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dc.contributor.authorVan de Vyver, H.
dc.contributor.author, Roulin, E.
dc.coverage.temporal21st century
dc.date2009
dc.date.accessioned2016-03-07T16:17:03Z
dc.date.accessioned2021-12-09T09:54:07Z
dc.date.available2016-03-07T16:17:03Z
dc.date.available2021-12-09T09:54:07Z
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/8832
dc.descriptionThis paper presents an application of scale-recursive estimation (SRE) used to assimilate rainfall rates within a storm, estimated from the data of two remote sensing devices. These are a ground-based weather radar and a spaceborne microwave cross-track scanner. The rain rate products corresponding to the latter were provided by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management. In our approach, we operate directly on the data so that it is not necessary to consider a predefined multiscale model structure. We introduce a simple and computationally efficient procedure to model the variability of the rain rate process in scales. The measurement noise of the radar is estimated by comparing a large number of data sets with rain gauge data. The noise in the microwave measurements is roughly estimated by using upscaled radar data as a reference. Special emphasis is placed on the specification of the multiscale structure of precipitation under sparse or noisy data. The new methodology is compared with the latest SRE method for data fusion of multisensor precipitation estimates. Applications to the Belgian region show the relevance of the new methodology.
dc.languageeng
dc.publisherIRM
dc.publisherKMI
dc.publisherRMI
dc.relation.ispartofseriesJournal of Geophysical Research 114
dc.titleScale-recursive estimation for merging precipitation data from radar and microwave cross-track scanners
dc.typeArticle
dc.subject.frascatiEarth and related Environmental sciences
dc.audienceGeneral Public
dc.audienceScientific
dc.subject.freedata fusion
dc.subject.freemultiscale model
dc.subject.freeremote sensing
dc.source.issueJournal of Geophysical Research 114
dc.source.pageD08104
Orfeo.peerreviewedNot pertinent


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