In situ observations under typhoon conditions are sparse and limited. Distributed acoustic sensing (DAS) is an emerging technology that uses submarine optical-fiber (OF) cables to monitor the sea state. Here, we present DAS-based ocean current observations when a super typhoon passed overhead. The microseismic noise induced by ocean surface gravity waves (OSGWs) during Typhoon Muifa (2022) is observed in the ~0.08–0.38 Hz frequency band, with high-frequency (>0.3 Hz) component being tidally modulated. The OSGW propagation along the entire cable is successfully revealed via frequency–wavenumber analysis. Further, a method based on the current-induced Doppler shifts of DAS-recorded OSGW dispersions is proposed to calculate both speeds and directions of horizontal ocean currents. The measured current is consistent with the tidally induced sea-level fluctuations and sea-surface winds observed at a nearby ocean buoy. These observations demonstrate the feasibility of monitoring the ocean current under typhoon conditions using DAS-instrumented cables.

Tropical cyclones (TCs) are one of the most destructive natural disasters, which can bring huge life and economic losses to the global coastal areas. Accurate TC intensity prediction is critical for disaster prevention and loss reduction, but the dynamic processes involved in TCs are complicated and not adequately understood, which make the intensity prediction is still a challenging task. In recent years, several deep-learning (DL)-based methods have been developed for TC prediction by mining TC intensity series or related environmental factors. However, information hidden between the two different data sources is generally ignored. Here, a novel DL-based TC intensity prediction network named Pre_3D is proposed, which aimed to mine of inter- and intra-patterns of TC intensity and related external factors independently by separate feature extraction sub-networks. An MLP network is adopted to achieve adaptive fusion of the two patterns for accurate TCs intensity prediction. TC records from several agencies were used to evaluate generalizability of the proposed framework and extensive experiments were conducted validate its effectiveness. The experimental results demonstrate that the models based on the Pre_3D framework achieved considerable performance. ConvGRU-based Pre_3D yields a significant improvement of over 15% in prediction accuracy in 24 h prediction relative to official agencies.

A tropical cyclone (TC) is a type of catastrophic weather encountered in the tropical or subtropical ocean, and it is of great significance to accurately estimate its intensity. Many estimation methods based on statistics have been proposed, but these methods have obvious problems, such as poor robustness and low accuracy. Therefore, in this study, a TC intensity estimation method is proposed based on satellite image data using the Xception network as a backbone. The main idea of the proposed method is to estimate the TC maximum wind speed by image feature extraction. First, a Laplacian pyramid image fusion method for the infrared (IR) and water vapor (WV) channels of satellite images is adopted to enhance the total amount of information in the basic input data of the model. Second, an optimization strategy for the depth and width of the Xception network model is proposed with the objective of reducing parameter redundancy and improving the estimation accuracy. Third, a multiscale feature extraction module and a multilayer feature fusion module are designed to realize the fusion of different features. In addition, a dual attention module is introduced to allow the model to focus on the key regions of cyclone images. Finally, the proposed network is evaluated on the HURSAT, FY-2, and Gridsat datasets. The results show that, on average, the maximum cyclone wind speed estimation errors, the mean absolute error (MAE), and root mean square error (RMSE), are 8.0% and 11.4% lower than the state-of-the-art models on the three datasets.

The p-wave double-frequency (DF) microseisms generated by super typhoon Lupit (14–26 October 2009) over the western Pacific Ocean were detected by an on-land seismological array deployed in Northeastern China. We applied a frequency-domain beamforming method to investigate their source regions. Comparing with the best-track data and satellite observations, the located source regions of the p-wave DF microseisms, which corresponded to the strongest ocean wave–wave interactions, were found to be comparable to the typhoon centers in the microseismic frequency band of ~0.18–0.21 Hz. The p-wave DF microseisms were probably excited by the nonlinear interaction of ocean waves generated by the typhoon at different times, in good agreement with the Longuet–Higgins theory for the generation of DF microseisms. The localization deviation, which was ~120 km for typhoon Lupit in this study, might depend on the speed and direction of typhoon movement, the geometry of the seismological array, and the heterogeneity of the solid Earth structure. The p-wave DF microseisms generated in coastal source regions were also observed in the beamformer outputs, but with relatively lower dominant frequency band of ~0.14–0.16 Hz. These observations show that the p-wave DF microseisms generated near typhoon centers could be used as a seismic remote sensing proxy to locate and track typhoons over the oceans from under water in a near-real-time and continuous manner.