Midlatitude Deep Convective Transport to the Upper-Troposphere and Lower-Stratosphere

中纬度深对流层对流层上层和平流层下层的输送

• 批准号: 1432930
• 负责人: Gretchen Mullendore
• 金额: $ 29.1万
• 依托单位: University of North Dakota Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1432930&HistoricalAwards=false
• 关键词: Midlatitude; Deep; Convective; Transport; Upper

Climate change can be understood as a change in the radiative budget of the earth; the radiative budget is sensitive to the chemical makeup of the upper-troposphere/lower-stratosphere (UTLS) region. The chemical makeup of the UTLS region remains poorly understood at scales important to chemistry models because of the difficulty in getting high temporal and spatial measurements at high altitude. Deep convection, such as the severe thunderstorms observed throughout the central United States in the summer months, is an efficient transporter of gases from the surface to the UTLS region and, therefore, is a significant source of uncertainty in UTLS composition.The research focuses on two primary objectives, both of which are important for improving our understanding of deep convective mass transport: 1) Improvement of the Algorithm to Estimate Deep Convective Transport using Radar Reflectivity, and 2) Impact of Variable UTLS Structure on Storm-Scale Deep Convective Mass Transport. Objective 1 will utilize the unique dataset provided by the 2012 Deep Convective Clouds and Chemistry (DC3) campaign to further improve a radar-only convective transport algorithm previously developed by the PI's research group. The radar-only algorithm was developed to allow cloud-scale convective transport estimates in the absence of dual-Doppler radar coverage or in situ chemical measurements. The DC3 campaign data is unique in that dual-Doppler radar coverage is co-located with a wide range of in situ chemical measurements, allowing extensive testing of the radar-only algorithm, and leading to additional improvements (e.g. objective storm maturity estimates). Objective 2 will utilize an idealized cloud-resolving model with identical storms to quantify the impact of varied UTLS structures on deep convective transport. The idealized soundings represent archetypal UTLS structures that have been observed in recent case studies and are hypothesized to strongly impact the irreversible transport, specifically a tropopause inversion and a double tropopause.Intellectual Merit:The algorithm development (objective 1) will allow for cloud-scale convective transport estimates using the NEXRAD radar network. Previously, cloud-scale transport measurements were only available in focused campaigns. The investigation into the impact of the UTLS structure on deep convective transport (objective 2) will allow for an improved understanding of the dynamical role of the tropopause region on convective evolution and transport. The impacts of varied tropopause structures on transport have been observed, but it is difficult to quantify the impact and/or understand the dynamical influences, as the storms themselves (e.g. CAPE, storm morphology) varied significantly in cases observed.Broader Impacts:The ability to use the NEXRAD radar network to estimate cloud-scale convective transport (objective 1) in the central U.S. is very important for constraining the convective contribution in chemical transport models. Previously, transport models have had to rely on satellite measurements (which are not cloud scale in at least one of dimension, i.e. x,y,z,t) or on aircraft or dual-Doppler measurements, which are limited to case studies. This feedback to the modeling community will allow for improvements to the convective transport parameterizations. The improved understanding of the impact of tropopause structures on deep convection (objective 2) is also important for constraining transport models, because often the tropopause regions is only poorly represented in regional models. This study will quantify the need for improvements to UTLS representation. This project also has educational impacts, as several graduate and undergraduate students will be trained over the course of the project.

气候变化可以理解为地球辐射预算的变化；辐射预算对上层层/下流层（UTLS）区域的化学构成敏感。 UTLS区域的化学构成在对化学模型很重要的尺度上仍然很鲜为人知，因为很难在高海拔高度进行高时空测量和空间测量。深深的对流，例如在夏季在整个美国中部观察到的严重雷暴，是一种有效的气体转运蛋白，从表面到UTLS地区，因此是UTLS组成中不确定性的重要来源。两个主要目标，这两个目标对于提高我们对深对流质量运输的理解至关重要：1）改进算法使用雷达反射率来估计深对流的运输，以及2）可变UTLS结构对风暴量表深对流大众传输的影响。目标1将利用2012年深度对流云与化学（DC3）活动提供的独特数据集，以进一步改善PI研究小组先前开发的纯对流运输算法。 开发了仅雷达算法，以允许在没有双多普勒雷达覆盖范围或原位化学测量的情况下允许云规模的对流传输估计。 DC3广告系列的数据是独一无二的，因为双重多普勒雷达覆盖范围与广泛的原位化学测量值共同分享，从而可以对仅雷达算法进行广泛的测试，并带来其他改进（例如，客观的风暴成熟度估计值）。目标2将利用具有相同风暴的理想化的云解析模型来量化各种UTLS结构对深对流传输的影响。理想化的声音代表了最近在案例研究中观察到的原型UTLS结构，并被认为可以强烈影响不可逆的运输，特别是对流层层面的倒置和双层对流层面。IntlectualForectual功绩：算法开发（目标1）将允许云计算1使用Nexrad雷达网络估计对流传输。以前，云规模的运输测量仅在集中活动中可用。对UTLS结构对深对流运输的影响的调查（目标2）将使人们对对流层顶区域对对流进化和运输的动态作用有了改进的了解。已经观察到了多种对流层顶结构对运输的影响，但是很难量化影响和/或理解动态影响，因为在观察到的情况下，风暴本身（例如斗篷，风暴，风暴形态）差异很大。能力：能力：能力：使用Nexrad雷达网络来估计美国中部的云量表对流运输（目标1）对于限制化学传输模型中的对流贡献非常重要。以前，运输模型必须依靠卫星测量值（至少在尺寸之一，即x，y，z，t）或飞机或双式多普勒测量值的卫星测量值中，这仅限于案例研究。对建模社区的反馈将允许改进对流传输参数化。对对流层载体结构对深对流的影响（目标2）的影响对约束运输模型也很重要，因为对流层顶区域通常仅在区域模型中表示很差。这项研究将量化对UTLS表示的改进的需求。 该项目还具有教育影响，因为在该项目的过程中将接受几名研究生和本科生的培训。