Multi-sensor large scale land surface data assimilation using ensemble approaches

One of the ensemble Kalman filter's (EnKF) attractive features in land surface applications is its ability to provide distributional information. The EnKF relies on normality approximations that improve its efficiency but can also compromise the accuracy of its distributional estimates. The effects of these approximations are evaluated by comparing the conditional marginal distributions and moments estimated by the EnKF to those obtained from an SIR particle filter, which gives exact solutions for large ensemble sizes. The results show that overall the EnKF appears to provide a good approximation for nonlinear, non-normal land surface problems. A difficulty in land data assimilation problems results from the high dimensionality of states created by spatial discretization over large computational grids. The high dimensionality can be reduced by exploiting the fact that soil moisture field may have significant spatial correlation structure especially after extensive rainfall while it may have local structure determined by soil and vegetation variability after prolonged drydown. This is confirmed by SVD of the replicate matrix produced in an ensemble forecasting experiment. Local EnKF's are suitable for problems during dry periods but give less accurate results after rainfall.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Zhou, Yuhua, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Dennis McLaughlin and Dara Entekhabi.

• Civil and Environmental Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/34610
• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Identifier URI

http://dspace.mit.edu/handle/1721.1/34610

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/34610