Research On Identification Algorithm Of Thunderstorm Gale Based On Radar Echo

Thunderstorm gale is a serious and common weather of small-medium scale meteorological disasters.Thunderstorm gale weather are generally caused by a long and narrow squall line composed of multiple thunderstorms or super-monomers.Thunderstorm gale weather have the characteristics of short generation time,high strength,strong destructive power,change fast,and difficult to defense.It causes badly harm to Production,people's lives,transportation and other industries.Compared with the medium-long scale meteorological disaster phenomenon,the identification and prediction of thunderstorm gale is very challenging.Chinese and foreign scholars in the meteorological field have done a lot of research on forecasting and early warning.The research on thunderstorm and windy identification using machine learning and deep learning methods is still in the frontier exploration stage.Therefore,the research of this subject not only has high academic value,but also has urgent practical significance.For the thunderstorm gale identification task,this paper builds a thunderstorm gale sample set based on the radar echo puzzle data and meteorological automatic observatory data of Guangdong Province from 2015 to 2017 provided by Shenzhen Meteorological Bureau,and then performs data preprocessing such as radar echo clutter filtering,noise sample rejection.This paper proposes a thunderstorm gale identification method based on ten traditional machine learning methods.The thunderstorm gale region is identified by extracting a total of ten radar characteristics such as radar combined reflectivity,vertical liquid water content(VIL)and radar echo top height.In order to reduce the misrecognition rate of thunderstorm gale,this paper designs a morphological-based squall line segmentation method,which only uses thunderstorm gale identification for radar echoes in the squall line region,and enhances the recognition rate while effectively improving the recognition rate of thunderstorm gale.In order to make full use of the spatiotemporal characteristics of thunderstorm gale identification problem,this paper combines the advantages of convolutional neural network and recurrent neural network,considering the relationship between time series factors and spatial context of radar images,and designs and implements four thunderstorm gale identification network models.They are a simple convolutional neural network model,a spatial context circular convolutional neural network model,a time-circulating convolutional neural network model,and a spatio-temporal convolutional neural network model that combines the advantages of the two models.The four models are strictly controlled by design experiments.Experimental comparison.At the same time,the deep learning model is compared with ten traditional machine learning methods that use manual extraction features.The experimental results show that methods in this paper have high recognition accuracy in the thunderstorm gale identification problem.The network model designed for thunderstorm gale identification problem combined with the squall line segmentation method is accurate in the detection of thunderstorm gale 5000 test sets.The accuracy rate reached 83.2%.It has obvious advantages over traditional methods in the meteorological field.This paper uses machine learning and deep learning methods to systematically study,experiment and analyze the thunderstorm gale identification problem.Finally,a thunderstorm gale identification display system was set up to streamline the research on thunderstorm gale identification problems.The system in this paper will be deployed to the Shenzhen Meteorological Bureau for use by weather forecasters.