Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using sa...Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus展开更多
The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade do...The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems.展开更多
文摘Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus
文摘The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems.
