Collaborative Research: Understanding Downdrafts in Deep Convection

合作研究：了解深层对流中的下沉气流

• 批准号: 2149354
• 负责人: Christopher Nowotarski
• 金额: $ 37.55万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149354&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Downdrafts; Deep

Improving the prediction of thunderstorms, their impacts on global climate patterns, and their ability to produce severe weather relies on fundamental knowledge of the processes that contribute to vertical motions in these storms. Recently, attention has been focused on upward motions in storms, known as updrafts. Downward motions in storms, which are called downdrafts, have received comparatively little attention. This research will address the aforementioned knowledge gap related to downdrafts. The project targets understanding the influence of atmospheric properties (e.g., temperature, moisture, and changes in the wind direction and speed with height) on the forces that drive downdrafts, the height at which downdrafts originate, and how large they become. This will be accomplished through theoretical analysis and idealized simulations. The knowledge gained from this work will allow for improvement in how downdrafts are represented in weather and climate models. Thus, this study will benefit scientific communities and the general public by improving the fundamental understanding of thunderstorms, improving forecasting of severe thunderstorm and precipitation hazards, and improving climate prediction. The inclusion of undergraduate students, graduate students, and a postdoctoral scholar in this research will also have a direct impact on the development and training of future scientists. This project will provide foundational research for improvements in downdraft forecasting and parameterization. The project will deliver on this front in three ways: first, it will uncover the typical origin heights of downdrafts, which are not well understood at present; second, it will solidify the physical basis of downdraft conceptual models, and consequently improve predictions of downdraft accelerations; third, it will demonstrate the direct impact of our improved conceptual models for downdrafts on cumulus parameterization and global climate model performance. These goals will be accomplished through a large suite of idealized large-eddy simulations with varying, realistic base-state thermodynamic and kinematic profiles, analyzed with novel techniques for assessing downdraft properties including layered and targeted passive tracers as well established trajectory analysis techniques. The new Multiple Analytic Plume (MAP) cumulus parameterization will be improved through the addition of a downdraft parameterization that is informed by the idealized simulation findings. The project will also develop an improved sounding-derived parameter for downdraft intensity forecasting that accounts for deviations from parcel theory, including nonhydrostatic vertical perturbation pressure gradient accelerations and entrainment. The foundational research will inform sensitivity experiments in a widely used global climate model to understand the connection between downdrafts and large-scale climate state simulations. Combined, all lines of research will facilitate improved understanding of convective phenomena and forecasts on multiple atmospheric scales.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.

改进雷暴的预测、雷暴对全球气候模式的影响以及雷暴产生恶劣天气的能力，依赖于对这些风暴垂直运动过程的基本了解。 最近，人们的注意力集中在风暴中的向上运动，即上升气流。 风暴中的向下运动（称为下沉气流）相对较少受到关注。 这项研究将解决上述与下沉气流相关的知识差距。 该项目的目标是了解大气特性（例如温度、湿度以及风向和风速随高度的变化）对驱动下沉气流的力量、下沉气流产生的高度以及它们的大小的影响。 这将通过理论分析和理想化模拟来完成。 从这项工作中获得的知识将有助于改进天气和气候模型中下降气流的表示方式。 因此，这项研究将通过提高对雷暴的基本认识、改进对严重雷暴和降水灾害的预报以及改进气候预测来使科学界和公众受益。 本科生、研究生和博士后学者参与这项研究也将对未来科学家的发展和培训产生直接影响。该项目将为改进下沉气流预测和参数化提供基础研究。该项目将通过三种方式实现这一目标：首先，它将揭示目前尚不清楚的下降气流的典型起源高度；其次，它将巩固下沉气流概念模型的物理基础，从而改进对下沉气流加速度的预测；第三，它将展示我们改进的下沉气流概念模型对积云参数化和全球气候模型性能的直接影响。这些目标将通过一整套理想化的大涡模拟来实现，这些模拟具有不同的、现实的基态热力学和运动学曲线，并使用评估下沉气流特性的新技术进行分析，包括分层和有针对性的被动示踪剂以及成熟的轨迹分析技术。 新的多重分析羽流 (MAP) 积云参数化将通过添加根据理想化模拟结果提供的下沉气流参数化进行改进。 该项目还将开发一种改进的探测衍生参数，用于下沉气流强度预测，该参数可以解释与地块理论的偏差，包括非静水垂直扰动压力梯度加速度和夹带。这项基础研究将为广泛使用的全球气候模型中的敏感性实验提供信息，以了解下降气流与大规模气候状态模拟之间的联系。 综合起来，所有研究领域将有助于提高对多个大气尺度的对流现象和预报的理解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。