摘要
一位新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑在一份新的报告中讨论了机器学习的研究结果。根据NewsRx记者从德国伊尔梅瑙发回的新闻报道，研究表明：“湍流参数化仍将是公里尺度地球系统模型的必要组成部分。在相对边界层中，诸如潜在温度和湿度等保守性质的平均垂直梯度近似为零，Standa RD ANSATZ通过涡扩散系数将湍流通量与平均垂直梯度联系起来，必须通过大气边界层中典型的对称上升和下降气流的质量通量参数来扩展。这项研究的财政支持来自科学和创新部。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning are discuss ed in a new report. According to news reporting originating from Ilmenau, German y, by NewsRx correspondents, research stated, “Turbulence parametrizations will remain a necessary building block in kilometer-scale Earth system models. In con vective boundary layers, where the mean vertical gradients of conserved properti es such as potential temperature and moisture are approximately zero, the standa rd ansatz which relates turbulent fluxes to mean vertical gradients via an eddy diffusivity has to be extended by mass-flux parametrizations for the typically a symmetric up- and downdrafts in the atmospheric boundary layer.” Financial support for this research came from Ministerio de Ciencia e Innovacion .
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