Collaborative Research: Observed and Future Dynamically Downscaled Estimates of Precipitation Associated with Mesoscale Convective Systems

合作研究：与中尺度对流系统相关的降水的观测和未来动态缩小估计

基本信息

批准号：
1637244
负责人：
Russ Schumacher
金额：
$ 8.65万
依托单位：
Colorado State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637244&HistoricalAwards=false
关键词：
Collaborative Research Observed Future Dynamically

项目摘要

A mesoscale convective system (MCS) is a collection of thunderstorms organized on a larger scale than the storms it contains, in which the individual thunderstorms act in concert to generate the atmospheric motion that organizes and sustains the system. These large storm systems produce extreme weather including hail, floods, and tornados, but they also make an important contribution to water resources over the eastern two thirds of the continental US (CONUS) during the growing season. This project seeks to understand MCS behavior in an aggregate sense, including the long-term contribution of MCS precipitation to the overall water balance of the CONUS and the importance of year-to-year variability in MCS activity for anomalously wet (flood) or dry (drought) conditions. A key tool for conducting the research is the Weather Services International (WSI) National Operational Weather radar (NOWrad) data set, a 20-year record (currently 1996-2015) created from the National Weather Service radar stations which provide continuous near-total coverage of the CONUS. A primary goal of the project is to develop and apply an automated procedure to detect and track MCSs in the radar data. The algorithm identifies MCSs as contiguous or semi-contiguous features in radar maps over an area of at least 100km along the system's major axis exceeding a threshold reflectivity value. MCS tracking is complicated by the the tendency of MCSs to split and merge as they propagate, and the algorithm incorporates a method for identifying mergers and splits. A further issue is that large regions of intense precipitation can occur in frontal cyclones and landfalling hurricanes, and a classification scheme is necessary to distinguish these regions from MCSs. A machine learning technique to perform this classification is developed using expert judgement to train a random forest classifier (RFC) scheme. Further expert judgement is solicited through a survey which invites the research community to participate in the development and validation of the tracking and classification schemes. The catalog of MCS events and their characteristics (intensity, duration, structure, etc) is then used to study MCS seasonality, interannual variability, and contribution to CONUS rainfall including floods and droughts.Further work uses a global climate model (GFDL-CM3) in combination with a regional convection permitting model (WRF-ARW at 4km horizontal resolution) to simulate MCSs over the CONUS under present-day and projected future climate conditions. The simulations are analyzed according to the tracking and classification schemes developed for the NOWrad data, and the model simulations allow examination of how MCS behavior depends on climatic factors such as tropospheric moisture, soil moisture, atmospheric stability, and large-scale atmospheric circulation.The work has broader impacts due to the importance of MCS rainfall as a water resource for agriculture and the severe weather hazards related to MCS activity. The algorithms and datasets produced for the project will be shared with researchers and operational climatologists and hydrologists through an online portal. In addition, the project supports and trains a graduate student and provides summer support for an undergraduate, thereby providing for the future scientific workforce in this area.

中尺度对流系统（MCS）是比其所包含的暴风雨更大的雷暴集合的集合，其中各个雷暴共同起作用，以产生组织和维持系统的大气运动。这些大型风暴系统会产生极端的天气，包括冰雹，洪水和龙卷风，但在生长季节，它们在美国大陆（Conus）的东部三分之二的水资源也做出了重要贡献。该项目试图从总体意义上理解MCS行为，包括MCS降水对圆锥的整体水平平衡的长期贡献以及MCS活动对异常湿（洪水）或干燥（干旱）或干旱（干旱）条件的年度变异性的重要性。进行研究的一个关键工具是国际天气服务（WSI）国家运营天气雷达（Nowrad）数据集，这是由国家气象服务雷达站创建的20年记录（目前为1996-2015），可提供CONUS的近乎全部覆盖范围。该项目的主要目标是开发和应用自动化过程，以检测和跟踪雷达数据中的MCSS。该算法将MCS识别为沿系统的主要轴至少100公里的雷达图中的连续或半连接特征，超过了阈值反射率值。 MCS跟踪在传播时分裂和合并的趋势使MCS跟踪变得复杂，并且算法结合了一种识别合并和分裂的方法。另一个问题是，在额叶旋风和登陆飓风中可能会发生大量的强降水区，并且必须进行分类方案以将这些区域与MCS区分开。使用专家判断来培训随机森林分类器（RFC）方案的机器学习技术来执行此分类。进一步的专家判断是通过一项调查征求的，该调查邀请研究界参与跟踪和分类方案的开发和验证。然后，使用MCS事件的目录及其特征（强度，持续时间，结构等）来研究MCS季节性，年际可变性以及对Conus降雨的贡献，包括洪水和干旱。未来的气候条件。根据针对NowRAD数据开发的跟踪和分类方案分析模拟，模型模拟允许检查MCS行为如何取决于气候因素，例如对流层水分，土壤水分，大气稳定性和大型大气循环。该工作由于MCS降雨的重要性而导致MC的重要性以及对农业的危害以及对农业的危害以及对农业造成的严重影响，并且对农业的影响更大。为该项目生产的算法和数据集将通过在线门户网站与研究人员，运营气候医生和水文学家共享。此外，该项目还支持和培训研究生，并为本科生提供夏季的支持，从而为该领域的未来科学劳动力提供了支持。