Microscale and Mesoscale Structures in Convective Marine Boundary Layers

该基金将通过对特殊野外数据的观测研究和理论模型模拟来研究对流海洋边界层的动力学和热力学性质和特征。这些云顶边界层的形成是对寒冷的大陆气团的反应，这些气团平流到相对温暖的水域上，导致对流混合，并形成位于大气两公里范围内的中尺度细胞对流的大型多边形阵列。这种对流模式可能以二维带或街道的形式存在，但通常（在下游）分解成三维单元。在过去的三十年中，通过各种测量手段，包括雷达、飞机和特殊野外观测计划中的卫星，积累了大量的大气对流研究观测数据。从这些数据中获得了描述行星边界层结构和动力学的统计参数。这些参数通常以时间或空间平均的形式从有限长度的几个（通常来自一个）记录中计算，而理论研究使用集合平均，假设对无限数量的随机函数实现进行平均。只有当底层随机函数是平稳的（齐次的）时，才能用在足够大的时间或空间段上的平均值来近似集合平均。由于数据的非平稳性或非均匀性以及平均距离不足，目前的气象研究往往导致统计上不可靠的推断。由于这些原因，在本项目下，将采用最近开发的综合统计方法重新审查在几个实地方案中收集的数据集，该方法提供统计参数及其统计可靠性的估计。大涡模拟（LES）模型是另一个强大的工具，它有助于从理论上理解大气对流的复杂流体力学。再一次，有一个获得统计可靠结果的问题，这个问题将在本项目中通过将上述统计方法从随机过程扩展到随机场，并通过使用蒙特卡罗技术近似集合平均来解决。此外，还将介绍关于pbl中尺度对流的新LES模式的发展和LES模式数据的统计分析。重点将放在具有冰粒和雪降水的对流pbl的物理性质上。模型结果将与来自实地项目的统计可靠结果进行比较。

9419927 Agee This grant will address the dynamic and thermodynamic properties and characteristics of convective marine boundary layers, through both observational study of special field data and theoretical model simulations. These cloud-topped boundary layers form in response to cold continental air masses that advect out over relatively warmer water, resulting in convective mixing and the formation of large polygonal arrays of mesoscale cellular convection that reside within the first one of two kilometers of the atmosphere. This convective pattern may exist as two- dimensional bands or streets, but often (downstream) resolves itself into three dimensional cells. During the past three decades a wealth of observational data for atmospheric convection studies has been accumulated from measurements by various means, including radar, aircraft, and satellites in special field observation programs. From the data, statistical parameters were obtained describing the structure and dynamics of the planetary boundary layers (PBL). These parameters are commonly computed in the form of temporal or spatial averages from a few (often from one) records of limited length, while theoretical studies operate with ensemble averages which assume averaging over an infinite number of realizations of random functions. Ensemble averages can be approximated by the averages taken over sufficiently large time or space segments only if the underlying random function is stationary (homogeneous). Current meteorological studies often result in statistically unreliable inference because of nonstationarity or inhomogeneity of data and inadequate averaging distances. For these reasons, under this project, data sets collected during several field programs will be revisited by employing a recently developed comprehensive statistical method that provides estimates of both statistical parameters and their statistical reliability. Large eddy simulation (LES) modeling is anoth er powerful tool that helps provide a theoretical understanding of the intricate fluid mechanics of atmospheric convection. Once again there is a problem of obtaining statistically reliable results, which will be addressed in this project by extending the above statistical method from random processes to random fields, and by approximating ensemble averages using Monte Carlo techniques. New LES model development of mesoscale convection in PBLs and proper statistical analysis of the LES model data will also be performed. The focus will be on the physical properties of convective PBLs with ice particles and snow precipitation. Model results will be compared with statistically reliable findings from field programs.