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

中尺度对流系统（英语：Mesoscale convective system，简称MCS）是一系列雷暴的集合，其规模比其所包含的风暴更大，其中单个雷暴协同行动，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包括冰雹、洪水和龙卷风在内的极端天气，但在生长季节，它们也对美国大陆东部三分之二地区的水资源做出了重要贡献。 该项目旨在从总体意义上了解MCS行为，包括MCS降水对CONUS总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）条件下逐年变化的重要性。 进行研究的一个关键工具是国际气象服务组织（WSI）的国家业务天气雷达（NOWrad）数据集，这是由国家气象局雷达站创建的20年记录（目前为1996-2015年），这些雷达站提供了对美国大陆的连续几乎全部覆盖。该项目的主要目标是开发和应用一种自动程序来检测和跟踪雷达数据中的MCS。 该算法将MCS识别为沿系统主轴沿着至少100 km区域内雷达图中超过阈值反射率值的连续或半连续特征。 MCS跟踪是复杂的MCS分裂和合并的趋势，因为它们传播，该算法采用了一种方法来识别合并和分裂。另一个问题是，锋面气旋和登陆飓风可能会出现大范围的强降水，因此需要一个分类方案来区分这些地区和MCS。 一个机器学习技术来执行这种分类开发使用专家判断训练随机森林分类器（RFC）计划。通过一项调查征求进一步的专家判断，这项调查邀请研究界参与追踪和分类计划的制定和验证。 MCS事件目录及其特征（强度，持续时间，结构等），然后用来研究MCS的季节性，年际变化，以及对CONUS降雨的贡献，包括洪水和干旱。进一步的工作使用全球气候模式（GFDL-CM 3）与区域对流允许模式相结合（WRF-ARW，水平分辨率4公里），以模拟美国大陆上的MCS在现在和预测的未来气候条件下。 根据为NOWrad数据开发的跟踪和分类方案分析了模拟，并且模型模拟允许检查MCS行为如何取决于气候因素，如对流层湿度，土壤湿度，大气稳定性，和大型-由于MCS降雨作为农业水资源的重要性以及与MCS有关的严重天气危害，活动 为该项目制作的算法和数据集将通过一个在线门户网站与研究人员和业务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工作者提供支持。

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.