In this study,fog simulations were conducted using the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) in and around the Yodo River Basin,Japan.The purpose is to investigate the MM5 performance of fog simulatio...In this study,fog simulations were conducted using the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) in and around the Yodo River Basin,Japan.The purpose is to investigate the MM5 performance of fog simulation for long-term periods.The simulations were performed for January,February,March,and July,2005 with a coarse 3-kin and a nested fine 1-km grid domains. Results of the simulations were compared with data from ten meteorological observatories,fog sampling site in Mt.Rokko,and visibility measurem...展开更多
This review presents some of the latest achievements in sea fog research,including fog climatology,fog structure in the marine atmospheric boundary layer,and numerical simulations and forecasting of fog.With the devel...This review presents some of the latest achievements in sea fog research,including fog climatology,fog structure in the marine atmospheric boundary layer,and numerical simulations and forecasting of fog.With the development of atmospheric observational techniques and equipments,new facts about sea fog are revealed.The mechanisms involved in the formation,development and dissipation of sea fog are further explored with the help of advanced atmospheric models.展开更多
For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are ac...For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.展开更多
文摘In this study,fog simulations were conducted using the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) in and around the Yodo River Basin,Japan.The purpose is to investigate the MM5 performance of fog simulation for long-term periods.The simulations were performed for January,February,March,and July,2005 with a coarse 3-kin and a nested fine 1-km grid domains. Results of the simulations were compared with data from ten meteorological observatories,fog sampling site in Mt.Rokko,and visibility measurem...
基金supported by the National Natural Science Foundation of China (NSFC) (41175006)‘973 Program’(2012CB955602) and the Ministry of Education (MOE)(20090132110008)
文摘This review presents some of the latest achievements in sea fog research,including fog climatology,fog structure in the marine atmospheric boundary layer,and numerical simulations and forecasting of fog.With the development of atmospheric observational techniques and equipments,new facts about sea fog are revealed.The mechanisms involved in the formation,development and dissipation of sea fog are further explored with the help of advanced atmospheric models.
基金supported by the National Natural Science Foundation of China (62173103)the Fundamental Research Funds for the Central Universities of China (3072022JC0402,3072022JC0403)。
文摘For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.
