Yuting Yang

Accurate wind speed prediction is crucial for conserving power resources and enhancing power utilization efficiency. However, deviations from typical wind patterns can introduce errors into predictions, potentially leading to imbalances between wind power supply and demand. Consequently, developing a model to forecast abnormal wind speeds is essential. To address this, we leverage the microcanonical multifractal formalism algorithm to detect abnormal wind speeds. In this paper, we integrate ensemble empirical mode decomposition, phase space reconstruction, and long short-term memory (LSTM) networks to predict these anomalies. Initially, wind speed data is meticulously pre-processed to generate datasets for one-hour, one-day, and non-zero wind speeds. Subsequently, LSTM networks are used to forecast abnormal wind speeds. Evaluations of our methodology across different datasets demonstrate its effectiveness, particularly excelling in one-hour forecasts.

Accurately predicting sea surface temperature (SST) is crucial for marine environmental monitoring and climate research. However, existing ocean model approaches often struggle to capture complex spatiotemporal patterns and are limited by their reliance on thermodynamic equations to impose oceanographic constraints. To address these challenges, we propose a multi-sensor SST prediction model that integrates Long Short-Term Memory (LSTM) networks, convolutional neural networks (CNNs), and an attention mechanism to directly incorporate physical variables such as temperature, salinity, density, and current velocity. By bypassing the need for explicit physical equation constraints, our model effectively learns complex relationships from multi-source data. Experimental results show that our approach significantly improves predictive accuracy across various ocean regions, providing a robust solution for both short-term and long-term SST forecasting.

Ocean front is one typical geophysical phenomenon acting as oases in the ocean for fishes and marine mammals. Accurate ocean-front prediction is critical for fishery and navigation safety. However, the formation and evolution of ocean fronts are inherently non-linear and are influenced by various factors such as ocean currents, wind fields, and temperature changes, making ocean front prediction a considerable challenge. This study proposes a temporal sensitive network named Attention-ConvNet to address this challenge. Ocean fronts exhibit significant multiscale characteristics, requiring analysis and prediction across various temporal and spatial scales. The proposed network designs a Hierarchical Attention Mechanism (HAM) that efficiently prioritizes relevant spatial and temporal information to meet the specific requirement. What is more, the proposed network utilizes a complex Hierarchical Branching Convolutional Network (HBCNet) architecture, which allows our network to leverage the complementary strengths of spatial and temporal information, effectively capturing the dynamic and complex variations of ocean fronts. In general, the network prioritizes and focuses on the most relevant information of front dynamics, which ensures its ability to effectively predict the ocean front. External experiments demonstrate that our network significantly outperforms conventional methods, confirming its capability for precise ocean-front prediction. The codes will be publicly available at https://github.com/yuhudeyue/Ocean-Front-Prediction-Model.

This paper proposes an ocean front database and a method for its construction tailored for studying the dynamic evolution of ocean fronts. Ocean fronts play a crucial role in the interactions between the ocean and atmosphere, affecting the transfer of heat and matter in the ocean. In recent years, research on ocean fronts has emerged as a significant and rapidly evolving area within oceanography. With the development of ocean remote sensing technology, the amount of available ocean remote sensing data has been increasing. However, the potential of this expanding volume of ocean front data remains largely untapped. The lag in data processing technology has hindered research progress in understanding ocean fronts despite the growing amount of data available. To bridge this gap, this paper proposes an ocean front dynamic evolution database along with a method for its construction to further promote research into the variations and interactions of ocean fronts. This is especially relevant for studies utilizing deep learning to explore the dynamic evolution of ocean fronts. Specifically, the proposed database is designed to capture the variation processes of ocean front enhancement and attenuation, as well as the interactions during ocean front splitting and merging. The proposed database construction method allows for the segmentation and extraction of specific ocean fronts of interest from ocean front images. The proposed method is beneficial for analyzing the dynamic evolution between multiple ocean fronts on the same timeline.

By providing valuable data that allow scientists to study various oceanographic characteristics on a global scale, remote sensing techniques have considerably advanced our understanding of ocean fronts. Ocean fronts involve the interaction of water masses with specific physical properties such as temperature, sea color, salinity, and density. In particular, ocean fronts can act as barriers, impeding the movement of water masses and leading to the convergence or divergence of nutrients and marine species. Research on ocean fronts and their impact on marine biodiversity and physical environments has recently become popular. This paper introduces ocean front research progress based on remote sensing images, including research material, methods, limitations, and possible future research directions. The latest research on spatiotemporal variation in ocean fronts has substantially enhanced our understanding of the interaction of water masses with specific physical properties in the ocean.

