Abstract
The reconstruction and monitoring of visibility over marine environments is critically important because of a lack of observations. To travel safely in marine environments, a high quality of visibility data is needed to evaluate navigation risk. Currently, although visibility is available through numerical weather prediction models as well as ground and spaceborne remote sensing platforms and ship measurements, issues still exist over the remote marine environments and northern latitudes. To improve visibility prediction and reduce navigational risks, gridded visibility data based on artificial neural network analysis can be used over marine environments, and the problem can be regarded as an air quality prediction problem based on machine learning algorithms. This new method based on artificial intelligence techniques developed here is tested over the Indian Ocean. The mean error of the inferred visibility from the artificial neural network analysis is found to be less than 8.0%. The results suggested that satellite-based optical thickness and numerical model-based reanalysis data can be used to infer gridded visibility values based on artificial neural network analysis, and that could help us reconstruct and monitor surface gridded visibility values over marine and remote environments.
Highlights
With the warming of the Earth’s surface and the gradually opening of the Arctic passages [1], marine transportation has become much more important than before
Vis representing reflected light intensity seeing by a human has an important impact on the risk analysis of marine navigation systems, but current measurements and the prediction of Vis lack spatial coverage over remote areas such as marine environments and Arctic conditions
Arctic regions still have a severe lack of observational networks [25], and numerical weather prediction (NWP) are limited because of microphysical and boundary layer algorithms [7,8]
Summary
With the warming of the Earth’s surface and the gradually opening of the Arctic passages [1], marine transportation has become much more important than before. Since the natural environment is complex and harsh in some sea areas, an objective assessment of the environmental risks of crossing the marine areas is needed urgently. Information on Vis and freezing precipitation over marine environments are usually limited and not easy to be predicted or monitored over various scales [3,4]. For this reason, having gridded data of Vis with high quality is a precondition to assess the navigation risk in marine environments. Vis is a direct reflection of air quality; the gridded Vis data could help people know more about the air quality around the world and monitor and predict the air quality
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