Abstract
With the accelerated warming of the arctic and the gradual opening of the Arctic passages, more and more attention has been paid to assessing the risk of the navigation environment in the Arctic. Due to the scarcity of visibility data in the Arctic, this study proposes a model for referring visibility based on a back propagation (BP) neural network. The reliability of the model is validated and the gridded atmospheric visibility data in the Arctic from 2009 to 2018 was obtained. At the same time, this study analyzed the spatial and temporal features of visibility in the Arctic. The results show that the mean relative error is less than 20% under the different sample forms and it is more accurate to infer the visibility in a specific month using the multiple-year data of that month as training samples. Furthermore, the amount of sample data has a positive effect on the accuracy of inferred visibility, but the effect decreases with data quantity increasing. Visibility changes quickly in the south of 80° N in August, but slowly in the north in that time. At the same time, visibility in July and August is lower than that in other months but higher in March and May.
Highlights
With the gradual increase in global temperatures, the melting rate of sea ice in the Arctic is accelerating, and the Arctic passages (APs) are close to being fully opened
Arctic, is proposed a model for referring visibility in the Arctic based on a neural network and analyzed its spatial receiving an increasing amount of attention [2]
The neural network can well fit the nonlinear relationship between visibility and its influencing analyzed its spatial and temporal features
Summary
With the gradual increase in global temperatures, the melting rate of sea ice in the Arctic is accelerating, and the Arctic passages (APs) are close to being fully opened. Obtaining a large number of high-quality visibility gridded data and understanding the temporal and spatial variations of visibility in the Arctic is of great significance for ensuring the safety of crossing the APs. To improve the result from NWP, many scholars have studied the parameterization scheme of visibility. A new microphysical parameterization for fog (NMPF) method using RH (relative humidity), LWC (liquid water content) and Nd (droplet number concentration) parameters was developed by Gultepe et al [5] to calculate visibility due to fog when RH~100%, and the NMPF has been used extensively in NWP simulations This new method significantly improves the prediction of visibility from the operational forecast models.
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