基于模态分解和多种智能算法耦合的校正数值天气预报降水预报方法

IF 1.9 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Changqing Meng, Zhihan Hu, Yuankun Wang, Yanke Zhang, Zijiao Dong
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引用次数: 0

摘要

数值天气模型在实现高预测精度方面经常面临重大挑战。为了提高这些模型的预测性能,有人提出了一种集成深度学习算法的解决方案。本文介绍了一种机器学习方法,用于修正气象研究和预测(WRF)模型的数值天气预报结果。首先,使用 WRF 模型模拟金沙江流域的夏季降水。随后,采用自适应噪声稳健经验模式分解法(CEEMDAN)对 WRF 模拟误差进行分解。然后将这些分解后的子序列输入四种机器学习算法和两种元启发式优化算法,以预测误差序列。最后,将预测的误差子序列合并并叠加到 WRF 模拟值上,得到修正后的降水量。研究结果表明,将机器学习算法与 WRF 相结合可显著提高预测精度。与修正前相比,最优模型的相关系数提高了 158%,纳什-苏特克利夫效率(NSE)提高了 149%。这表明,通过深度学习方法修正 WRF 模型可有效提高降水预报精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A forecasting method for corrected numerical weather prediction precipitation based on modal decomposition and coupling of multiple intelligent algorithms

A forecasting method for corrected numerical weather prediction precipitation based on modal decomposition and coupling of multiple intelligent algorithms

Numerical weather models often face significant challenges in achieving high prediction accuracy. To enhance the predictive performance of these models, a solution involving the integration of deep learning algorithms has been proposed. This paper introduces a machine learning approach for correcting the numerical weather forecast results from the Weather Research and Forecasting (WRF) model. Initially, the WRF model is used to simulate summer precipitation in the Jinsha River Basin. Subsequently, the adaptive noise-robust empirical mode decomposition (CEEMDAN) method is employed to decompose WRF simulation errors. These decomposed subsequences are then input into four machine learning algorithms and two metaheuristic optimization algorithms to predict the error sequences. Finally, the predicted error subsequences are merged and superimposed on the WRF simulation values to obtain the corrected precipitation. Research findings demonstrate that the integration of machine learning algorithms with WRF significantly improves prediction accuracy. The correlation coefficient of the optimal model increases by 158%, and Nash-Sutcliffe Efficiency (NSE) increases by 149% compared to before correction. This indicates that correcting the WRF model through deep learning methods effectively enhances precipitation forecasting accuracy.

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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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