A methodology for fixed observational network design: theory and application to a simulated global prediction system

References

• Anderson, J. L. 1992. Barotropic stationary states and persistent anoma-lies in the atmosphere. J, Atmos. Sci. 49, 1709–1722.
   Web of Science ®Google Scholar
• Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev. 129, 2884–2903.
   Web of Science ®Google Scholar
• Anderson, J. L. 2003. A local least squares framework for ensemble filtering. Mon. Wea. Rev. 131, 634–642.
   Web of Science ®Google Scholar
• Anderson, J. L., Wyman, B., Zhang, S. and Hoar, T. 2005. Assimilation of surface pressure observations using an ensemble filter in an idealized global atmospheric prediction system. J, Atmos. Sci. 62, 2925–2938.
   Web of Science ®Google Scholar
• Arnold, C. and Dey, C., 1986. Observation system simulation experi-ments: Past, present and future. Bull. Am. Meteor. Soc. 67, 687–695.
   Web of Science ®Google Scholar
• Bishop, C. H., Etherton, B. J. and Majumdar, S. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part 1. Theoretical aspects. Mon. Wea. Rev. 129, 420–436.
   Web of Science ®Google Scholar
• Bishop, C. H., Reynolds, C. A. and Tippett, M. K. 2003. Optimization of the Fixed Global Observing Network in a Simple Model. J. Atmos. Sci. 60, 1471–1489.
   Web of Science ®Google Scholar
• Burgers, G., van Leeuwen, P. J. and Evensen, G. 1998. Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev. 126, 1719–1724.
   Web of Science ®Google Scholar
• Cohn, S. E. 1997. An introduction to estimation theory. J Meteorol Soc Japan. 75, 257–288.
   Web of Science ®Google Scholar
• Dabbert, W. E and co-authors, 1996. Research opportunities from merg-ing atmospheric observing and modeling capabilities. Bull. Am. Me-teor. Soc. 77, 305–323.
   Google Scholar
• Daley, R. 1991. Atmospheric Data Analysis. Vol. 2. Cambridge Univer-sity Press, 457 pp.
   Google Scholar
• DelSole, T. 2004. Predictability and information theory, Part I: Measures of predictability. J, Atmos. Sci. 61, 2425–2440.
   Web of Science ®Google Scholar
• Emanuel, K. A., Raymond, D., Betts, A., Bosart, L., Bretherton, C. and co-authors, 1995. Report of the first prospectus development team of the US Weather Research Program to NOAA and NSF. Bull. Am. Meteor. Soc. 76, 1194-1208.
   Web of Science ®Google Scholar
• Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statis-tics. J. Geophys Res. 99, 10143–10162.
   Google Scholar
• Evensen, G. and van Leeuwen, P. J. 1996. Assimilation of geosat altime-ter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model. Mon. Wea. Rev. 128, 1187-1193.
   Google Scholar
• Held I. and Suarez, M. J. 1994. A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models. Bull. Am. Meteor. Soc. 75, 1825–1830.
   Web of Science ®Google Scholar
• Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126, 796–811.
   Web of Science ®Google Scholar
• Houtekamer, P. L. and Mitchell, H. L. 2001. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev. 129, 123–137.
   Web of Science ®Google Scholar
• Khare, S. P. 2004. Observing network design for improved prediction of geophysical fluid flows - analysis of ensemble methods. PhD thesis, Princeton University, 195 pp.
   Google Scholar
• Khare, S. P. and Anderson, J. L. 2006. An examination of ensemble filter based adaptive observation methodologies. Tellus A 58, 179–195.
   Google Scholar
• Kleeman, R., 2002. Measuring dynamical prediction utility using relative entropy. J, Atmos. Sci. 59, 2057–2072.
   Web of Science ®Google Scholar
• Lorenz, E. N. 1995. Predictability: A Problem Partly Solve. Proc. Sem-inar on Predictability. 1, ECMWF, Reading, Berkshire, UK, 1–18.
   Google Scholar
• Majda, A. J. and Timofeyev, I. 2000. Remarkable statistical behavior for truncated Burgers-Hopf dynamics. Proc. National Acad. Sci. 97, 12413–12417.
   Google Scholar
• Majumdar, S. J., Bishop, C. H., Etherton, B. J., Szunyogh, I. and Toth, Z. 2001. Can an ensemble transform Kalman filter predict the reduction in forecast-error variance produced by targeted observations? Q. J. R. Meteorol. Soc. 127, 2803–2820.
   Google Scholar
• Majumdar, S. J., Bishop, C. H., Buizza, R. and Gelaro, R. 2002. A comparison of ensemble transform Kalman filter targeting guidance with ECMWF and NRL total energy singular vector guidance? Q. J. R. Meteorol. Soc. 128, 1–23.
   Web of Science ®Google Scholar
• Marshall, J. and Molteni, E 1993. Toward a dynamical understanding of planetary-scale flow regimes. J, Atmos. Sci. 50, 1792–1818.
   Web of Science ®Google Scholar
• Morss, R. E., Miller, K. and Vasil, M. 2004. A systematic economic approach to evaluating public investment in observations for weather forecasting. Mon. Wea. Rev. 86, 181–191.
   Google Scholar
• Schneider, T. and Griffies, S. M. 1999. A conceptual framework for predictability studies. J. Climate. 12,3133–3155.
   Web of Science ®Google Scholar
• Simmons, A. J. and Burridge, D. M. 1981. An energy and angular-momentum conserving vertical finite-difference scheme and hybrid vertical coordinates. Mon. Wea. Rev. 109, 758–766.
   Web of Science ®Google Scholar
• Simmons, A. J., Mureau, R. and Petroliagas, T. 1995. Error growth and predictability estimates for the ECMWF forecasting system. Q. J. R. Meteorol Soc. 121, 1739–1771.
   Web of Science ®Google Scholar
• Simmons, A. J. and Hollingsworth, A. 2002. Some aspects of the im-provement in skill of numerical weather prediction. Q. J. R. Meteorol Soc. 128, 647–677.
   Web of Science ®Google Scholar
• Snyder, C. and Zhang, E 2003. Assimilation of simulated doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev. 131, 1663–1667.
   Web of Science ®Google Scholar
• Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M. and Whitaker, J. S. 2002. Ensemble square-root filters. Mon. Wea. Rev. 131, 1485–1490.
   Google Scholar
• Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev. 130, 1931–1924.
   Web of Science ®Google Scholar
• Whitaker, J. S., Compo, G. P., Wei, X. and Hamill, T. M. 2003. Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev. 132, 1190–1200.
   Google Scholar