Geometric harnessing of precipitation records: reexamining four storms from Iowa City

Complex geometries often present in hydrologic data sets such as precipitation records have been difficult to model in their totality using classical stochastic methods. In recent years, we have developed extensions of a deterministic procedure, the fractal-multifractal (FM) method, whose patterns s...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2013-05, Vol.27 (4), p.955-968
Main Authors: Huang, Huai-Hsien, Puente, Carlos E., Cortis, Andrea
Format: Article
Language:English
Subjects:
Aquatic Pollution
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Earth and Environmental Science
Earth Sciences
Environment
Fractals
Geometrical optics
Math. Appl. in Environmental Science
Original Paper
Physics
Probability Theory and Stochastic Processes
Rainfall measurement
Statistics for Engineering
Stochastic control theory
Storms
Waste Water Technology
Water Management
Water Pollution Control
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Summary:Complex geometries often present in hydrologic data sets such as precipitation records have been difficult to model in their totality using classical stochastic methods. In recent years, we have developed extensions of a deterministic procedure, the fractal-multifractal (FM) method, whose patterns share fine details and textures of individual data sets in addition to the usual key statistical properties. This work discusses our latest efforts at encoding four geometrically distinct storms gathered in Iowa City with parameters found running a modified particle swarm optimization procedure. The results reaffirm the capabilities of the FM method as all storms are closely fitted within measurement errors. All sets may be encoded with a compression ratio exceeding 350:1, have a maximum error in cumulative distribution less than 2.5 %, and closely preserve the autocorrelation, power spectrum, and multifractal spectrum of the records.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-012-0617-6