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Random-forest based adjusting method for wind forecast of WRF model
Computers & Geosciences ( IF 4.4 ) Pub Date : 2021-06-07 , DOI: 10.1016/j.cageo.2021.104842
Anxi Wang , Libing Xu , Yi Li , Jianyong Xing , Xingrong Chen , Kewei Liu , Yishuang Liang , Zheng Zhou

Nowadays, machine learning (ML) methods have gained much attention and have been applied in some important related applications in earth science field, including observation data mining, geoscience image recognition, remote sensing image classification and so on. These ML-based applications play important roles in our daily life. However, in meteorological and oceanographic forecast, numerical is still the most popular method. Although researchers have proposed some ML-based prediction methods to overcome the shortcomings of numerical weather forecast methods, the explainability for the forecast result of artificial intelligence (AI) technology is still not as good as numerical weather forecast methods. Therefore, in this paper, we propose a random forest based adjusting method, which introduces AI technology to correct wind prediction results of numerical model. The proposed adjusting method greatly improves the accuracy of forecast results. Furthermore, the physical meanings of parameters in the numerical model are retained in adjusting results. From experimental evaluations, it is obvious that the root mean square error (RMSE) of each feature is reduced greatly. In detail, the average RMSE of 10m wind decreased by more than 45%, and the average RMSE of sea level pressure decreased by more than 50%. It is worth noting that the improvement here is the average of all forecasts for whole region within 7 days.



中文翻译:

基于随机森林的WRF模型风预报调整方法

如今,机器学习(ML)方法受到广泛关注,并已在地球科学领域的一些重要相关应用中得到应用,包括观测数据挖掘、地球科学图像识别、遥感图像分类等。这些基于机器学习的应用程序在我们的日常生活中发挥着重要作用。然而,在气象和海洋预报中,数值仍然是最流行的方法。尽管研究人员提出了一些基于ML的预测方法来克服数值天气预报方法的缺点,但人工智能(AI)技术对预测结果的可解释性仍然不如数值天气预报方法。因此,在本文中,我们提出了一种基于随机森林的调整方法,引入人工智能技术对数值模型的风预测结果进行修正。提出的调整方法大大提高了预测结果的准确性。此外,在调整结果中保留了数值模型中参数的物理意义。从实验评估可以看出,每个特征的均方根误差(RMSE)明显降低了很多。具体来看,10m风平均RMSE下降45%以上,海平面气压平均RMSE下降50%以上。值得注意的是,这里的改进是7天内整个区域所有预测的平均值。从实验评估可以看出,每个特征的均方根误差(RMSE)明显降低了很多。具体来看,10m风平均RMSE下降45%以上,海平面气压平均RMSE下降50%以上。值得注意的是,这里的改进是7天内整个区域所有预测的平均值。从实验评估可以看出,每个特征的均方根误差(RMSE)明显降低了很多。具体来看,10m风平均RMSE下降45%以上,海平面气压平均RMSE下降50%以上。值得注意的是,这里的改进是7天内整个区域所有预测的平均值。

更新日期:2021-06-11
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