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

气候变化可以被理解为地球辐射收支的变化;辐射收支对对流层上部/平流层下部（UTLS）区域的化学组成很敏感。UTLS区域的化学组成在对化学模型重要的尺度上仍然知之甚少，因为在高海拔地区难以获得高时空测量。深对流，如在整个美国中部的夏季观测到的严重雷暴，是从地面到UTLS区域的气体的有效运输者，因此，是UTLS成分不确定性的重要来源。研究集中在两个主要目标上，这两个目标对于提高我们对深对流质量输送的理解都很重要：1）利用雷达反射率估计深对流输送的算法的改进，和2）可变UTLS结构对风暴尺度深对流物质输送的影响。目标1将利用2012年深对流云和化学（DC 3）活动提供的独特数据集，进一步改进PI研究小组先前开发的仅雷达对流传输算法。 雷达算法的发展，使云尺度的对流输送估计在没有双多普勒雷达覆盖或现场化学测量。DC 3活动数据的独特之处在于，双多普勒雷达覆盖范围与广泛的现场化学测量位于同一地点，允许对仅雷达算法进行广泛测试，并导致额外的改进（例如，客观的风暴成熟度估计）。目标2将利用具有相同风暴的理想化云分辨模式来量化不同UTLS结构对深对流输送的影响。理想化的探测代表原型UTLS结构，已被观察到在最近的案例研究，并假设强烈影响不可逆的运输，特别是对流层顶逆温和双对流层顶。智力优点：算法的发展（目标1）将允许云尺度对流运输估计使用NEXRAD雷达网络。在此之前，云尺度的传输测量仅在有针对性的活动中可用。对UTLS结构对深对流输送影响的研究（目标2）将有助于更好地了解对流层顶区域对对流演变和输送的动力作用。不同的对流层顶结构对运输的影响已经观察到，但它是很难量化的影响和/或理解的动力学影响，风暴本身（如CAPE，风暴形态）变化显着的情况下observed.Broader影响：使用NEXRAD雷达网络来估计云尺度对流运输（目标1）在美国中部的能力是非常重要的约束对流贡献化学运输模型。以前，传输模型必须依赖卫星测量（至少在一个维度，即x、y、z、t上不是云尺度）或飞机或双多普勒测量，这仅限于个案研究。这种对建模界的反馈将有助于改进对流输送参数化。更好地理解对流层顶结构对深对流的影响（目标2）对于约束传输模式也很重要，因为对流层顶区域在区域模式中的代表性往往很差。这项研究将量化改进UTLS表示的必要性。 该项目还具有教育影响，因为在项目过程中将培训几名研究生和本科生。

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.