Deep Convective Transport to the Upper-Troposphere/Lower-Stratosphere

到对流层上层/平流层下层的深对流输送

• 批准号: 0918010
• 负责人: Gretchen Mullendore
• 金额: $ 33.32万
• 依托单位: University of North Dakota Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0918010&HistoricalAwards=false
• 关键词: Deep; Convective; Transport; Upper; Troposphere

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The gases in the upper troposphere and lower stratosphere (UT/LS) have a significant effect on the chemical and radiative budgets of the global atmosphere, crucial to understanding climate change. To understand the chemical makeup of the UT/LS region, a much better understanding of deep convective transport is needed. Transport in deep convection is unique in its ability to entrain constituents from the near surface and rapidly transport them to the UT/LS region where they are detrained. The variability of entrainment and detrainment in storms is not captured in current climate model convective transport parameterizations.This study proposes to address these problems with a tripartite approach. First, using dual-Doppler velocity data from field campaigns, the observed detrainment profile will be derived for a suite of storms covering a range of storm morphologies and background environments. Second, radar reflectivity data from the same field campaigns will be used to look for relationships between velocity-derived detrainment profiles and the reflectivity fields. The goal of the second part of the proposal is to define an algorithm for processing radar reflectivity observations that will allow for estimates of convective mass transport based on radar reflectivity observations over any region covered by the weather radar network. Third, cloud-resolving models will be used to assess the quality of reflectivity-based transport estimates for specific storms. Using the combination of radar reflectivity observations and modeling, a climatology of convective transport over the central United States will be compiled.Improvements in understanding and modeling of deep convective transport resulting from this study will directly benefit many complementary modeling efforts and improve understanding of convective transport of gases into the UT/LS. Ultimately, these results can be used to reduce uncertainty in climate prediction models, a critical need considering the climate challenges facing humanity.

该奖项是根据2009年《美国回收与再投资法》（公法111-5）资助的。对流层上层和下层平流层（UT/LS）的气体对全球大气的化学和辐射预算具有重大影响，对于理解气候变化至关重要。要了解UT/LS区域的化学构成，需要更好地了解深对流运输。深度对流的运输是其从近表面夹带成分并迅速运输到ut/ls区域的能力的独特之处。在当前气候模型对流传输参数化中未捕获风暴中夹带和损害的变异性。这项研究建议通过三方方法解决这些问题。首先，使用来自现场活动的双多普勒速度数据，将在涵盖一系列风暴形态和背景环境的一系列风暴中得出观察到的损害概况。其次，将使用来自同一字段活动的雷达反射率数据来寻找速度衍生的损失概况与反射率字段之间的关系。该提案第二部分的目的是定义用于处理雷达反射率观察的算法，该算法将允许根据天气雷达网络覆盖的任何区域的雷达反射率观测值估算对流质量传输。第三，将云解析模型用于评估特定风暴的基于反射率的运输估计的质量。将结合雷达反射率观察和建模的结合，将汇编在美国中部的对流运输的气候。在理解和建模这项研究的深对流运输方面的改进将直接受益进入UT/LS的气体。最终，这些结果可用于减少气候预测模型中的不确定性，考虑到人类面临的气候挑战，这是一个关键的需求。