A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity
A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity
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一种基于机器学习的大西洋飓风季前预报的包围预测优化算法
DOI：
10.3390/atmos12040522
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发表时间：
2021-04
期刊：
影响因子：
2.9
通讯作者：
Xia Sun;Lianqi Xie;S. Shah;Xipeng Shen
中科院分区：
文献类型：
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作者：
Xia Sun;Lianqi Xie;S. Shah;Xipeng Shen
在本研究中，使用不同的预测变量组合构建了九种不同的统计模型，包括带有和不带有预测预测变量的模型。采用多种机器学习 (ML) 技术，通过选择表现最好的集成成员并确定每个集成成员的权重来优化集成预测。机器学习优化集成 (ML-OE) 预测是根据简单平均集成 (SAE) 预测进行评估的。结果表明，对于个别集合成员和 SAE 凭借出色技能预测的响应变量（例如大西洋热带气旋计数），SAE 的性能与最佳 ML-OE 结果相当。然而，对于个体集合成员建模不佳的响应变量（例如大西洋和墨西哥湾的主要飓风计数），ML-OE 预测通常显示出比个体模型预测和 SAE 预测更高的技能得分。然而，当所有模型都表现出一致的偏差时，SAE 和 ML-OE 都无法改进响应变量的预测。结果还表明，增加集合成员的数量并不一定会带来更好的集合预测。最佳的集合预测来自模型的最佳组合子集。
In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models.