Journal
REMOTE SENSING
Volume 14, Issue 20, Pages -Publisher
MDPI
DOI: 10.3390/rs14205205
Keywords
deep learning; typhoon intensity; spatio-temporal model; rolling forecast; ConvLSTM
Categories
Funding
- National Natural Science Foundation of China [42192564]
- National Key R&D Program of China [2019YFA0606701]
- Strategic Priority Research Program of Chinese Academy of Sciences [XDB42000000, XDA20060502]
- Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) [GML2019ZD0306]
- National Postdoctoral Program of Innovative Talents [BX2021324]
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This paper proposes a novel model called TITP-Net to improve typhoon intensity forecasting, and conducts a series of experiments to demonstrate its effectiveness.
Typhoons can cause massive casualties and economic damage, and accurately predicting typhoon intensity has always been a hot topic both in theory and practice. In consideration with the spatial and temporal complexity of typhoons, machine learning methods have recently been applied in typhoon forecasting. In this paper, we attempt to improve typhoon intensity forecasting by treating it as a spatio-temporal problem in the deep learning field. In particular, we propose a novel typhoon intensity forecasting model named the Typhoon Intensity Spatio-temporal Prediction Network (TITP-Net). The proposed model takes multidimensional environmental variables and physical factors of typhoons into account and fully extracts the information from the datasets by capturing spatio-temporal dependencies with a spatial attention module, which includes two-dimensional and three-dimensional convolutional operations. A series of experiments with a comprehensive framework by using TITP-Net are conducted. The MAEs of the forecasts with 18, 24, 36 and 48 h lead time obtain a significant improvement by 7.02%, 6.53%, 6.25% and 5.37% compared with some existing deep learning models and dynamical models from official agencies.
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