# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## 1 Introduction  
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. The main factors that affect the navigation risk are sea wave, sea ice, visibility (Vis), icing, wind, and turbulence, as well as precipitation [2; 3]. 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. At the same time, Vis has an important influence on aviation, especially on the take-off and landing for pilots. Due to that, 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. The statistical data on marine accidents [5] showed that 220 maritime accidents occurred in the southern waters of the Taiwan Strait from 2000 to 2007, including 122 collisions, accounting for55.45% of the total number of maritime accidents (68 collisions) that occurred during poor visibility conditions. At the same time, in China, 31% of the total number of aviation accidents were caused by adverse weather. Even in the United States, where aviation technology is advanced, weather-related air crashes account for up to one-third of all fatalities [6]. Therefore, in aeronautical meteorology, people have always attached great importance to the relationship between Vis and aviation. These suggest that under the Arctic environments, these numbers can easily go beyond what is given here because of the lack of observations near coastal areas and open marine environments. In fact, because of the lack of observations, including ones from satellites over Arctic regions, and limited precipitation radars available on the large ships (not on small ones), fog radars (e.g., W-band radars) are needed to monitor fog conditions that are not usually available. Accurate nowcasting and forecasting of Vis is needed more than using only monitoring of the ship maneuvers and their motion. This is in a way similar to aviation applications [7]. The only way to predict fog currently in the Arctic is use numerical weather prediction (NWP) models, but their physical algorithms are usually not developed for fog predictions [8]. Vis is the greatest horizontal distance at which it is possible to observe and identify particular objects, and fog is defined when Vis <1 km. For Vis evaluation, any particle type in the atmosphere can play an important role and needs to be considered in the analysis [7]. Currently, Vis data are available from in-situ and remote sensing platforms, as well as from NWP simulations in a limited way. In in-situ observation for Vis, the atmospheric extinction coefficient is usually measured firstly based on an instrument. Then, the Vis value is calculated based on the Koschmieder equation, which defines visibility mathematically. Many methods have been proposed for remote sensing observations on Vis. The most often used methods to obtain Vis data based on satellite remote sensing is to obtain the planet reflectivity at various bands firstly, and then calculate the Vis data based on the built relationship between Vis and planet reflectivity. In recent years, one way to calculate Vis based on satellite-based aerosol products has been proposed, which has been tested on the East Coast of the United States [9]. However, Vis from satellite observations cannot be used accurately during the daytime when mid or high-level clouds exist [8; 10; 11].