IDEAS home Printed from https://ideas.repec.org/a/bes/jnlasa/v105i491y2010p978-990.html
   My bibliography  Save this article

An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation

Author

Listed:
  • Stroud, Jonathan R.
  • Stein, Michael L.
  • Lesht, Barry M.
  • Schwab, David J.
  • Beletsky, Dmitry

Abstract

No abstract is available for this item.

Suggested Citation

  • Stroud, Jonathan R. & Stein, Michael L. & Lesht, Barry M. & Schwab, David J. & Beletsky, Dmitry, 2010. "An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 978-990.
    • Handle: RePEc:bes:jnlasa:v:105:i:491:y:2010:p:978-990
    as

    Download full text from publisher

    File URL: http://pubs.amstat.org/doi/abs/10.1198/jasa.2010.ap07636
    File Function: full text
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christopher Wikle & Mevin Hooten, 2010. "A general science-based framework for dynamical spatio-temporal models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 417-451, November.
    2. Margaret C Johnson & Brian J Reich & Josh M Gray, 2021. "Multisensor fusion of remotely sensed vegetation indices using space‐time dynamic linear models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 793-812, June.
    3. Hajiha, Mohammadmahdi & Liu, Xiao & Lee, Young M. & Ramin, Moghaddass, 2022. "A physics-regularized data-driven approach for health prognostics of complex engineered systems with dependent health states," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Guillermo Ferreira & Jorge Mateu & Emilio Porcu, 2018. "Spatio-temporal analysis with short- and long-memory dependence: a state-space approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 221-245, March.
    5. S. R. Johnson & S. E. Heaps & K. J. Wilson & D. J. Wilkinson, 2023. "A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    6. Marcin Jurek & Matthias Katzfuss, 2023. "Scalable spatio‐temporal smoothing via hierarchical sparse Cholesky decomposition," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bes:jnlasa:v:105:i:491:y:2010:p:978-990. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.