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Bias correction of global ensemble precipitation forecasts by Random Forest method
Earth Science Informatics ( IF 2.7 ) Pub Date : 2021-02-09 , DOI: 10.1007/s12145-021-00577-7
Morteza Zarei , Mohsen Najarchi , Reza Mastouri

One of the most important topics in operational applications of precipitation forecasts is their improvement by bias correction methods. In this study, the ensemble precipitation forecasts of six numerical models from the TIGGE (THORPEX Interactive Grand Global Ensemble) database, associated with four basins in Iran for 2008–2018, were extracted and bias-corrected by the Quantile Mapping (QM) and Random Forest (RF) methods. Random Forest is a supervised machine learning algorithm made of an ensemble of decision trees. The results in all four basins demonstrated that most models had better skills in forecasting precipitation depth after bias correction using the RF method, compared to using the QM method and raw forecasts. In the dichotomous evaluation for 5-mm and 25-mm precipitation thresholds, all models improved their performance after bias correction. However, the QM performed slightly better than the RF. In probabilistic evaluations, significant improvements were observed after bias correction using the RF method, compared to using the QM in the models, and the reliability diagrams of the bias-corrected forecasts by the RF concentrated around the 1:1 line in all four basins. In seasonal evaluation, models had better probabilistic forecasts in autumn and winter than in spring and summer, and showed better scores in the lower tercile category than in the middle and upper tercile categories. In general, the improvement of a model’s performance after bias correction with the Random Forest method shows the importance of this method for operational application.



中文翻译:

用随机森林法校正全球总体降水预报的偏差。

降水预报业务应用中最重要的主题之一是通过偏差校正方法对其进行改进。在这项研究中,提取了TIGGE(THORPEX互动全球全球合奏)数据库中与伊朗四个盆地相关的2008-2018年六个数值模型的集合降水预报,并通过分位数图谱(QM)和随机法对它们进行了偏差校正。森林(RF)方法。随机森林(Random Forest)是一种由决策树集成而成的有监督的机器学习算法。在所有四个盆地中的结果表明,与使用QM方法和原始预测相比,使用RF方法校正后,大多数模型在预测降水深度方面具有更好的技巧。在五毫米和25毫米降水阈值的二分法评估中,偏差校正后,所有模型均改善了性能。但是,QM的性能略好于RF。在概率评估中,与模型中的QM相比,使用RF方法进行偏差校正后观察到显着改善,并且通过RF进行偏差校正的预测的可靠性图集中在所有四个盆地的1:1线附近。在季节评估中,模型在秋季和冬季比春季和夏季具有更好的概率预测,并且在低等位度类别中的得分高于中部和高度纬度类别的得分。通常,使用随机森林方法进行偏差校正后,模型性能的提高表明了该方法对操作应用的重要性。在概率评估中,与模型中的QM相比,使用RF方法进行偏差校正后观察到显着改善,并且通过RF进行偏差校正的预测的可靠性图集中在所有四个盆地的1:1线附近。在季节评估中,模型在秋季和冬季比春季和夏季具有更好的概率预测,并且在低等位度类别中的得分高于中部和高度纬度类别的得分。通常,使用随机森林方法进行偏差校正后,模型性能的提高表明了该方法对操作应用的重要性。在概率评估中,与模型中的QM相比,使用RF方法进行偏差校正后观察到显着改善,并且通过RF进行偏差校正的预测的可靠性图集中在所有四个盆地的1:1线附近。在季节评估中,模型在秋季和冬季比春季和夏季具有更好的概率预测,并且在低等位度类别中的得分高于中部和高度纬度类别的得分。通常,使用随机森林方法进行偏差校正后,模型性能的提高表明了该方法对操作应用的重要性。RF进行的偏差校正预测的可靠性图集中在所有四个盆地的1:1线附近。在季节评估中,模型在秋季和冬季比春季和夏季具有更好的概率预测,并且在低等位度类别中的得分高于中部和高度纬度类别的得分。通常,使用随机森林方法进行偏差校正后,模型性能的提高表明了该方法对操作应用的重要性。RF进行的偏差校正预测的可靠性图集中在所有四个盆地的1:1线附近。在季节评估中,模型在秋季和冬季比春季和夏季具有更好的概率预测,并且在低等位度类别中的得分高于中部和高度纬度类别的得分。通常,使用随机森林方法进行偏差校正后,模型性能的提高表明了该方法对操作应用的重要性。

更新日期:2021-02-09
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