International Journal of Marine Science, 2024, Vol.14, No.3, 193-203 http://www.aquapublisher.com/index.php/ijms 200 6.3 Comparative analysis of different approaches 6.3.1 Data coverage and resolution Satellite remote sensing provides broad spatial coverage but may lack the resolution of in-situ measurements like those from AUVs and gliders. Combining these technologies can optimize data collection, offering both extensive coverage and high-resolution insights (Chai et al., 2020; Loveday et al., 2022). 6.3.2 Operational cost and maintenance AUVs and gliders involve higher operational costs and maintenance compared to drifting buoys and satellite remote sensing. However, they provide more detailed and specific data. Budget considerations and specific research goals should guide the choice of technology (Whitt et al., 2020). 6.3.3 Real-time data availability Drifting buoys and satellite systems generally offer real-time data transmission, which is critical for immediate decision-making. In contrast, AUVs and gliders may have delays in data retrieval. Ensuring a balance between real-time data needs and detailed measurements is crucial for effective marine observation (Vicen-Bueno et al., 2019). 6.3.4 Environmental impact The environmental impact of deploying these technologies varies. AUVs and gliders are less intrusive but may disturb local marine life temporarily. Drifting buoys and HFR have minimal direct impact but require careful consideration of their long-term deployment on marine ecosystems (Aniceto et al., 2020). 6.3.5 Integration and interoperability The integration of different observation technologies enhances data robustness and reliability. Projects like the Global Ocean Observing System (GOOS) exemplify the benefits of combining satellite, buoy, and in-situ data. Standardizing protocols and improving interoperability are key to maximizing the utility of diverse marine observation systems (Whitt et al., 2020). 7 Challenges and Future Directions 7.1 Technical and methodological challenges The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into marine observation and prediction systems presents several technical and methodological challenges. One significant challenge is the discrepancy between the volume of data collected and our capacity for data analysis. While sensor technology and autonomous platforms have advanced, enabling the collection of vast amounts of data, the analytical capabilities have not kept pace, creating a bottleneck in effective data utilization (Malde et al., 2020). Additionally, the formulation and accuracy of data assimilative models are highly dependent on the quality and quantity of interdisciplinary observational data, which remains a challenge due to the complexity and variability of oceanographic processes. 7.2 Knowledge gaps and research needs There are notable knowledge gaps in understanding the physical and biological processes that influence marine ecosystems, which limit prediction capabilities. For instance, the lack of sufficient observations for forecast initialization and verification hampers the development of accurate models (Capotondi et al., 2019). Furthermore, the influence of climate change on ocean conditions, such as sea level rise and increased stratification, adds another layer of complexity to marine forecasting (Capotondi et al., 2019). Research is needed to develop more comprehensive observational networks and to improve the understanding of the interactions between different oceanographic variables and processes. 7.3 Emerging trends and innovations Emerging trends in marine observation and prediction include the increasing application of AI and ML to enhance data analysis and forecasting capabilities. AI algorithms are being used to identify and predict ocean phenomena such as internal waves, heatwaves, and the El Niño-Southern Oscillation (ENSO) (Song et al., 2023). The
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