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DOI10.1016/j.atmosres.2020.105026
Forecasting lightning around the Korean Peninsula by postprocessing ECMWF data using SVMs and undersampling
Moon S.-H.; Kim Y.-H.
发表日期2020
ISSN0169-8095
卷号243
英文摘要We use machine learning to generate binary forecasts of the occurrence of lightning within a particular location and time interval. The training data is weather variables found in the forecasts from the European Centre for Medium-range Weather Forecasts, correlated against subsequent lightning reports, for a region containing the Korean Peninsula. Lightning is uncommon, so the amount of data which does not involve lightning tends to swamp the training process. Thus we only consider spatial locations at which lightning frequently occurs, and we also undersample the subset of the remaining data-points which are not associated with lightning. Results from support vector machines and random forests had equitable threat scores of 0.0885 and 0.0828, respectively. The ETS of results from SVMs can be increased to 0.1241 if temporal resolution is reduced by a factor of 2, and 0.1499 if spatial resolution is reduced by a factor of 3. © 2020 Elsevier B.V.
关键词Decision treesLightningSupport vector machinesEquitable threat scoreEuropean centre for medium-range weather forecastsSpatial locationSpatial resolutionTemporal resolutionTime intervalTraining processUnder-samplingWeather forecastingconfidence intervaldata processinglightningmachine learningsamplingspatial resolutionsupport vector machineweather forecastingKorea
语种英语
来源机构Atmospheric Research
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/132424
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Moon S.-H.,Kim Y.-H.. Forecasting lightning around the Korean Peninsula by postprocessing ECMWF data using SVMs and undersampling[J]. Atmospheric Research,2020,243.
APA Moon S.-H.,&Kim Y.-H..(2020).Forecasting lightning around the Korean Peninsula by postprocessing ECMWF data using SVMs and undersampling.,243.
MLA Moon S.-H.,et al."Forecasting lightning around the Korean Peninsula by postprocessing ECMWF data using SVMs and undersampling".243(2020).
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