Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output

Thomas Bøvith, Allan Aasbjerg Nielsen, Lars Kai Hansen, Rashpal S. Gill, Søren Overgaard

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    Abstract

    A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale-space ensemble method is used for classification and the clutter detection method is illustrated on a case of severe sea clutter contaminated radar data. Detection accuracies above 90 % are achieved and using an ensemble classification method the error rate is reduced by 40 %.
    Original languageEnglish
    Title of host publicationProceedings of the IEEE Geoscience and Remote Sensing Symposium (IGARSS) 2006
    PublisherIEEE
    Publication date2006
    ISBN (Print)0-7803-9510-7
    DOIs
    Publication statusPublished - 2006
    EventIEEE International Conference on Geoscience and Remote Sensing Symposium 2006 - Denver, CO, United States
    Duration: 31 Jul 20064 Aug 2006
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4087812

    Conference

    ConferenceIEEE International Conference on Geoscience and Remote Sensing Symposium 2006
    Country/TerritoryUnited States
    CityDenver, CO
    Period31/07/200604/08/2006
    Internet address

    Bibliographical note

    IEEE International Conference on Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006.

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