Cloud Modeling and Probability Density Functions

云建模和概率密度函数

• 批准号: 0442605
• 负责人: Vincent Larson
• 金额: $ 28.46万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0442605&HistoricalAwards=false
• 关键词: Cloud; Modeling; Probability; Density; Functions

Large-scale numerical models of the atmosphere include parameterizations of microphysical and radiative processes. Such parameterizations require accurate input in the form of grid-resolved fields such as liquid water content, ice water content, and droplet number concentration. These fields are produced by host model equations. Some mesoscale models prognose liquid water using the assumption that there is no thermodynamic variability on scales smaller than the grid box size. This assumption is incompatible with cloud schemes that predict subgrid-scale cloud fraction or any other thermodynamic subgrid variability.In this research, the Principal Investigator seeks to improve the formulation and accuracy of host model equations for liquid, ice, and droplet number that drive microphysics and radiative parameterizations. To improve them, he will investigate the effects of subgrid variability.A principal object of study is the probability density function (PDF) of relevant quantities, such as total water content. Specifically, the PI will (1) use mathematical properties of the relevant PDF to rigorously derive large-scale equations for liquid water content, ice water content, and droplet number concentration; (2) analyze aircraft data to ascertain the shapes of joint PDFs of liquid and ice, and then use the PDFs to close the host model equations; and (3) implement the host model equations in an idealized one-dimensional cloud parameterization.The intellectual merit is twofold: (1) to improve understanding of mixed-phase clouds; and (2) to improve representation of clouds in atmospheric numerical models. If successful, the research may ultimately have implications for cloud parameterizations ranging from simple bulk microphysics schemes to bin microphysics schemes, and for models ranging from general circulation models to cloud resolving models.The research will have two broader impacts: it will help train a postdoctoral research associate, and its results will be made widely available to the scientific community through journal publications and the internet.

大气的大尺度数值模式包括微物理和辐射过程的参数化。 这样的参数化需要以网格解析字段的形式精确输入，例如液态水含量、冰水含量和液滴数量浓度。 这些场由主体模型方程产生。 一些中尺度模式假设在小于网格尺寸的尺度上没有热力学变率，以此来模拟液态水。 这一假设是不兼容的云计划，预测次网格尺度云分数或任何其他热力学次网格variability.In这项研究中，首席研究员试图提高主机模型方程的液体，冰和液滴数，驱动微物理和辐射参数化的制定和准确性。 为了改进它们，他将研究次网格可变性的影响。一个主要的研究对象是相关量的概率密度函数（PDF），如总含水量。 具体来说，PI将（1）利用相关PDF的数学特性，严格推导出液态水含量、冰水含量和液滴数浓度的大规模方程;（2）分析飞机数据，确定液体和冰的联合PDF形状，然后利用PDF闭合主模型方程;（3）在理想化的一维云参数化中实现主模式方程。其智力价值有两个方面：（1）提高对混合相云的理解;（2）提高大气数值模式中云的表示。 如果成功的话，这项研究最终可能会对云参数化产生影响，从简单的体微物理方案到箱微物理方案，以及从大气环流模型到云解析模型。这项研究将产生两个更广泛的影响：它将有助于培养一名博士后研究助理，其结果将通过期刊出版物和互联网广泛提供给科学界。