Summary The Hunga Tonga-Hunga Ha'apai volcano eruption resulted in propagation of tsunamis globally. Atmospheric pressure disturbances and tsunamis were recorded in Lingding Bay, China, situated more than 9000 km from the volcano. We studied the features of tsunamis in Lingding Bay and its surrounding areas by using records from tide gauges and meteorological stations. Lamb waves were observed in the bay approximately 8, 44, and 80 h after the volcanic eruption. The first and second tsunami waves arrived approximately 11 and 45–47 h following the eruption, respectively, indicating consistency with the arrival time of Lamb waves. In addition, wavelet and Fourier analyses were applied to the sea level records to investigate the frequency characteristics. The ratio of the tsunami spectra to the background spectra for two tsunami waves was calculated as the source spectra. The source spectra of two tsunami waves were mostly of the same shape, with dominant periods of ∼17 and ∼46 min. Our results provide information for theoretical investigation of the Tonga tsunami event. More efforts should be devoted to relevant research on the generating mechanism and early warning of tsunamis from non-seismic origins.

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Clouds are a significant factor in regional climates and play a crucial role in regulating the Earth’s water cycle through the interaction of sunlight and wind. Meteorological agencies around the world must regularly observe and record cloud data. Unfortunately, the current methods for collecting cloud data mainly rely on manual observation. This paper presents a novel approach to identifying ground-based cloud images to aid in the collection of cloud data. However, there is currently no publicly available dataset that is suitable for this research. To solve this, we built a dataset of surface-shot images of clouds called the SSC, which was overseen by the Macao Meteorological Society. Compared to previous datasets, the SSC dataset offers a more balanced distribution of data samples across various cloud genera and provides a more precise classification of cloud genera. This paper presents a method for identifying cloud genera based on cloud texture, using convolutional neural networks. To extract cloud texture effectively, we apply Gamma Correction to the images. The experiments were conducted on the SSC dataset. The results show that the proposed model performs well in identifying 10 cloud genera, achieving an accuracy rate of 80% for the top three possibilities.