Early recognition of convective initiation using 3D U-Net with multi-source data

Lei Han; Xiaoxu Qi; Wei Zhang; Yurong Ge

doi:10.1117/12.2589383

27 January 2021 Early recognition of convective initiation using 3D U-Net with multi-source data

Lei Han, Xiaoxu Qi, Wei Zhang, Yurong Ge

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117201Y (2021) https://doi.org/10.1117/12.2589383
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Accurate recognition of convective initiation (CI) is important to locate severe hazardous weather events. Early identifying CI can provide warning signals so that people can prepare for the coming natural disasters. Modern geostationary satellite and Doppler weather radar can provide high spatial-temporal resolution imageries to monitor CI. In this study, CI refers to Doppler radar image having reflectivity greater than 35dBZ at the first time. This paper presents a deep learning method for early recognition of CI using multi-source observation data, including geostationary satellite and Doppler weather radar imagery. We use the 3D U-Net method which is composed of three-dimensional convolution, pooling, down sampling and up sampling. The North China area is selected as the study domain. The experimental results show that the proposed method can recognize CI effectively while the false alarms still need to be reduced in future work.

Citation Download Citation

Lei Han, Xiaoxu Qi, Wei Zhang, and Yurong Ge "Early recognition of convective initiation using 3D U-Net with multi-source data", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117201Y (27 January 2021); https://doi.org/10.1117/12.2589383

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