Multi-sensor rainfall data assimilation using ensemble approaches

Rainfall is a major process transferring water mass and energy from the atmosphere to the surface. Rainfall data is needed over large scales for improved understanding of the Earth climate system. Although there are many instruments for measuring rainfall, none of them can provide continuous global coverage at fine spatial and temporal resolutions. This thesis proposes an efficient methodology for obtaining a probabilistic characterization of rainfall over an extended time period and spatial domain. The characterization takes the form of an ensemble of rainfall replicates, each conditioned on multiple measurement sources. The conditional replicates are obtained from ensemble data assimilation algorithms (Kalman filters and smoothers) based on a recursive cluster rainfall model. Satellite measurements of cloud-top temperatures are used to identify areas where rainfall can possibly occur. A variational field alignment algorithm is used to estimate rainfall advective velocity field from successive cloud-top temperature images. A stable pseudo-inverse improves the stability of the algorithms when the ensemble size is small. The ensemble data assimilation is implemented over the United States Great Plains during the summer of 2004.

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

• Chatdarong, Virat, 1978-

Advisor dc:contributor.advisor

• Dennis McLaughlin.

• 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/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/35493

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/35493