Using a skillful statistical model to predict September sea ice covering Arctic shipping routes

The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the midlatitudes but also brings more opportunities for Arctic shipping routes and polar resource exploration,both of which motivate us to further understand causes of sea-ice variations and to obtain more accurate estimates of seaice cover in the future.Here,a novel data-driven method,the causal effect networks algorithm,is applied to identify the direct precursors of September sea-ice extent covering the Northern Sea Route and Transpolar Sea Route at different lead times so that statistical models can be constructed for sea-ice prediction.The whole study area was also divided into two parts:the northern region covered by multiyear ice and the southern region covered by seasonal ice.The forecast models of September sea-ice extent in the whole study area(TSIE)and southern region(SSIE)at lead times of 1–4 months can explain over 65%and 79%of the variances,respectively,but the forecast skill of sea-ice extent in the northern region(NSIE)is limited at a lead time of 1 month.At lead times of 1–4 months,local sea-ice concentration and sea-ice thickness have a larger influence on September TSIE and SSIE than other teleconnection factors.When the lead time is more than 4 months,the surface meridional wind anomaly from northern Europe in the preceding autumn or early winter is dominant for September TSIE variations but is comparable to thermodynamic factors for NSIE and SSIE.We suggest that this study provides a complementary approach for predicting regional sea ice and is helpful in evaluating and improving climate models.

Big data-driven automatic generation of ship route planning in complex maritime environments

With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system(AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques;assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper.

Waigaoqiao Harbour——An Important International Shipping Route

The Shanghai Waigaoqiao Bonded Area Harbour Corporation has been developed for five years. In 1995, the corporation handled 2.26 million tons of cargo including 120,000 containers. The year 1996 also witnessed a sharp increase in cargo turnover. The Waigaoqiao Harbour, situated in the northeast corner of Pudong and facing the mouth of the Yangtze River,

Scale and scope of maritime cargoes through the Arctic Passages

This paper investigates the sources of goods being shipped through the Arctic passages, and trade generated in the Arc- tic, including oil and gas exploitation. Furthermore, it assesses the present situation for maritime cargo shipped from the Far East to Northwestern Europe and North America. Two main types of cargo are predicted to pass through the Arctic passages in the future. First, about 10 million t of liquefied natural gas will be delivered from Russia and the Nordic Arctic to the Far East by 2030. Second, there will be two-way trade flow of containerized cargo from the Far East to Europe and the United States through the North- east, Central and Northwest Passages. This will relieve pressure on present routes from the Far East to Northwestern Europe and North America. If Arctic navigation is technically possible in all seasons and shipping costs fall to those of ordinary ships, then assuming an equal share of shipping volume with the traditional canal routes, the maximum container freight passing through the Arctic passages by 2030 will be approximately 17.43 million TEUs （Twenty-foot Equivalent Units） per year, which is 85% of the volume transported on traditional canal routes in 2011. We conclude that there will be large-scale gas transportation through the Northeast Passage in the near future, and transit shipping across the Arctic will focus more on container transportation. The differences in shipping costs between Arctic routes and traditional canal routes are also compared.

Space Modeling Method for Ship Pipe Route Design

Ship pipe route design(SPRD)is one of the most complex and timeconsuming processes in ship detail design.Currently,there are many researches on the optimization of ship pipe routes,but there is still a lack of effective and convenient methods to build the pipe routing space.In order to solve this problem,a piping space modeling method for SPRD is proposed.This method is based on stereo lithographic(STL)file which is commonly used in data exchange,and it can convert the initial space model built in 3D-CAD software into the data model required by the pipe routing algorithms.For the application purpose,a piping space modeling utility(PSMU)is developed with Python and OpenGL,promoting the development of practical pipe routing system.Finally,the feasibility and practicability of the proposed method are verified by the experiment on the piping space of an actual ship fuel system.