# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## 1 Introduction
At the same time, Vis data from the NWPs is limited because of the high uncertainty in the Vis microphysical parameterization based on using only liquid water content (LWC) [10; 12], where droplet number concentration (Nd) is assumed to be a negligible factor. In addition, because of the limited number of observations from buoys and ships, the reliable Vis data over marine environments can be highly uncertain; therefore, both the monitoring and forecasting of Vis need to be improved significantly. Many earlier studies have tried to obtain Vis data over gridded surfaces using observations and NWP models. For example, [PERSON] et al. [13] retrieved Vis based on principal component regression (PCR) and NOAA/AVHRR remote sensing data. They found that the correlation coefficient between the retrieved Vis and measured Vis was 0.82, but the method could not accurately obtain high or low Vis, and the average relative error was at 21.4%. A new microphysical parameterization for fog Vis (MPF) method using RH (relative humidity), LWC, and Nd parameters was developed by [PERSON] et al. [10]. They stated that when the RH is close to 100%, the MPF should be used in NWP simulations. This new method significantly improves the prediction of Vis obtained from the operational forecast models. Uncertainty in the new Vis parameterization is found to be less than 29% compared to more than 50% based on only LWC-based parameterization. Their work improved NWP predictions of Vis significantly, but further studies are still needed for high-resolution prediction models. [PERSON] et al. [9] studied the relationship between Vis and satellite-based aerosol products such as optical thickness (aerosol optical depth, or AOD), and presented their results for atmospheric attenuation coefficients over the globe. The fog prediction of Vis in marine environments using NWP models includes large uncertainties on small space scales over the short time periods, because the microphysical schemes used in numerical models [4] have limited boundary layer processes such as turbulence. To achieve more stable and reliable historical gridded Vis data around the world's marine environments, remote sensing retrievals of aerosol and cloud optical thickness together with meteorological parameters are needed. There have been many earlier studies revealing the relationships between atmospheric Vis and meteorological elements. For example, [PERSON] [14] studied the various environmental factors affecting atmospheric Vis in the region of Chongqing, China, and their results showed that atmospheric Vis changes seasonally and also as a function of horizontal wind speed (Uh), RH, and rainfall. [PERSON] et al. [15] explored the interdecadal variability and trends of surface horizontal Vis in the urban areas of Athens. Their results showed that Vis was negatively correlated with RH. The correlation was stronger in the early part of the time series, and decreased over the years. In contrast, a positive correlation between Vis and wind speed was found. At the same time, the relationship between AOD and Vis in Athens was also examined in the study, and their negative correlation was confirmed. [PERSON] et al. also studied the air quality of Athens by studying the Vis in the area. [PERSON] et al. [16] investigated the long-term trends in Vis for eight meteorological stations situated in the UK. In general, Vis has improved at most of the stations through time, which are attributed to reductions in aerosol particle loadings and decreases in RH. At the same time, variations of a few percentage points in this RH range can have significant effects on Vis. Their study also showed that Uh and wind direction were both important factors influencing Vis. [PERSON] et al. [7] summarized all the meteorological factors that affect Vis severely. Since precipitation and fog both have a great effect on Vis, meteorological processes affecting precipitation and sea fog should also be considered in the analysis when inferring Vis from remote sensing platforms and NWP model simulations. [PERSON] et al.