Forecast Verification and Visualization based on Gaussian Mixture Model Co‐estimation

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dc.contributor.authorWang, Y. H.en_US
dc.contributor.authorFan, C. R.en_US
dc.contributor.authorZhang, J.en_US
dc.contributor.authorNiu, T.en_US
dc.contributor.authorZhang, S.en_US
dc.contributor.authorJiang, J. R.en_US
dc.contributor.editorDeussen, Oliver and Zhang, Hao (Richard)en_US
dc.date.accessioned2015-10-12T13:32:45Z
dc.date.available2015-10-12T13:32:45Z
dc.date.issued2015en_US
dc.description.abstractPrecipitation forecast verification is essential to the quality of a forecast. The Gaussian mixture model (GMM) can be used to approximate the precipitation of several rain bands and provide a concise view of the data, which is especially useful for comparing forecast and observation data. The robustness of such comparison mainly depends on the consistency of and the correspondence between the extracted rain bands in the forecast and observation data. We propose a novel co‐estimation approach based on GMM in which forecast and observation data are analysed simultaneously. This approach naturally increases the consistency of and correspondence between the extracted rain bands by exploiting the similarity between both forecast and observation data. Moreover, a novel visualization and exploration framework is implemented to help the meteorologists gain insight from the forecast. The proposed approach was applied to the forecast and observation data provided by the China Meteorological Administration. The results are evaluated by meteorologists and novel insight has been gained.Precipitation forecast verification is essential to the quality of a forecast. The Gaussian mixture model (GMM) can be used to approximate the precipitation of several rain bands and provide a concise view of the data, which is especially useful for comparing forecast and observation data. The robustness of such comparison mainly depends on the consistency of and the correspondence between the extracted rain bands in the forecast and observation data. We propose a novel co‐estimation approach based on GMM in which forecast and observation data are analysed simultaneously.en_US
dc.description.number6en_US
dc.description.sectionheadersArticlesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12520en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12520en_US
dc.publisherCopyright © 2015 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectWeather Visualizationen_US
dc.subjectVisual Analyticsen_US
dc.subjectVerificationen_US
dc.subjectI.3.3 [Computer Graphics]: Weather Visualization----Verificationen_US
dc.titleForecast Verification and Visualization based on Gaussian Mixture Model Co‐estimationen_US
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