bocquet M. Pires C. A. Wu L. Beyond Gaussian statistical modeling in geophysical data assimilation. Mon. Weather Rev. 2010; 138: 2997–3023. 10.3402/tellusa.v64i0.17192.
- Kalnay E. Atmospheric modeling, data assimilation and predictability. Cambridge University Press: Cambridge, 2003
- Cardinali, C, Pezzulli, S and Andersson, E. 2004. Influence-matrix diagnostics of a data assimilation system. Q. J. R. Met. Soc. 130, 2767–2786. 10.3402/tellusa.v64i0.17192.
- Cover T. M. Thomas J. A. Elements of Information Theory (Wiley series in Telecommunications). John Wiley and Sons: New York, 1991
- Evensen G. Advanced data assimilation for strongly nonlinear dynamics. Mon. Weather Rev. 1997; 125: 1342–1354.
- Eyre, J. E. 1990. The information content of data from satellite sounding systems: A simulation study. Q. J. R. Met. Soc. 116, 401–434. 10.3402/tellusa.v64i0.17192.
- Fisher, M. 2003. Estimation of entropy reduction and degrees of freedom for signal for large variational analysis systems. Technical Report, ECMWF.
- Haven K. Majda A. Abramov R. Quantifying predictability through information theory: small sample estimation in a non-Gaussian framework. J. Comput. Phys. 2005; 206: 334–362. 10.3402/tellusa.v64i0.17192.
- Hoffmann, G. M, Waslander, S. L and Tomlin, C. J. 2006. Mutual information methods with particle filters for mobile sensor network control. In: Proceedings of the 45th IEEE Conference on Decision & Control, conference held 13–15 Dec, 2006 in San Diego, CA. Publisher is Institute for Electrical and Electronic Engineers (IEEE), USA. pp. 1019–1024.
- Kleeman R. Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci. 2002; 59: 2057–2072.
- Kleeman, R. 2011. Information theory and dynamical system predictability. Entopy. 13, 612–649. 10.3402/tellusa.v64i0.17192.
- Lorenz E. N. Deterministic nonperiodic flow. J. Atmos. Sci. 1963; 20: 130–141.
- Majda A. Kleeman R. Cai D. A mathematical framework for quantifying predictability through relative entropy. Methods Appl. Anal. 2002; 9: 425–444.
- Pires C. A. Vautard R. Talagrand O. On extending the limits of variational assimilation in nonlinear chaotic systems. Tellus. 1996; 48A: 96–121.
- Rabier F. Overview of global data assimilation developments in numerical weather-prediction centres. Q. J. R. Met. Soc. 2005; 131: 3215–3233. 10.3402/tellusa.v64i0.17192.
- Rodgers C. D. Inverse Methods for Atmospheric Sounding. World Scientific Publishing: Singapore, 2000
- Tarantola A. Inverse Problem Theory. SIAM: Philadelphia, 2005
- van Leeuwen P. J. Particle filtering in geophysical systems. Mon. Weather Rev. 2009; 137: 4089–4114. 10.3402/tellusa.v64i0.17192.
- Xu Q. Measuring information content from observations for data assimilation: relative entropy versus Shannon entropy difference. Tellus. 2007; 59A: 198–209.
- Xu Q. Wei L. Healy S. Measuring information content from observations for data assimilation: connection between different measures and application to radar scan design. Tellus. 2009; 61A: 144–153.