# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## 4 Discussion and Conclusions  
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. Geostationary satellites have global spatial coverage capability, but may not work accurately in northern latitudes and during the daytime because of high-level cloud cover and solar radiation impact. At the same time, Arctic regions still have a severe lack of observational networks [25], and NWPs are limited because of microphysical and boundary layer algorithms [7, 8]. This study aimed to discover the feasibility of using a reanalysis of meteorological data from the ECMWF and satellite-based AOD data from the National Aeronautics and Space Administration (NASA) MODIS products to monitor Vis. Certainly, a vertical extinction of visible light from satellites needs to be converted to horizontal Vis and needs geostationary polar orbiting satellite data in northern latitudes. The results showed that ANN analysis for Vis monitoring can promise better results representing remote marine environment conditions than conventional methods. Although the inferred Vis has a higher accuracy than that of NWP and PCR predictions, an important issue still exists in the current analysis. Figure 8 suggests that inferred Vis values had NaN values near shorelines because Ts is obtained from ECMWF analysis with NaN values, and this resulted in bad values in inferred Vis. This shows that the uncertainty still exists in the inferred Vis due to the uncertainty found in the gridded data of child nodes. Reducing the uncertainty in the analysis requires improvements in the quality of collected data and microphysical algorithms in the NWP model simulations. The microphysical model of atmospheric Vis needs to be significantly improved, especially for sea fog [4]. At the same time, with the development of Big Data technology, a deeper network could be used to infer Vis to reduce the uncertainty of the inferred Vis in the future. Meanwhile, improving the model by trying to take other factors influencing Vis into consideration is also necessary to reduce the uncertainty in the future. Since the method used to calculate AOD is the Inverse Distance Weighting Method, which is a common and simple method, better methods to calculate AOD will be studied in the future. Figure 8: Vis results from the ANN method under tropical storm weather conditions. Tracking a tropical storm in the Indian Ocean from 6 to 7 November 2010. Its low-pressure center is indicated by blue ovals. The color bar indicates the Vis level. The current work suggested that Vis has a very important effect on the sea transportation safety, likely reducing accidents; therefore, a large amount of high-quality Vis data should be the basis for evaluating the navigability risk over remote areas. At the same time, the generated large amount of high-quality Vis data could be input into the Vis prediction model as an initial field to predict Vis in the future, which is so scarce now. The method used to generate gridded Vis in the paper can also be used to directly predict Vis in the future. If the objective is to predict Vis in a specific location, the constructed model should be trained with the data available for that location. The overall results suggested that improved Vis products can be obtained using ANN analysis, but Vis prediction in foggy environments is still an issue because of the uncertainty exists in microphysical parameters simulated by the numerical models. This needs to be further studied. At the same time, large data sets should be used to adequately train the parameters of ANN. Perhaps, a better designed field project can help to develop improved ANN analysis for Vis predictions for marine environments.