The Northwest Pacific Ocean (NWP) is one of the most vulnerable regions that has been hit by typhoons. In September 2018, Mangkhut was the 22nd Tropical Cyclone (TC) over the NWP regions (so, the event was numbered as 1822). In this paper, we investigated the highest amplitude ionospheric variations, along with the atmospheric anomalies, such as the sea-level pressure, Mangkhut’s cloud system, and the meridional and zonal wind during the typhoon. Regional Ionosphere Maps (RIMs) were created through the Hong Kong Continuously Operating Reference Stations (HKCORS) and International GNSS Service (IGS) data around the area of Mangkhut typhoon. RIMs were utilized to analyze the ionospheric Total Electron Content (TEC) response over the maximum wind speed points (maximum spots) under the meticulous observations of the solar-terrestrial environment and geomagnetic storm indices. Ionospheric vertical TEC (VTEC) time sequences over the maximum spots are detected by three methods: interquartile range method (IQR), enhanced average difference (EAD), and range of ten days (RTD) during the super typhoon Mangkhut. The research findings indicated significant ionospheric variations over the maximum spots during this powerful tropical cyclone within a few hours before the extreme wind speed. Moreover, the ionosphere showed a positive response where the maximum VTEC amplitude variations coincided with the cyclone rainbands or typhoon edges rather than the center of the storm. The sea-level pressure tends to decrease around the typhoon periphery, and the highest ionospheric VTEC amplitude was observed when the low-pressure cell covers the largest area. The possible mechanism of the ionospheric response is based on strong convective cells that create the gravity waves over tropical cyclones. Moreover, the critical change state in the meridional wind happened on the same day of maximum ionospheric variations on the 256th day of the year (DOY 256). This comprehensive analysis suggests that the meridional winds and their resulting waves may contribute in one way or another to upper atmosphere-ionosphere coupling.

Earthquakes occur all around the world, causing varying degrees of damage and destruction. Earthquakes are by their very nature a sudden phenomenon and predicting them with a precise time range is difficult. Some phenomena may be indicators of physical conditions favorable for large earthquakes (e.g., the ionospheric Total Electron Content (TEC)). The TEC is an important parameter used to detect pre-earthquake changes by measuring ionospheric disturbances and space weather indices, such as the global geomagnetic index (Kp), the storm duration distribution (Dst), the sunspot number (R), the geomagnetic storm index (Ap-index), the solar wind speed (Vsw), and the solar activity index (F10.7), have also been used to detect pre-earthquake ionospheric changes. In this study, the feasibility of the 6th-day earthquake prediction by the deep neural network technique using the previous five consecutive days is investigated. For this purpose, a two-staged approach is developed. In the first stage, various preprocessing steps, namely TEC signal improvement and time-frequency representation-based TEC image construction, are performed. In the second stage, a multi-input convolutional neural network (CNN) model is designed and trained in an end-to-end fashion. This multi-input CNN model has a total of six inputs, and five of the inputs are designed as 2D and the sixth is a 1D vector. The 2D inputs to the multi-input CNN model are TEC images and the vector input is concatenated space weather indices. The network branches with the 2D inputs contain convolution, batch normalization, and Rectified Linear Unit (ReLU) activation layers, and the branch with the 1D input contains a ReLU activation layer. The ReLU activation outputs of all the branches are flattened and then concatenated. And the classification is performed via fully connected, softmax, and classification layers, respectively. In the experimental work, earthquakes with a magnitude of M_w5.0 and above that occurred in Turkey between 2012 and 2019 are used as the dataset. The TEC data were recorded by the Turkey National Permanent GNSS Network-Active (TNPGN-Active) Global Navigation Satellite System (GNSS) stations. The TEC data five days before the earthquake were marked as “precursor days” and the TEC data five days after the earthquake were marked as “normal days”. In total, 75% of the dataset is used to train the proposed method and 25% of the dataset is used for testing. The classification accuracy, sensitivity, specificity, and F1-score values are obtained for performance evaluations. The results are promising, and an 89.31% classification accuracy is obtained.