2026-09-19T05:54:53Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/389532022-01-13T07:54:23Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Dennis McLaughlin and Dara Entekhabi.
Friedman, Sara Hargrove
Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
2007-09-28T13:17:54Z
2007-09-28T13:17:54Z
2007
2007
http://hdl.handle.net/1721.1/38953
166274029
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.
Includes bibliographical references (p. 63-64).
In this thesis, I modified, optimized, and verified the stochastic Recursive Cluster-point Rainfall model of Chatdarong (2006). A novel error metric allows comparison of the stochastic ensemble of rainfall image forecasts to a single observation (radar) image. The error metric forgives position errors and provides a flexible framework for assessing how well the model works vis-a-vis a set of image measures, including the distribution of rainfall intensities over the domain at different scales. The error metric is used in various forms to perform ad hoc optimization of the model parameters and to verify the ensemble forecast in a probabilistic framework. Verification results show that the optimized model is limited in its ability to create truly realistic rainfall patterns. Despite the model's limitations, it has unique applicability to ensemble-based rainfall data assimilation.
by Sara Hargrove Friedman.
S.M.
64 p.
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Civil and Environmental Engineering.
Optimizing and verifying an ensemble-based rainfall model
Thesis
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