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Optimal placement of mobile sensors for data assimilations Cover

Optimal placement of mobile sensors for data assimilations

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Open Access
|Dec 2012

References

  1. Baker N. Daley R. Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Q. J. R. Meteorol. Soc. 2000; 126: 14311454. 10.3402/tellusa.v64i0.17133.
  2. Baker N. Langland R. Diagnostics for evaluating the impact of satellite observations. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. Park S.Xu L.Springer-Verlag: New York, 2009; 475.
  3. Cardinali C. Monitoring the observation impact on the short-range forecast. Q. J. R. Meteorol. Soc. 2009; 135: 239250. 10.3402/tellusa.v64i0.17133.
  4. Cardinali C. Pezzulli S. Andersson E. Influence matrix diagnostics of a data assimilation system. Q. J. R. Meteorol. Soc. 2004; 130: 27672786. 10.3402/tellusa.v64i0.17133.
  5. Cardinali, C and Prates, F. 2011. Performance measurement with advanced diagnostic tools of all-sky microwave imager radiances in 4D-Var. Q. J. R. Meteorol. Soc. 137(661): 20382046. DOI:10.3402/tellusa.v64i0.17133.
  6. Daescu D. On the sensitivity equations of four-dimensional variational (4D-Var) data assimilation. Mon. Weather Rev. 2008; 136: 30503065. 10.3402/tellusa.v64i0.17133.
  7. Daescu D. Todling R. Adjoint estimation of the variation in model functional output due to the assimilation of data. Mon. Weather Rev. 2009; 137: 17051716. 10.3402/tellusa.v64i0.17133.
  8. Daescu D. Todling R. Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters. Q. J. R. Meteorol. Soc. 2010; 136: 20002012. 10.3402/tellusa.v64i0.17133.
  9. Gelaro R. Zhu Y. Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models. Tellus. 2009; 61A: 179193.
  10. Gelaro R. Langland R. Pellerin S. Todling R. The THORPEX observation impact intercomparison experiment. Mon. Weather Rev. 2010; 138: 40094025. 10.3402/tellusa.v64i0.17133.
  11. Golub G. H. Van Loan C. F. Matrix Computations3rd ed. The Johns Hopkins University Press: Baltimore MD, 1996
  12. Hogan T. Rosmond T. The description of the navy operational global atmospheric prediction systems spectral forecast model. Mon. Weather Rev. 1991; 119: 17861815.
  13. Kang, W and Xu, L. 2009a. Computational analysis of control systems using dynamic optimization. arXiv. 0906.0215v2.
  14. Kang, W and Xu, L. 2009b. A quantitative measure of observability and controllability. In: IEEE Proceedings of Conference on Decision and Control. Shanghai, China. 6413–6418.
  15. Krener, A.J and Ide, K. 2009. Measures of unobservability. In: IEEE Proceedings of Conference on Decision and Control. Shanghai, China. 6401–6406.
  16. Langland R. Baker N. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus. 2004; 56A: 189201.
  17. Li H. Kalnay E. Miyoshi T. Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 2009; 135: 523533. 10.3402/tellusa.v64i0.17133.
  18. Liu J. Kalnay E. Estimating observation impact without adjoint model in an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 2008; 134: 13271335. 10.3402/tellusa.v64i0.17133.
  19. Liu J. Kalnay E. Miyoshi T. Cardinali C. Analysis sensitivity calculation in an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 2009; 135: 18421851. 10.3402/tellusa.v64i0.17133.
  20. Polak E. Optimization: Algorithms and Consistent Approximations. Springer: New York, 1997
  21. Rosmond, T. 1997. A Technical Description of the NRL Adjoint Modeling System. Naval Research Laboratory Publication, NRL/MR/7532/97/7230, NRL, Monterey, CA.
  22. Tang, K, Wang, B, Kang, W and Chen, B. M. 2011. Minimum time control of helicopter UAVs using computational dynamic optimization. In: IEEE Proceedings of International Conference on Control & Automation, Santiago, Chile. 848–852.
  23. Tremolet Y. Computation of observation sensitivity and observation impact in incremental variational data assimilation. Tellus. 2008; 60A: 964978.
  24. Xu L. Daley R. Towards a true 4-dimensional data assimilation algorithm: application of a cycling representer algorithm to a simple transport problem. Tellus. 2000; 52A: 109128.
  25. Xu, L, Langland, R, Baker, N and Rosmond, T. 2006. Development and testing of the adjoint of NAVDAS-AR. 9–13 October. In: Proceedings of the Seventh International Workshop on Adjoint Applications in Dynamic Meteorology. Obergurgl, Austria. 57–58.
  26. Xu L. Rosmond T. Daley R. Development of NAVDAS-AR: formulation and initial tests of the linear problem. Tellus. 2005; 57A: 546559.
  27. Xu L. Rosmond T. Goerss J. Chua B. Toward a weak constraint operational 4D-Var system: application to the Burger's equation. Meteorologische Zeitschrift. 2007; 16(6): 113. 10.3402/tellusa.v64i0.17133.
Language: English
Page range: 17133 - 17133
Submitted on: Jan 2, 2012
Accepted on: Sep 11, 2012
Published on: Dec 1, 2012
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2012 Wei Kang, Liang Xu, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.