Recognition and forecast method for rainstorm caused by train effect

Abstract

The invention discloses a recognition and forecast method for a rainstorm caused by the train effect. The method includes the steps that real-time radar data are preprocessed; single bodies subjected to preliminary area screening are subjected to preliminary fitting, interference is eliminated, and a suspected train effect area is searched for; after the suspected train effect area is determined, the train effect can be automatically recognized; through the moving distance of the center of mass of banded echoes of the suspected area and the rotating angle of the axis of the banded echoes of the suspected area, banded echo tracing is performed, and therefore the train effect can be traced; according to the moving inertia of the single bodies, the moving direction, the moving speed and the shape change of the suspected train effect area are extrapolated. The method has the advantages that when the train effect weather occurs, the train effect can be recognized, an alarm is given to a forecaster, extrapolation can be performed according to the train effect weather conditions at various moments, important reference is provided for the forecaster to perform subsequent forecast, and accuracy and rapidity are high.

Description

technical field [0001] The invention relates to the field of meteorology, in particular to a method for identifying and forecasting heavy rain by "train effect". Background technique [0002] Severe convective weather in small and medium-sized cities often causes severe weather phenomena, such as local severe storms, strong winds, hail and other disastrous weather, which seriously threaten the safety of people's lives and properties. Weather radar is the main means of detecting precipitation systems, and the main tool for monitoring and early warning of severe convective weather. The all-weather detection capability of Doppler weather radar and the very rich dynamic information reflecting the evolution process of atmospheric clouds and rain have greatly strengthened the detection and early warning capabilities of small and medium-scale weather systems, and for the monitoring and forecasting of short-term disastrous weather, A solid foundation has been laid. [0003] "Train...

AI Technical Summary

Problems solved by technology

Among them, the TITAN algorithm uses a single reflectivity factor threshold to identify strong convective storms. This algorithm is suitable for the overall storm belt, but the intensity threshold is small, and it cannot extract the internal parameters of the storm and distinguish the fine structure of the storm cluster.

The SCIT algorithm uses seven thresholds to identify the centroid of the storm, which can better identify the storm individual in the storm cluster, but only retains the identification results of high thresholds, and discards the identification results of low thresholds, losing a lot of information. storm body structure letter

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