Abstract
In situ observation is one of the most direct and efficient ways to understand the ocean, but it is usually limited in terms of spatial and temporal coverage. The determination of optimal sampling strategies that effectively utilize available resources to maximize the information content of the collected ocean data is becoming an open problem. The historical sea surface temperature (SST) dataset contains the spatial variability information of SST, and this prior knowledge can be used to optimize the configuration of sampling points. Here, a configuration method of sampling points based on the variability of SST is studied. Firstly, in order to get the spatial variability of SST in the ocean field to be sampled, the historical SST data of the field is analyzed. Then, K-means algorithm is used to cluster the subsampled fields to make the configuration of sampling points more suitable. Finally, to evaluate the sampling performance of the new configuration method of sampling points, the SST field is reconstructed by the method based on compression sensing algorithm. Results show that the proposed optimal configuration method of sampling points significantly outperforms the traditional random sampling points distribution method in terms of reconstruction accuracy. These results provide a new method for configuring sampling points of ocean in situ observation with limited resources.
Highlights
sea surface temperature (SST) is a key input to atmospheric and oceanic forecasting and prediction systems [1], and it is always used as boundary conditions for numerical weather prediction (NWP) and ocean forecasting models [2, 3]
How to choose the most suitable sampling points to gather maximum information is the main task in ocean in situ observation
A new optimal configuration method of sampling points based on the variability of SST is proposed in this paper
Summary
SST is a key input to atmospheric and oceanic forecasting and prediction systems [1], and it is always used as boundary conditions for numerical weather prediction (NWP) and ocean forecasting models [2, 3]. It is not surprising that high-resolution SST data influences the formation and subsequent evolution of synoptic weather systems [6, 7]. Such models are increasingly used for operational ocean applications including offshore operations, maritime transport, maritime safety, marine pollution control, and wave and surf models. Based on these applications, to explore the ocean effectively, regular, timely, and accurate SST measurements are necessary. In situ observation is one of the most effective methods in exploring the ocean. The ocean observation network (such as the buoy network) and the mobile observation platform are two commonly used ways of in situ observation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
