Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis

Yulong Shan, Ren Zhang, Ismail Gültepe, Yaojia Zhang, Ming Li, Yangjun Wang

Applied Sciences · 23 October 2019 · Environment and Climate Change Canada (ECCC)

In one sentence. A neural network infers gridded marine surface visibility over the Indian Ocean from satellite optical thickness and reanalysis, with mean error under 8%.

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.

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