** Progress in Earth and Planetary Science is the official journal of the Japan Geoscience Union, published in collaboration with its 51 society members.
** Progress in Earth and Planetary Science is partly financially supported by a Grant-in-Aid for Publication of Scientific Research Results to enhance dissemination of information of scientific research.
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Research
Atmospheric and hydrospheric sciences
Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model
Matsuoka D, Nakano M, Sugiyama D, Uchida S
Tropical cyclogenesis, Cloud resolving atmospheric model, Deep learning, Convolutional neural network
Detection result of tropical cyclones and precursors using deep learning (POD = 88.9% and FAR = 9.8%).
We propose a deep learning approach for identifying tropical cyclones (TCs) and their precursors. Twenty year simulated outgoing longwave radiation (OLR) calculated using a cloud-resolving global atmospheric simulation is used for training two-dimensional deep convolutional neural networks (CNNs). The CNNs are trained with 50,000 TCs and their precursors and 500,000 non-TC data for binary classification. Ensemble CNN classifiers are applied to 10 year independent global OLR data for detecting precursors and TCs. The performance of the CNNs is investigated for various basins, seasons, and lead times. The CNN model successfully detects TCs and their precursors in the western North Pacific in the period from July to November with a probability of detection (POD) of 79.9–89.1% and a false alarm ratio (FAR) of 32.8–53.4%. Detection results include 91.2%, 77.8%, and 74.8% of precursors 2, 5, and 7 days before their formation, respectively, in the western North Pacific. Furthermore, although the detection performance is correlated with the amount of training data and TC lifetimes, it is possible to achieve high detectability with a POD exceeding 70% and a FAR below 50% during TC season for several ocean basins, such as the North Atlantic, with a limited sample size and short lifetime.
