基于ML树算法的地面风预测与预报

IF 1.9 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
M. H. ElTaweel, S. C. Alfaro, G. Siour, A. Coman, S. M. Robaa, M. M. Abdel Wahab
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引用次数: 0

摘要

这项研究的重点是可靠的地面风预报对各个部门的重要性,特别是能源生产。传统的数值天气预报模型正面临着局限性和复杂性的增加,导致机器学习模型的发展作为替代或补充。本研究分为两个阶段。在第一阶段,使用ERA5数据库评估不同特征组合和两种基于树的算法预测开罗地面风特征(速度和方向)的长期性能。XGBoost算法的性能略优于随机森林算法,特别是在与适当的特征选择相结合时。即使在训练结束三年后,结果仍然很好,RMSE为0.59 m/s, rRMSE为17%,R2为0.84。第二阶段评估多变量方法在一周内不同时间范围(1-12小时)风速演变的预测能力。预测结果与1 h时间范围内的观测结果非常吻合,RMSE为0.35 m/s, rRMSE为7.6%,R2为0.98,超过或与其他文献结果相当。然而,随着滞后时间的增加,RMSE(3、6和12 h分别为0.86、1.14和1.51 m/s)和rRMSE(3、6和12 h分别为18.7%、24.8%和32.9%)也增加,R2降低(0.86、0.79和0.60)。此外,风的变化幅度被低估了。为了解决这种偏差,提出了一种简单的校正方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction and forecast of surface wind using ML tree-based algorithms

Prediction and forecast of surface wind using ML tree-based algorithms

This study focuses on the importance of reliable surface wind forecasts for various sectors, particularly energy production. Traditional numerical weather prediction models are facing limitations and increasing complexity, leading to the development of machine learning models as alternatives or supplements. The research consists of two stages. In the first stage, the ERA5 database is used to evaluate the long-term performance of different combinations of features and two tree-based algorithms for predicting surface wind characteristics (speed and direction) in Cairo. The XGBoost algorithm slightly outperforms the Random Forest algorithm, especially when combined with appropriate feature selection. Even three years after the training period, the results remain very good, with an RMSE of 0.59 m/s, rRMSE of 17%, and R2 of 0.84. The second stage assesses the multivariate approach's ability to forecast wind speed evolution at different time horizons (1–12 h) during a week characterized by significant wind dynamics. The forecasts demonstrate excellent agreement with observations at a 1-h time horizon, with an RMSE of 0.35 m/s, rRMSE of 7.6%, and R2 of 0.98, surpassing or comparable to other literature results. However, as the time lag increases, the RMSE (0.86, 1.14, and 1.51 m/s for 3, 6, and 12 h, respectively) and rRMSE (18.7%, 24.8%, and 32.9% for 3, 6, and 12 h, respectively) also increase, while R2 decreases (0.86, 0.79, and 0.60). Furthermore, the wind variations' amplitude is underestimated. To address this bias, a simple correction method is proposed.

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来源期刊
Meteorology and Atmospheric Physics
Meteorology and Atmospheric Physics 地学-气象与大气科学
CiteScore
4.00
自引率
5.00%
发文量
87
审稿时长
6-12 weeks
期刊介绍: Meteorology and Atmospheric Physics accepts original research papers for publication following the recommendations of a review panel. The emphasis lies with the following topic areas: - atmospheric dynamics and general circulation; - synoptic meteorology; - weather systems in specific regions, such as the tropics, the polar caps, the oceans; - atmospheric energetics; - numerical modeling and forecasting; - physical and chemical processes in the atmosphere, including radiation, optical effects, electricity, and atmospheric turbulence and transport processes; - mathematical and statistical techniques applied to meteorological data sets Meteorology and Atmospheric Physics discusses physical and chemical processes - in both clear and cloudy atmospheres - including radiation, optical and electrical effects, precipitation and cloud microphysics.
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