利用机器学习预测云湍流夹带混合过程

IF 4.4 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES
Sinan Gao, Chunsong Lu, Jiashan Zhu, Yabin Li, Yangang Liu, Binqi Zhao, Sheng Hu, Xiantong Liu, Jingjing Lv
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引用次数: 0

摘要

云与环境之间不同的湍流夹带混合机制对云相关过程至关重要;然而,在天气/气候模型中准确表示夹带混合仍然是一项挑战。本研究利用机器学习(ML)来应对这一挑战。对四种 ML(轻梯度提升机 [LGB]、极端梯度提升、随机森林和支持向量回归)进行了研究和比较。结果发现,LGB 的性能最佳,因此被选来利用显式混合包裹模型的模拟数据了解夹带混合对微物理的影响。与传统的参数化方法相比,训练有素的 LGB 提供了更准确的微物理特性(数量浓度和云滴光谱弥散)。预测的微物理特性对特征的部分依赖性与解释方法确定的物理机制和预期结果非常吻合,从而克服了 "黑箱 "方案的局限性。其基本机制是,较小的数量浓度和较大的频谱散布对应于更不均匀的夹带混合。具体来说,夹带混合后的数量浓度与受夹带混合影响的绝热数量浓度和液态水含量呈正相关,而与绝热体积平均半径呈反相关。夹带混合后的频谱散布与受夹带混合影响的液态水含量、湍流耗散率和夹带空气的相对湿度呈负相关。敏感性分析进一步表明,数量浓度主要由云的微物理特性决定,而频谱散布则同时受云的微物理特性和环境变量的影响。结果表明,LGB 方案有可能在天气/气候模型中加强对夹带混合的表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Using Machine Learning to Predict Cloud Turbulent Entrainment-Mixing Processes

Using Machine Learning to Predict Cloud Turbulent Entrainment-Mixing Processes

Different turbulent entrainment-mixing mechanisms between clouds and environment are essential to cloud-related processes; however, accurate representation of entrainment-mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment-mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the “black box” scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment-mixing. Specifically, number concentration after entrainment-mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment-mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment-mixing is negatively correlated with liquid water content affected by entrainment-mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment-mixing in weather/climate models.

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来源期刊
Journal of Advances in Modeling Earth Systems
Journal of Advances in Modeling Earth Systems METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
11.40
自引率
11.80%
发文量
241
审稿时长
>12 weeks
期刊介绍: The Journal of Advances in Modeling Earth Systems (JAMES) is committed to advancing the science of Earth systems modeling by offering high-quality scientific research through online availability and open access licensing. JAMES invites authors and readers from the international Earth systems modeling community. Open access. Articles are available free of charge for everyone with Internet access to view and download. Formal peer review. Supplemental material, such as code samples, images, and visualizations, is published at no additional charge. No additional charge for color figures. Modest page charges to cover production costs. Articles published in high-quality full text PDF, HTML, and XML. Internal and external reference linking, DOI registration, and forward linking via CrossRef.
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