Improving Subseasonal-to-Seasonal forecasts in predicting the occurrence of extreme precipitation events over the contiguous U.S. using machine learning models
Improving Subseasonal-to-Seasonal forecasts in predicting the occurrence of extreme precipitation events over the contiguous U.S. using machine learning models
复制标题
使用机器学习模型改进次季节到季节的预测，预测美国本土极端降水事件的发生
DOI：
10.1016/j.atmosres.2022.106502
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发表时间：
2023
影响因子：
5.5
通讯作者：
Cheng, Chuntian
中科院分区：
文献类型：
--
作者：
Zhang, Lujun;Yang, Tiantian;Gao, Shang;Hong, Yang;Zhang, Qin;Wen, Xin;Cheng, Chuntian
分季节(S2S)尺度的降水预报是在更大范围内辅助水资源规划和决策的有价值的信息。但是，原始的S2S降水预报能力相当有限，这阻碍了进一步的水文应用。以往的许多研究都是从集合“均值”的角度对S2S降水预报进行验证，而现有的从集合分布的角度评估和提高S2S预报预报极端降水事件的能力的研究却鲜有报道。这项研究的目的是改进S2S预报，以预测邻近美国(CONUS)每周99%以上的极端降水事件的发生。使用了随机森林分类器(RF)，并包括了其他预报变量(即地面气温、500亿帕斯卡的位势高度和850百万帕斯卡的位势高度)，以对美国宇航局戈达德地球观测系统模型第五版(GEOS5)的原始S2S预报进行后处理。考察了不同的射频训练输入和射频超参数敏感度分析。我们发现：(1)将S2S降水预报作为唯一的输入输入到RF中，预报质量只在第1周显著提高；(2)当在RF训练中加入额外的预报变量时，预报技能在第2周后的较长提前期略有提高；(3)调整RFS的最大树深，并加入额外的预报变量作为RF的输入，在所有提前期都能提高预报技巧。总之，这项研究证明了RF的应用以及附加预报变量在改进S2S极端降水预报方面的有效性，这可能对洪水和河流集合预报有潜在的帮助。采用集合探测概率(EPOD)、集合虚警率(EFAR)、集合临界成功指数(ECSI)、Brier Sill评分(BSS)和接收器工作特征曲线下面积(AUROC)等多种统计指标，对不同试验情景下S2S极端降水预报的预报性能进行了综合评价。
The precipitation forecasts at the Subseasonal-to-Seasonal (S2S) scale are valuable information to assist water resources planning and decision-making at an extended range. But the raw S2S precipitation forecasts are rather limited regarding their predictive skills, which hinders further hydrological applications. Many previous studies were carried out to validate the S2S precipitation forecasts from the perspective of the ensemble “mean”, while existing study, which evaluates and improves the ability of S2S forecasts in predicting extreme precipitation events with their ensemble spreads, is rarely reported. This study aims to improve the S2S forecasts in predicting the occurrence of weekly extreme precipitation events above 99% over the contiguous United States (CONUS). The Random Forest Classifiers (RF) were employed and additional forecasts variables (i.e., surface air temperature, geopotential heights at 500 hPa and 850 hPa) were included to post-process the raw S2S forecasts from the NASA's Goddard Earth Observation System model version five (GEOS5). Different RF training inputs and RF hyperparameter sensitivity analysis are examined. We found that(1)using S2S precipitation forecast as the only inputs to RF, the forecast quality is improved significantly only at week 1; (2) When additional forecasts variables are included in RF training, the forecast skill tend to improve slightly at longer lead times after weeks 2; (3) The tunning of the maximum tree depth of RFs combine with the inclusion of additional forecasts variables as inputs to RF can improve the forecasts skill at all lead times over CONUS. In short, this study demonstrated the effectiveness of the application of RF as well as the effectiveness of additional forecast variables in improving the S2S extreme precipitation forecasts, which could be potentially useful for flood and river ensemble forecasting. Multiple statistical metrics, including the ensemble probability of detections (EPOD), ensemble false alarm ratios (EFAR), ensemble critical success index (ECSI), Brier Sill Score (BSS), and Area Under the Receiver Operating Characteristics Curves (AUROC) are employed for a comprehensive evaluation of the predictive performances of S2S extreme precipitation forecasts under different experiment scenarios.