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dc.contributor.authorMao, Yiwen
dc.contributor.authorSorteberg, Asgeir
dc.date.accessioned2021-08-04T12:52:30Z
dc.date.available2021-08-04T12:52:30Z
dc.date.created2021-01-15T09:53:26Z
dc.date.issued2020
dc.identifier.issn0882-8156
dc.identifier.urihttps://hdl.handle.net/11250/2766240
dc.description.abstractA binary classification model is trained by random forest using data from 41 stations in Norway to predict the precipitation in a given hour. The predictors consist of results from radar nowcasts and numerical weather predictions. The results demonstrate that the random forest model can improve the precipitation predictions by the radar nowcasts and the numerical weather predictions. This study clarifies whether certain potential factors related to model training can influence the predictive skill of the random forest method. The results indicate that enforcing a balanced prediction by resampling the training datasets or lowering the threshold probability for classification cannot improve the predictive skill of the random forest model. The study reveals that the predictive skill of the random forest model shows seasonality, but is only weakly influenced by the geographic diversity of the training dataset. Finally, the study shows that the most important predictor is the precipitation predictions by the radar nowcasts followed by the precipitation predictions by the numerical weather predictions. Although meteorological variables other than precipitation are weaker predictors, the results suggest that they can help to reduce the false alarm ratio and to increase the success ratio of the precipitation prediction.en_US
dc.language.isoengen_US
dc.publisherAMSen_US
dc.titleImproving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Foresten_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2020 American Meteorological Societyen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1175/WAF-D-20-0080.1
dc.identifier.cristin1871826
dc.source.journalWeather and forecastingen_US
dc.source.pagenumber2461-2478en_US
dc.identifier.citationWeather and forecasting. 2020, 35 (6), 2461-2478.en_US
dc.source.volume35en_US
dc.source.issue6en_US


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