Short-term rainfall prediction using a multifractal model

This study develops a method to predict multifractal measure of temporal rainfall intensity by using Kalman filter, and gives some examples of prediction for generated rainfall. The model for the rainfall generation proposed here is established using a continuous-time, discrete-scale lognormal cascade (CLC) with AR(1) process for each component. This model allows us to simulate rainfall field with the property of the multifractality, which indicates the invariance for scaling of rainfall measure. Through the observation from the synthetic rainfall simulated by this model, Kalman filter is used as the tool for short-term rainfall prediction. We compare different results of predictions made under different simulations and discuss the extensions of this study, prediction for the wet/dry process while looking at real rainfall and issues about space-time rainfall modeling. Keywords: Multifractality, Bayesian estimation, Kalman filter.

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

2003

Author dc:creator

• Chou, Yi-Ju, 1976-

Advisor dc:contributor.advisor

• Daniele Veneziano.

• 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/1721.1/29335

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/29335