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

• 批准号: 1637212
• 负责人: Vittorio Gensini
• 金额: $ 4.95万
• 依托单位: Community College District 502
• 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 2017-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637212&HistoricalAwards=false
• 关键词: Collaborative; Research; Observed; Future; Dynamically

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）是雷暴的集合，其组织尺度大于其所包含的雷暴，其中单个雷暴协同作用，产生组织和维持系统的大气运动。这些大型风暴系统产生冰雹、洪水和龙卷风等极端天气，但在生长季节，它们也对美国大陆东部三分之二的水资源做出了重要贡献。该项目旨在从总体意义上理解MCS的行为，包括MCS降水对CONUS整体水平衡的长期贡献，以及MCS活动在异常潮湿（洪水）或干燥（干旱）条件下的年际变化的重要性。开展这项研究的一个关键工具是国际气象服务（WSI）国家业务气象雷达（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年），这些雷达站提供了连续的几乎完全覆盖CONUS的数据。该项目的主要目标是开发和应用一种自动化程序来检测和跟踪雷达数据中的mcs。该算法将mcs识别为雷达图上沿系统长轴超过阈值反射率值至少100公里的区域内的连续或半连续特征。MCS跟踪由于MCS在传播过程中分裂和合并的趋势而变得复杂，该算法采用了一种识别合并和分裂的方法。另一个问题是，在锋面气旋和登陆飓风中可能出现大面积的强降水，需要一个分类方案来将这些区域与MCSs区分开来。使用专家判断来训练随机森林分类器（RFC）方案，开发了一种机器学习技术来执行这种分类。通过一项调查征求进一步的专家判断，该调查邀请研究界参与跟踪和分类方案的开发和验证。然后利用MCS事件目录及其特征（强度、持续时间、结构等）来研究MCS的季节性、年际变化以及对CONUS降雨（包括洪涝和干旱）的贡献。进一步的工作使用一个全球气候模式（GFDL-CM3）结合一个区域对流允许模式（WRF-ARW， 4公里水平分辨率）来模拟当前和预估未来气候条件下CONUS上空的mcs。根据为NOWrad数据开发的跟踪和分类方案对模拟进行了分析，模式模拟允许检查MCS行为如何依赖于对流层湿度、土壤湿度、大气稳定性和大尺度大气环流等气候因子。由于MCS降雨作为农业水资源的重要性以及与MCS活动相关的恶劣天气灾害，这项工作具有更广泛的影响。为该项目制作的算法和数据集将通过一个在线门户与研究人员、气候学家和水文学家共享。此外，该项目还支持和培训一名研究生，并为一名本科生提供暑期支持，从而为该领域未来的科学劳动力提供支持。