Marine hydrological elements are of vital importance in marine surveys. The evolution of these elements can have a profound effect on the relationship between human activities and marine hydrology. Therefore, the detection and explanation of the evolution laws of marine hydrological elements are urgently needed. In this paper, a novel method, named Evolution Trend Recognition (ETR), is proposed to recognize the trend of ocean fronts, being the most important information in the ocean dynamic process. Therefore, in this paper, we focus on the task of ocean-front trend classification. A novel classification algorithm is first proposed for recognizing the ocean-front trend, in terms of the ocean-front scale and strength. Then, the GoogLeNet Inception network is trained to classify the ocean-front trend, i.e., enhancing or attenuating. The ocean-front trend is classified using the deep neural network, as well as a physics-informed classification algorithm. The two classification results are combined to make the final decision on the trend classification. Furthermore, two novel databases were created for this research, and their generation method is described, to foster research in this direction. These two databases are called the Ocean-Front Tracking Dataset (OFTraD) and the Ocean-Front Trend Dataset (OFTreD). Moreover, experiment results show that our proposed method on OFTreD achieves a higher classification accuracy, which is 97.5%, than state-of-the-art networks. This demonstrates that the proposed ETR algorithm is highly promising for trend classification.

Ocean eddies, as a ubiquitous phenomenon of the global ocean, are extremely important for ocean energy and material exchanges. Therefore, efficient eddy detection and tracking are crucial for advancing our understanding of ocean dynamics. This work presents a framework for automatic ocean eddy detection and tracking by leveraging state-of-the-art machine learning algorithms. First, we propose a new convolutional neural network model for multieddies detection. This model is capable of extracting accurate boundary information of eddies and fitting the gap between semantic context and sea surface height (SSH). Second, a tracking algorithm is designed to track eddies lasting a number of days and provide visualization of the dynamical processes governing eddies' movements. Finally, we have made our data set publicly available, which is named SCSE-Eddy and can be used as a benchmark to evaluate the performances of artificial intelligence (AI)-based eddy detection methods. The data set covers daily remotely sensed SSH data located in the South China Sea and its eastern sea areas over a period of 15 years. The experimental results show that our methods achieve promising performances compared to existing approaches, especially for the eddies with indistinct geographical border. We believe that this work opens a new avenue for oceanographers to better discover and understand the physical properties of ocean eddies.

Cloud occlusion is a common problem in the satellite remote sensing (RS) field and poses great challenges for image processing and object detection. Most existing methods for cloud occlusion recovery extract the surrounding information from the single corrupted image rather than the historical RS image records. Moreover, the existing algorithms can only handle small and regular-shaped obnubilation regions. This letter introduces a deep convolutional generative adversarial network to recover the RS sea surface temperature images with cloud occlusion from the big historical image records. We propose a new loss function for the inpainting network, which adds a supervision term to solve our specific problem. Given a trained generative model, we search for the closest encoding of the corrupted image in the low-dimensional space using our inpainting loss function. This encoding is then passed through the generative model to infer the missing content. We conduct experiments on the RS image data set from the national oceanic and atmospheric administration. Compared with traditional and machine learning methods, both qualitative and quantitative results show that our method has advantages over existing methods.

Ocean front plays an important role in marine fishery production and biogeochemical cycling. This letter proposes a multiscale deep framework to meet the need for automatic ocean front detection and fine-grained location. The framework mainly focuses on bringing a well-trained deep learning model into front detection and location on the global satellite sea surface temperature image. First, a multiscale scanner is designed to divide the ocean into small areas of different scales. Then, we introduce the deep model to determine that a front has occurred, and translate the global image into binary ones of various grained. Here, an overlapping scanning way is suggested to locate the front in a small region. Finally, all the binary images are scale-weighted fused into one image, which presents the center and periphery with different brightness levels. Experimental illustrations on three typical areas of the ocean are featured with six scanning scales to show the effectiveness and practical use of the proposed framework. Moreover, the comparison experiments with the traditional method also show its advantages.

Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.