ENSEMBLE DATA ASSIMILATION BASED ON CONTROL THEORY is a collaborative project between the Florida State University and Colorado State University, funded by the National Science Foundation, Collaboration in Mathematical Geosciences.

Duration of the project is Sep 2003 - Aug 2008.

The proposed research addresses the development of new ensemble data assimilation (EnsDA) methodology which efficiently combines the ensemble filtering with control theory. It consists of six major parts:

  1. Developing new EnsDA methodology;
  2. Evaluating the impact of various optimization algorithms in EnsDA, with special emphasis on the nonlinear aspects of optimization algorithm;
  3. Evaluating the impact of nonlinear measurements and non-Gaussian assumption on the quality of the probabilistic EnsDA output;
  4. Comparing forecast error covariance from second order 4-D VAR with same covariance obtained using EnsDA methodology;
  5. Comparing Bayesian particle with EnKF filter and MLEF filter.
  6. Assessing impact of non-linear observation operators on particle filter, MLEF and EnKF filter methods .

RESEARCHERS

Educational Impact

  • A course entitled : ISC-5935-02: Computational Aspects of Data Assimilation, Fall 2007 was taught to 10 graduate students and used MLEF as illustration of advanced ensemble data assimilation methods.

    Syllabus Data Assimilation Class

    • PUBLICATIONS

    Initialization of Ensemble Data Assimilation, Milija Zupanski, Steven Fletcher, I. Michael Navon, Bahri Uzunoglu, Ross P. Heikes, David A. Randall, , Todd D. Ringler and Dacian N. Daescu Tellus A , 58A , 159-170 (2006)

    A Note on Gaussian Resampling Particle Filter, X. Xiong and I.M. Navon and B. Uzunoglu. Tellus A , Vol 58A , 456-460(2006)

    .Predictability, Observations and Uncertainties in Geosciences ,M. Zupanski and I. Michael Navon, Bulletin of the American Meteorological Society , September 2007 Issue , (2007)

    Adaptive Ensemble size reduction and inflation. Bahri Uzunoglu, Steven J Fletcher, M. Zupanski and I.M. Navon , Quarterly Journal of the Royal Meteorological Society , Vol 133, 1281-1294 (2007)

    The Maximum Likelihood Ensemble Filter as a non-differentiable minimization algorithm. Milija Zupanski, I. Michael Navon, and Dusanka Zupanski. Quarterly Journal of the Royal Meteorological Society , Volume 134, Issue 633,April 2008 Part B,1039-1050 (2008)

    Comparison of Sequential data assimilation methods for the Kuramoto-Sivashinsky equation . M. Jardak , I. M. Navon and M. Zupanski Submitted to International Journal for for Numerical Methods in Fluids. , (2008)

    Comparing ensemble data assimilation methods for the shallow water equations model M. Jardak, I.M. Navon and M. Zupanski Paper in progress to be submitted to JGR Atmospheres (2008)

    Data assimilation for Numerical Weather Prediction : a review. , I.M. Navon. Springer Book entitled " Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications". Park, Seon K., Xu, Liang(Eds), 2009, XVIII, 475 p. 326 illus., Hardcover, ISBN: 978-3-540-71055-4 (2008)

    FINAL REPORT Ensemble Data Assimilation System Based on Control Theory M. Zupanski and I.M. Navon (2008)

    Last modified: November 30, 2008 *** Contact us