Optimizing and verifying an ensemble-based rainfall model
Name
166274029-MIT.pdf
Description
Full printable version
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7.42 MB
Format
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Checksum (MD5)
3e84b2f28586934762aae078fc3b85ef
Author(s)
Friedman, Sara Hargrove
Advisor(s)
Dennis McLaughlin and Dara Entekhabi.
Date Issued
2007
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.
Includes bibliographical references (p. 63-64).
Subjects
Civil and Environmental Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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