HBCU-Excellence in Research: Understanding Atmospheric Moist Convection and Organization Using Automatic Feature Identification and Tracking

GSTDTAP

项目编号	1832121
	HBCU-Excellence in Research: Understanding Atmospheric Moist Convection and Organization Using Automatic Feature Identification and Tracking
	Xiaowen Li
主持机构	Morgan State University
项目开始年	2018
	2018-09-01
项目结束日期	2021-08-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	299827(USD)
国家	美国
语种	英语
英文摘要	Convective updraft core and its lifecycle are fundamental processes in atmospheric moist convection. Cumulus parameterization is a critical component in global climate prediction. The ultimate goal of the study is to improve cumulus parameterization using a physically based, time-variant convective updraft core concept. The close collaborations between the PI and Co-PI will add new research field (atmospheric sciences) and strengthen the current research and teaching (computer sciences) at Morgan State University, an HBCU (Historically Black Colleges and Universities) that has been recently designated as Maryland's preeminent public urban research university. An education component is built into the study, where a graduate student and an undergraduate student will be supported and trained in STEM (Science, Technology, Engineering, and Mathematics) field at Morgan State University. Coherent updraft cores are the engine for convective heat and moisture transport. However, systematic study of convective updraft cores from a Lagrangian point of view has not been performed before. This project will take advantages of existing computer image recognition and machine learning algorithms, adapt them to high resolution cloud-resolving model simulations, in order to automatically identify updraft core features and track them throughout their full life cycle. Three sets of progressively more sophisticated simulations will be carried out: ensemble simulations of triggered single convection, quasi-equilibrium state simulation, and case studies using observed large-scale forcing. Convective updraft core characteristics such as their sizes, depths, lifespan and spatial distributions will be derived from these simulations. Model sensitivity tests that perturb environmental conditions, e.g., stability, water vapor, wind shear, will reveal environmental controls on updraft core characteristics. Convection organization will also be studied in the context of updraft cores through their congregation, merging and splitting. Although not immediately achievable in the framework of current project, the ultimate goal is to use the time-variant convective core concept established in this study to improve cumulus parameterization in global climate predictions. A database including thousands of convective updraft cores and their lifecycles will be compiled and made public through this project for further study. This is an interdisciplinary study that takes advantage of existing data mining and machine learning algorithms in information sciences and applies them to 3D wind fields where convection is embedded. Identifying and tracking updraft cores is a novel approach that will provide new insights to dynamics and physics of moist convection and its organization. The time-dependent updraft core concept can potentially be used to replace the steady state plume model in current GCM cumulus parameterizations, allowing for a scale-independent cumulus parameterization with a physical underlying concept. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/73285
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Xiaowen Li.HBCU-Excellence in Research: Understanding Atmospheric Moist Convection and Organization Using Automatic Feature Identification and Tracking.2018.