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An adaptive neural network scheme for radar rainfall estimation from WSR-88D observations

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dc.contributor.authorChandrasekar, V., author
dc.contributor.authorLiu, Hongping, author
dc.contributor.authorXu, Gang, author
dc.contributor.authorAmerican Meteorological Society, publisher
dc.date.accessioned2007-01-03T08:11:00Z
dc.date.available2007-01-03T08:11:00Z
dc.date.issued2001
dc.description.abstractRecent research has shown that neural network techniques can be used successfully for ground rainfall estimation from radar measurements. The neural network is a nonparametric method for representing the relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The effectiveness of the rainfall estimation by using neural networks can be influenced by many factors such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, season change, location change, and so on. In this paper, a novel scheme of adaptively updating the structure and parameters of the neural network for rainfall estimation is presented. This adaptive neural network scheme enables the network to account for any variability in the relationship between radar measurements and precipitation estimation and also to incorporate new information to the network without retraining the complete network from the beginning. This precipitation estimation scheme is a good compromise between the competing demands of accuracy and generalization. Data collected by a Weather Surveillance Radar—1988 Doppler (WSR-88D) and a rain gauge network were used to evaluate the performance of the adaptive network for rainfall estimation. It is shown that the adaptive network can estimate rainfall fairly accurately. The implementation of the adaptive network is very efficient and convenient for real-time rainfall estimation to be used with WSR-88D.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationLui, Hongping, V. Chandrasekar, and Gang Xu, An Adaptive Neural Network Scheme for Radar Rainfall Estimation from WSR-88D Observations, Journal of Applied Meteorology (Boston, Mass.: 1988) 40, no. 11 (November 2001): 2038-2050.
dc.identifier.urihttp://hdl.handle.net/10217/68058
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2001 American Meteorological Society.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleAn adaptive neural network scheme for radar rainfall estimation from WSR-88D observations
dc.typeText

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