Abstract
Severe convective weather is a catastrophic weather that can cause great harm to the public. One of the key studies for meteorological practitioners is how to recognize severe convection weather accurately and effectively, and it is also an important issue in government climate risk management. However, most existing methods extract features from satellite data by classifying individual pixels instead of using tightly integrated spatial information, ignoring the fact the clouds are highly dynamic. In this paper, we propose a new classification model, which is based on image segmentation of deep learning. And it uses U-net architecture as the technology platform to identify all weather conditions in the datasets accurately. As heavy rainfall is one of the most frequent and widespread server weather hazards, when the storms come ashore with high speed of wind, it makes the precipitation time longer and causes serious damage in turn. Therefore, we suggest a new evaluation metric to evaluate the performance of detecting heavy rainfall. Compared with existing methods, the model based on Himawari-8 dataset has a better performance. Further, we explore the representations learned by our model in order to better understand this important dataset. The results play a crucial role in the prediction of climate change risks and the formulation of government policies on climate change.
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Yuan, M., Chai, Z., Zhao, W. (2019). Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_12
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