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: 2149353
• 负责人: John Peters
• 金额: $ 40.86万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard 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=2149353&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.

改善雷暴的预测，它们对全球气候模式的影响以及产生恶劣天气的能力取决于对这些风暴中垂直运动的过程的基本知识。 最近，注意力集中在暴风雨中的向上运动，称为上升气流。 暴风雨中的下降动作被称为低泄，受到相对较少的关注。 这项研究将解决上述与放水有关的知识差距。 该项目的目标是了解大气特性（例如温度，水分以及风向和速度的变化）对驱动下降的力的力，下降气流的高度以及它们变为大小的影响。 这将通过理论分析和理想化的模拟来实现。 从这项工作中获得的知识将可以改善天气和气候模型中的下降气流的代表。 因此，这项研究将通过改善对雷暴的基本理解，改善对严重雷暴和降水危害的预测以及改善气候预测的预测，从而使科学社区和公众受益。 这项研究中的本科生，研究生和博士后学者的包括，还将直接影响未来科学家的发展和培训。该项目将提供基础研究，以改善下降的预测和参数化。该项目将以三种方式在这方面交付：首先，它将揭示出目前尚不清楚的下降气流的典型起源高度；其次，它将巩固下降概念模型的物理基础，从而改善对下降加速度的预测。第三，它将证明我们改进的概念模型对降落的直接模型对积云参数化和全球气候模型性能的影响。这些目标将通过一系列理想化的大型模拟来实现，这些模拟具有不同的，现实的基础状态热力学和运动学曲线，并用新型技术进行了分析，用于评估包括分层和靶向的被动示踪剂在内的下降水域以及良好的轨迹分析技术。 通过添加理想化的仿真结果告知的下降参数化，将改善新的多重分析羽流（MAP）积云参数化。 该项目还将为下降强度预测开发出改进的声音衍生的参数，该参数涉及包裹理论的偏差，包括非静态垂直扰动压力梯度加速度和夹带。基础研究将在广泛使用的全球气候模型中为敏感性实验提供信息，以了解下降气候与大规模气候状态模拟之间的联系。 结合在一起，所有研究方案都将有助于提高对对流现象和对多种大气规模的预测的了解。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估来支持的。