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

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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.