The traditional ＂dephase and sum＂ algorithms for over/under data combination estimate the ghost operator by assuming a calm sea surface. However, the real sea surface is typically rough, which invalidates the calm s...The traditional ＂dephase and sum＂ algorithms for over/under data combination estimate the ghost operator by assuming a calm sea surface. However, the real sea surface is typically rough, which invalidates the calm sea surface assumption. Hence, the traditional ＂dephase and sum＂ algorithms might produce poor-quality results in rough sea conditions. We propose an adaptive over/under data combination method, which adaptively estimates the amplitude spectrum of the ghost operator from the over/under data, and then over/under data combinations are implemented using the estimated ghost operators. A synthetic single shot gather is used to verify the performance of the proposed method in rough sea surface conditions and a real triple over/under dataset demonstrates the method performance.展开更多
In this study, two deep convective cloud cases were analyzed in detail to study their initiation and evolution. In both cases, all deep convective clouds were positioned at the rear of the cold front cloud bands and p...In this study, two deep convective cloud cases were analyzed in detail to study their initiation and evolution. In both cases, all deep convective clouds were positioned at the rear of the cold front cloud bands and propagated backward. Satellite data showed that prior to initiation of the deep convective clouds, thermodynamic and moist conditions were favorable for their formation. In the morning, a deep convective cloud at the rear of cold front cloud band propagated backward, the outflow boundary of which created favorable conditions for initiation. An additional deep convective cloud cluster moved in from the west and interacted with the outflow boundary to develop a mesoscale convective system(MCS) with large, ellipse-shaped deep convective clouds that brought strong rainfall. The initiation and evolution of these clouds are shown clearly in satellite data and provide significant information for nowcasting and short-term forecasting.展开更多
This paper describes the program module ＂GRAPHS＂ which was developed for data processing in geobotany and ecology fields. The ＂GRAPHS＂ has a simple interface and is integrated into the Microsoft Excel. This allows...This paper describes the program module ＂GRAPHS＂ which was developed for data processing in geobotany and ecology fields. The ＂GRAPHS＂ has a simple interface and is integrated into the Microsoft Excel. This allows users to use all features of Microsoft Excel for storage and preparation data for analysis. Calculation of the most common similarity indexes （Jaccarda. Sorenson, Ohai etc.） and their visualization by using different algorithms of the graph theory or hierarchical cluster analysis allows simplifying and accelerating the process of data analysis in ecology or geobotany and makes it clearer. Also, three ordination methods--PCA （principal components analysis）, CA （correspondence analysis）. NMS （nonmetric multidimensional scaling）-were implemented in the module. The module can be used for vegetation classification, and be used to allocate diagnostic species or to search environmental factors most strongly impact on vegetation. Algorithms of data analysis which were implemented in the module ＂GRAPHS＂ have universal nature so they can be applied in many other fields of science.展开更多
文摘The traditional ＂dephase and sum＂ algorithms for over/under data combination estimate the ghost operator by assuming a calm sea surface. However, the real sea surface is typically rough, which invalidates the calm sea surface assumption. Hence, the traditional ＂dephase and sum＂ algorithms might produce poor-quality results in rough sea conditions. We propose an adaptive over/under data combination method, which adaptively estimates the amplitude spectrum of the ghost operator from the over/under data, and then over/under data combinations are implemented using the estimated ghost operators. A synthetic single shot gather is used to verify the performance of the proposed method in rough sea surface conditions and a real triple over/under dataset demonstrates the method performance.
基金supported by the National Natural Science Foundation of China"Study of Characteristics of the Environmental Field before the Deep Convective Cloud Initiated Using Geostational Meteorological Satellite Data"(Grant No.41005026)
文摘In this study, two deep convective cloud cases were analyzed in detail to study their initiation and evolution. In both cases, all deep convective clouds were positioned at the rear of the cold front cloud bands and propagated backward. Satellite data showed that prior to initiation of the deep convective clouds, thermodynamic and moist conditions were favorable for their formation. In the morning, a deep convective cloud at the rear of cold front cloud band propagated backward, the outflow boundary of which created favorable conditions for initiation. An additional deep convective cloud cluster moved in from the west and interacted with the outflow boundary to develop a mesoscale convective system(MCS) with large, ellipse-shaped deep convective clouds that brought strong rainfall. The initiation and evolution of these clouds are shown clearly in satellite data and provide significant information for nowcasting and short-term forecasting.
文摘This paper describes the program module ＂GRAPHS＂ which was developed for data processing in geobotany and ecology fields. The ＂GRAPHS＂ has a simple interface and is integrated into the Microsoft Excel. This allows users to use all features of Microsoft Excel for storage and preparation data for analysis. Calculation of the most common similarity indexes （Jaccarda. Sorenson, Ohai etc.） and their visualization by using different algorithms of the graph theory or hierarchical cluster analysis allows simplifying and accelerating the process of data analysis in ecology or geobotany and makes it clearer. Also, three ordination methods--PCA （principal components analysis）, CA （correspondence analysis）. NMS （nonmetric multidimensional scaling）-were implemented in the module. The module can be used for vegetation classification, and be used to allocate diagnostic species or to search environmental factors most strongly impact on vegetation. Algorithms of data analysis which were implemented in the module ＂GRAPHS＂ have universal nature so they can be applied in many other fields of science.
