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

气候变化可以理解为地球辐射收支的变化；辐射收支对对流层上层/平流层下层（UTLS）区域的化学组成很敏感。由于难以在高海拔地区进行高时空测量，对UTLS地区的化学组成在尺度上仍然知之甚少，这对化学模型很重要。深层对流，如夏季在整个美国中部观测到的剧烈雷暴，是气体从地面到UTLS区域的有效输送者，因此是UTLS组成的一个重要不确定性来源。本文的研究主要集中在两个方面，这两个方面对于提高我们对深对流质量输送的认识都具有重要意义：1)改进雷达反射率估计深对流输送的算法；2)变UTLS结构对风暴尺度深对流质量输送的影响。目标1将利用2012年深对流云和化学（DC3）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。在没有双多普勒雷达覆盖或现场化学测量的情况下，开发了仅使用雷达的算法来估计云尺度的对流输送。DC3战役数据的独特之处在于双多普勒雷达覆盖范围与广泛的原位化学测量同时存在，允许对仅雷达算法进行广泛的测试，并导致额外的改进（例如客观风暴成熟度估计）。目标2将利用具有相同风暴的理想云分辨模型来量化不同UTLS结构对深层对流输送的影响。理想化的探测结果代表了在最近的案例研究中观察到的典型UTLS结构，并被假设强烈影响不可逆输送，特别是对流层顶反转和双对流层顶。智能优势：算法开发（目标1）将允许使用NEXRAD雷达网络进行云尺度对流传输估计。以前，云尺度的运输测量只能在重点活动中使用。研究UTLS结构对深层对流输送的影响（目标2）将有助于更好地理解对流层顶区对对流演化和输送的动力学作用。已观测到不同对流层顶结构对运输的影响，但由于观测到的情况下风暴本身（如CAPE、风暴形态）变化很大，因此很难量化这种影响和/或了解其动力学影响。更广泛的影响：使用NEXRAD雷达网络估算美国中部云尺度对流输送（目标1）的能力对于限制化学输送模式中的对流贡献非常重要。以前，运输模式必须依赖卫星测量（至少在x、y、z、t的一个维度上不是云尺度）或飞机或双多普勒测量，这些测量仅限于案例研究。这种对建模社区的反馈将允许对对流传输参数化进行改进。对对流层顶结构对深层对流的影响的更好理解（目标2）对于约束运输模式也很重要，因为对流层顶区域通常在区域模式中只得到很少的表示。本研究将量化改进UTLS表示的需要。这个项目也有教育方面的影响，因为一些研究生和本科生将在项目过程中接受培训。

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.