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

HBCU-卓越研究：使用自动特征识别和跟踪了解大气潮湿对流和组织

• 批准号: 1832121
• 负责人: Xiaowen Li
• 金额: $ 29.98万
• 依托单位: Morgan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1832121&HistoricalAwards=false
• 关键词: HBCU; Excellence; Research; Understanding; Atmospheric

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.

对流上升气流核心及其生命周期是大气湿对流的基本过程。积云参数化是全球气候预测的关键组成部分。该研究的最终目标是使用基于物理的时变对流上升气流核心概念来改进积云参数化。 PI 和 Co-PI 之间的密切合作将增加新的研究领域（大气科学）并加强摩根州立大学当前的研究和教学（计算机科学），摩根州立大学是一所 HBCU（历史悠久的黑人学院和大学），最近被指定为马里兰州卓越的公立城市研究大学。该研究中包含教育内容，一名研究生和一名本科生将在摩根州立大学的 STEM（科学、技术、工程和数学）领域获得支持和培训。相干上升气流核心是对流热量和湿气传输的引擎。然而，之前还没有从拉格朗日的角度对对流上升气流核心进行系统的研究。该项目将利用现有的计算机图像识别和机器学习算法，使其适应高分辨率云解析模型模拟，以自动识别上升气流核心特征并在其整个生命周期中对其进行跟踪。将进行三组逐渐复杂的模拟：触发单对流的系综模拟、准平衡态模拟以及使用观测到的大规模强迫的案例研究。对流上升气流核心特征，如尺寸、深度、寿命和空间分布，将从这些模拟中得出。扰乱环境条件（例如稳定性、水蒸气、风切变）的模型敏感性测试将揭示对上升气流核心特性的环境控制。还将在上升气流核心的背景下通过其聚集、合并和分裂来研究对流组织。 尽管在当前项目的框架内无法立即实现，但最终目标是利用本研究中建立的时变对流核心概念来改进全球气候预测中的积云参数化。通过该项目，将编制并公开一个包含数千个对流上升气流核心及其生命周期的数据库，以供进一步研究。这是一项跨学科研究，利用信息科学中现有的数据挖掘和机器学习算法，并将其应用于嵌入对流的3D风场。识别和跟踪上升气流核心是一种新颖的方法，将为湿对流及其组织的动力学和物理学提供新的见解。时间相关的上升气流核心概念有可能用于取代当前 GCM 积云参数化中的稳态羽流模型，从而允许具有物理基础概念的与尺度无关的积云参数化。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。