Ms. Lan Sun Profile

Lan Sun

at Guangxi Univ

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Proceedings Article | 15 June 2022 Paper

Research on hail recognition based on multi-layer neural network

Lan Sun

Proceedings Volume 12285, 1228514 (2022) https://doi.org/10.1117/12.2637172

KEYWORDS: Neural networks, Detection and tracking algorithms, Data modeling, Evolutionary algorithms, Radar, Reflectivity, Clouds, Machine learning, Polarization, Network architectures

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In a very humid and unstable air, convective clouds can grow to very high heights, and can produce weather processes such as torrential rain, hail, and thunder and lightning during the strong development stage. These strong storms are either generated individually, or more often in groups associated with weather-scale fronts and mesoscale convergence zones, which can cause large losses of life and property. This paper uses the 2019 Guangdong S-band dual-polarization radar base data and ground hail observation records, and extracts hail (positive samples) and non-hail data (negative samples) from the echo structure characteristics as label data to construct a training data set. Using a multi-layer neural network algorithm, design the hail recognition network architecture, taking the reflectance factor (Z), differential reflectivity factor (ZDR), differential propagation phase shift rate (KDP), correlation coefficient (CC), etc. as input, using 5 layers Neural network, modeling and predicting the hail area. A typical hail case is also used to compare and analyze the recognition effect of the WSR-88D hail recognition algorithm and the multilayer neural network algorithm. The results show that the two methods can predict hail clouds more accurately. The hail area predicted by the network is larger, and the recognition at the lower level is more reliable. Using multi-layer neural network method can improve the effect of hail recognition.

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