Domain knowledge integration into deep learning for typhoon intensity classification2021 · <title>Abstract</title>In this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon’s eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts. A series of analyses revealed that the data-driven approach using only deep learning has limitations, and the integration of domain knowledge could bring new breakthroughs.
Stationary and Transient Asymmetric Features in Tropical Cyclone Eye with Wavenumber-one Instability: Case Study for Typhoon Haishen (2020) with Atmospheric Motion Vectors from 30-second Imaging2020 · Abstract Dynamics of low-level flows in the eye of Typhoon Haishen (2020) in its late phase of intensification are investigated with a special rapid-scan observation of the Himawari-8 geosynchronous satellite conducted every 30 seconds. This is accomplished by deriving storm-relative atmospheric motion vectors at an unprecedentedly high spatiotemporal resolution by tracking clouds across five consecutive visible-light reflectivity. The overall low-level circulation center was situated several kilometers away from the storm center defined in terms of the inner edge of the lower part of eyewall clouds. The shift direction is rearward the storm translation, consistently with a numerical study of tropical cyclone (TC) boundary layer. Over the analysis period of 10 hours, azimuthal-mean tangential wind around this center was increased at each radius within the eye, and the rotational angular velocity was nearly homogenized. The instantaneous low-level circulation center is found to orbit around the overall circulation center at distances around 5 km. Its orbital angular speed was close to the maximum angular speed of azimuthal-mean tangential winds. This rotating transient disturbance is found to transport angular momentum inward, which explains the tangential wind increase and the angular velocity homogenization in the eye. These features are consistent with an algebraically growing wavenumber-1 barotropic instability, whose impact on TC structures has not been explored. This instability enhances wavenuber-1 asymmetry in ring shaped vorticity, which can be induced by various processes such as translation, environmental shear, and exponential barotropic instability. Therefore, it may appear broadly in TCs to affect wind distribution in their eyes.
