Identification, tracking and classification method of mesoscale convective system based on radar composite reflectivity mosaic and deep learning

KeyWord:Deep learning;Mesoscale convective system;Identifying;Tracking;Classification

Abstract:

      Mesoscale convective system (MCS) is the main cause of lots of convective weather, which can lead to severe meteorological and hydrological disasters such as thunderstorms, tornadoes and flash floods. Accurate identification and tracking of MCS and the realization of MCS classification based on the tracking trajectory as well as understanding the MCS features are of great importance for the analysis and forecast of catastrophic weather. Based on the radar composite reflectivity mosaic data in the Beijing-Tianjin-Hebei region from 2010 to 2019, the support vector machines (SVM), the random forest (RF), the Extreme Gradient Boosting (XGBoost) and the deep neural network (DNN) are used to develop an automatic recognition algorithm for MCSs in Beijing-Tianjin-Hebei region. Secondly, the tracking and matching of identified MCS slices are completed according to spatiotemporal overlap tracking, and a tracking database of MCS is established, which includes MCS intensity and spatial and temporal information. Finally, on the basis of distinction between linear convection and non-linear convection and starting from three conceptual models and structural characteristics of thee classical quasi-linear MCSs, i.e., the trailing, leading, and parallel stratiform precipition, an algorithm for quasi-linear MCS classification is established based on the area ratios of stratiform and intense convection on both sides of the approximate major axis of MCS and its movement direction, which is calculated according to tracking trajectory. The recognition of MCS is subsumed under binary classification. Taking POD, FAR, CSI and ACC as evaluation indexes, the DNN model is better than the SVM, RF and XGBoost models in MCS recognition after comprehensive comparison. Spatiotemporal overlap tracking is used to track MCS slices identified by the DNN model. The analyses of two tracking examples suggest that the algorithm used in this research has achieved good tracking results, which further demonstates the accuracy and advantage of deep learning in identifying MCS. The accurate realization of MCS classification including TS, LS and PS provides a technical idea for the life cycle prediction of quasi-linear MCS and objective prediction of disasterous weather, especially the intensity, location and duration of short-term heavy precipitation by combining the movement direction of MCS at a single radar snashot with the distributions of stratiform and intense convection in MCS slices.