Long-gap Filling Method for the Coastal Monitoring Data

Technique for the long-gap filling that occur frequently in ocean monitoring data is developed. The method estimates the unknown values of the long-gap by the summation of the estimated trend and selected residual components of the given missing intervals. The method was used to impute the data of the long-term missing interval of about 1 month, such as temperature and water temperature of the Ulleungdo ocean buoy data. The imputed data showed differences depending on the monitoring parameters, but it was found that the variation pattern was appropriately reproduced. Although this method causes bias and variance errors due to trend and residual components estimation, it was found that the bias error of statistical measure estimation due to long-term missing is greatly reduced. The mean, and the 90% confidence intervals of the gap-filling model’s RMS errors are 0.93 and 0.35~1.95, respectively.

SACTNet: Spatial Attention Context Transformation Network for Cloud Removal

Optical remote sensing image has the advantages of fast information acquisition, short update cycle, and dynamic monitoring. It plays an important role in many earth observation activities, such as ocean monitoring, meteorological observation, land planning, and crop yield investigation. However, in the process of image acquisition, an optical remote sensing system is often disturbed by clouds, resulting in low image clarity or even loss of ground information, affecting the acquisition of feature information and subsequent applications. We propose a spatial attention recurrent neural network model combined with a context transformation network to overcome the challenge of cloud occlusion. This model can obtain the core information in remote sensing images and consider the remote dependencies in the network. Furthermore, the network proposed in this paper has achieved excellent performance on the RICE1 and RICE2 datasets.

Project Atuin

Project Atuin brings together several bleeding edge technologies into a state-of-the-art, semi-autonomous research and ocean monitoring platform. Able to be deployed anywhere in the world and, in a future state, combined with a dedicated micro-satellite, the platform will be able to be accessed and given instructions from anywhere in the world. This modular science and protection platform will be deployed in partnership with local stakeholders and the data will be open to all and packaged into curriculum for all levels.

Atlantic Ocean science diplomacy in action: the pole-to-pole All Atlantic Ocean Research Alliance

The ocean provides important ecosystem services to society, but its health is in crisis due to the impacts of human activities. Ocean sustainability requires ambitious levels of scientific evidence to support governance and management of human activities that impact the ocean. However, due to the size, complexity and connectivity of the ocean, monitoring and data collection presupposes high investments, and nations need to cooperate to deliver the ambitious, costly science that is required to inform decisions. Here, we highlight the role that ocean science diplomacy plays in facilitating the science needed to support ocean governance and management from domestic, regional to international scales in the Atlantic region via the All Atlantic Ocean Research Alliance. This Alliance is supported by the Galway Statement (2013), the South–South Framework for Scientific and Technical Cooperation in the South and Tropical Atlantic and the Southern Oceans (2017), and the Belém Statement (2017). We discuss the national and international interests that drove the processes of negotiating these agreements, as well as their challenges to date. We also discuss the potential future of the All Atlantic Alliance, as well as its significance in emerging global initiatives such as the UN Decade of Ocean Science for Sustainable Development (2021–2030).

Examining the Influence of Recording System on the Pure Temperature Error in XBT Data

Expendable bathythermographs (XBTs) have been widely deployed for ocean monitoring since the late-1960s. Improving the quality of XBT data is a vital task in climatology. Many factors (e.g., temperature, probe type, and manufacturing time) have been identified as major influences of XBT systematic bias. In addition, the recording system (RS) has long been suspected as another factor. However, this factor has not been taken into account in any global XBT correction schemes, partly because its impact is poorly understood. Here, based on analysis of an XBT/CTD side-by-side dataset and a global collocated reference dataset, the influence of RSs on the pure temperature error (PTE) is examined. Results show a clear time-dependency of PTE on the RS, with maximum values occurring in the 1970s. In addition, the method used to convert thermistor resistance into temperature in the RS (using a resistance-temperature equation) has changed over time. These changes, together with the decadal changes in RSs, might contribute a small error (10% of average) to the RS dependency. Here, an improvement of global XBT bias correction that accounts for the RS dependency is proposed. However, more than 70% of historical global XBT data is missing RS type information. We investigate several assumptions about the temporal distribution of RS types, and all scenarios lead to at least a ~50% reduction in the time variation of PTE compared with the uncorrected data. Therefore, the RS dependency should be taken into account in updated XBT correction schemes, which would have further implications for climatology studies.