International Journal of Marine Science, 2024, Vol.14, No.3, 193-203 http://www.aquapublisher.com/index.php/ijms 195 algorithms facilitates the analysis of long-term datasets, enhancing the accuracy of marine models and predictions (Liu et al., 2017; Vance et al., 2019). 3.2 Data assimilation techniques Data assimilation techniques are essential for improving the accuracy of marine models by integrating observational data with model predictions. Various methods, such as variational and sequential approaches, have been developed to address the challenges of representing physical, chemical, and biological properties in the ocean. Recent advances include ensemble and four-dimensional variational methods, which have shown promise in regional ocean systems and biogeochemical applications (Edwards et al., 2015; Zalesny et al., 2020). These techniques help in producing more accurate estimates of the ocean state by combining observations and model dynamics. 3.3 Cloud computing and storage solutions Cloud computing offers significant opportunities for managing and analyzing marine data. The shift to cloud-based platforms allows for the development of shared data processing workflows and the utilization of adaptable software for data ingestion and storage. Cloud computing enables high-performance mass storage of observational data and on-demand computing for running model simulations, which can be done in close proximity to the data. This approach facilitates a more flexible and adaptable observation and prediction computing architecture, allowing researchers to access and analyze data more efficiently (Vance et al., 2019). Additionally, cloud platforms provide tools to manage workflows and frameworks for collaboration, making it easier to create, analyze, and distribute products derived from long-term datasets. 3.4 Interoperability standards Interoperability standards are crucial for ensuring that different marine observation and prediction systems can work together seamlessly. These standards facilitate the integration of data from various sources and disciplines, enabling a more comprehensive understanding of oceanic processes. The development and adoption of common standards for data formats, metadata, and communication protocols are essential for achieving interoperability. This allows for the efficient sharing and utilization of data across different platforms and systems, enhancing the overall effectiveness of marine observation and prediction efforts (Vance et al., 2019). By leveraging big data analytics, advanced data assimilation techniques, cloud computing, and interoperability standards, the field of marine observation and prediction can achieve significant advancements in understanding and forecasting oceanic phenomena. These technologies and methodologies provide the foundation for more accurate and reliable marine models, ultimately contributing to better management and protection of marine environments. 4 Key Technologies in Marine Prediction 4.1 Numerical modeling and simulation Numerical modeling and simulation are foundational techniques in marine prediction, enabling the representation of complex oceanic processes through mathematical formulations. These models are essential for predicting various marine phenomena, including sea surface temperatures (SST), wave heights, and ocean currents. For instance, traditional numerical models based on physics-based assumptions are widely used for SST prediction, although they are often complemented by machine learning techniques to enhance accuracy and reduce computational demands (Sarkar et al., 2020; Ali et al., 2021). Additionally, the integration of high-resolution observational data, such as satellite and in-situ measurements, is crucial for improving the skill of these models, particularly in predicting mesoscale ocean features like eddies (Jacobs et al., 2021). 4.2 Machine learning and AI applications The application of machine learning (ML) and artificial intelligence (AI) in marine prediction has seen significant growth, offering new methodologies to complement traditional numerical models. AI techniques, such as deep learning neural networks and ensemble machine learning models, have been successfully applied to predict SST, wave heights, and other oceanic parameters with high accuracy (Chen et al., 2021; Panda et al., 2021; Song et al.,
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