Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/13620
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dc.contributor.authorMonteiro, Magdapt
dc.contributor.authorCosta, Marcopt
dc.date.accessioned2015-03-13T12:39:33Z-
dc.date.available2015-03-13T12:39:33Z-
dc.date.issued2015-03-
dc.identifier.urihttp://hdl.handle.net/10773/13620-
dc.description.abstractThis work presents a comparative study between two approaches to calibrate radar rainfall in real time. The weather radar provides continuous measurements in real-time which have errors of either meteorological or instrumental nature. Locally, gauge measurements have a greater performance than radar measurements that can be used to improve radar estimates. One way of doing that is via a state space representation associated to the Kalman filter algorithm. In the single- site modeling approach we use the linear calibration model applied in [1] and [3] while the multivariate state-space model proposed in [6] is used in the multiple site approach. This work aims to discuss and compare these two different state space formulations based on the same data set.pt
dc.language.isoengpt
dc.publisherAIP Publishingpt
dc.rightsopenAccesspor
dc.subjectTime series analysispt
dc.subjectstate space modelpt
dc.subjectrainfall estimatespt
dc.subjectweather radarpt
dc.subjectcalibrationpt
dc.titleA comparison between single site modeling and multiple site modeling approaches using Kalman filteringpt
dc.typeconferenceObjectpt
dc.peerreviewedyespt
ua.publicationstatuspublishedpt
ua.event.datesetembro, 2014pt
ua.event.typeconferencept
degois.publication.firstPage110003-1pt
degois.publication.lastPage110003-4pt
degois.publication.locationRhodes, Greecept
degois.publication.titleProceedings of the International Conference on Numerical Analysis and Applied Mathematics 2014 (ICNAAM-2014)pt
degois.publication.volume1648pt
dc.identifier.doi10.1063/1.4912410pt
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