Observations and Modeling of Orographic Cumulus Development Using Digital Imaging and Data Cataloguing

本项目将重点研究地形积云的发展，特别是从浅对流到深对流的转变及其伴随的热力剖面的改变。西南沙漠地形积云的形成为探讨这些问题提供了理想的试验环境。对流在通常空气清澈的山顶上发展，因此初始位置很明确。人们可以对云前环境进行采样，并描述对流引起的变化。地表强迫（潜热和感热）是足够强的，因此即使在边缘不稳定的条件下，垂直剖面的改变也可能导致最终的深对流。这些因素导致了与初始、浅对流、从浅对流到深对流的转变以及深对流事件之间的恢复时间相关的各种时间尺度。该项目将依赖于亚利桑那州立大学开发的现有图像分析技术和数据编目工具的改进，以自动识别云边界并跟踪体积随时间的变化。本项目通过基于色调的像素分割，使用三角网格对云体进行变换、分割和建模，迭代识别和自动变形底层3D模型，以表示体积随时间的变化，并使用迭代反馈来改进这一过程，来解决这个问题。知识价值：目标是扩展对积云发展的基本方面的理解。特别是，首席研究员将在相似理论、经典概念模型和高分辨率数值模拟的背景下关注单个炮塔、羽流和热气流的行为。他们将研究积云夹带/夹带的机制，从浅对流到深对流转变的时间，以及在不同切变、稳定性和湿度剖面下积云与环境的相互作用。通过自动从图像中提取定量信息，他们将能够处理大量案例，并扩展和概括以前在这一主题上的工作结果。更广泛的影响：收集的信息将用于西南部沙漠的预报员，特别是亚利桑那州南部的预报员。在高架地形上对流边界层的演变在很大程度上是未知的。不同环境条件下对流发展的汇编将使预报员能够将山谷的条件与引发风暴的地区的条件联系起来。本项目收集的数据将有助于设计和调整数值天气预报、中尺度和云解析过程模式研究以及大尺度、一般环流和气候模式的云参数化。最后，要开发的工具和技术应该推广到其他动态体积表征和跟踪问题。

This project will focus on the development of orographic cumulus, and in particular, the transition from shallow to deep convection and the accompanying modification of the thermodynamic profile. The formation of orographic cumulus over in the desert southwest offers an ideal test environment to explore these issues. The convection develops over the tops of the mountains in generally clear air, so the initial location is well defined. One can sample the pre-cloud environment and characterize modifications due to the convection. The surface forcing (latent and sensible heat) is sufficiently strong so that even in marginally unstable conditions, modification of the vertical profile may result in eventual deep convection. These factors result in a wide variety of time scales associated with onset of the initial, shallow convection, transition from shallow to deep convection and recovery time between deep convective events. This project will rely on refinement of existing image analysis techniques and data cataloguing tools developed at Arizona State University to automatically identify cloud boundaries and track volumetric changes over time. This project approaches the problem by using pixel segmentation based on hue to transform, segment, and model the cloud volume using triangular mesh, iteratively identifying and automatically morphing the underlying 3D model to represent volumetric changes over time, and using iterative feedback to refine the process. Intellectual Merit: The goal is to extend understanding of fundamental aspects of cumulus development. In particular, the Principal Investigators will focus on the behavior of individual turrets, plumes and thermals in the context of similarity theory, classical conceptual models and those emerging from high resolution numerical simulations. They will examine the mechanics of cumulus entrainment/detrainment, timing of the transition from shallow to deep convection and interaction of cumulus with the environment under varying shear, stability and moisture profiles. By extracting quantitative information from the images automatically, they will be able to process large numbers of cases and extend and generalize the results of previous work on this subject.Broader Impact: The collected information will be of use to operational forecasters in the desert southwest, and to those in southern Arizona in particular. The evolution of the convective boundary layer over the elevated terrain is largely unexplored. A compilation of convective development under different environmental conditions will allow forecasters to relate conditions in the valley to those in the regions where storms are triggered. The data collected during this project will be useful in designing and tuning cloud parameterizations for numerical weather prediction, meso- and cloud resolving process model studies and large-scale, general circulation and climate models. Finally, the tools and techniques to be developed should be generalizable to other dynamic volume characterization and tracking problems.