针对半球共形阵体制下进行低空风切变检测时会受到强地杂波信号的干扰,导致风切变信号难以检测的问题,提出了一种基于空时自回归的直接数据域算法(Space-Time Autoregressive Direct Data Domain,D3AR)的低空风切变风速估计方法。该方...针对半球共形阵体制下进行低空风切变检测时会受到强地杂波信号的干扰,导致风切变信号难以检测的问题,提出了一种基于空时自回归的直接数据域算法(Space-Time Autoregressive Direct Data Domain,D3AR)的低空风切变风速估计方法。该方法首先将待检测距离单元的数据从空域、时域以及空时域进行信号对消处理;然后将处理后的数据矩阵描述为空时自回归(Autoregression,AR)模型并估计模型参数;再通过构造与杂波子空间正交的空间来实现对杂波的抑制,最后通过提取待检测单元的最大多普勒频率来估计风场速度。根据仿真结果显示,该方法有效地实现了地杂波抑制,并且能够精确估计风速。展开更多
To understand the response of marine ecosystem to environmental factors, the oceanographic (physical and biochemical) data are analyzed to examine the spatio-temporal distributions of chlorophyll a (Chl a) associated ...To understand the response of marine ecosystem to environmental factors, the oceanographic (physical and biochemical) data are analyzed to examine the spatio-temporal distributions of chlorophyll a (Chl a) associated with surface temperature, winds and height anomaly for long periods (1997-2008) in the western South China Sea (SCS). The results indicate that seasonal and spatial distributions of Chl a are primarily influenced by monsoon winds and hydrography. A preliminary Empirical Orthogonal Function (EOF) analysis of remotely sensed data is used to assess basic characteristics of the response process of Chl a to physical changes, which reveals interannual variability of anomalous low Chl a values corresponding to strong El Ni o (1997-1998), high values corresponding to strong La Ni a (1999-2000), low Chl a corresponding to moderate El Ni o (2001-2003), upward Chl a after warm event in 2005 off the east coast of Vietnam. The variability of Chl a in nearshore and the Mekong River Estuary (MER) waters also suggests its response to these warm or cold processes. Considering the evidence for covariabilities between Chl a and sea surface temperature, winds, height anomaly (upwelling or downwelling), cold waters input and strong winds mixing may play important roles in the spatial and temporal variability of high Chl a. Such research activities could be very important to gain a mechanistic understanding of ecosystem responses to the climate change in the SCS.展开更多
文摘针对半球共形阵体制下进行低空风切变检测时会受到强地杂波信号的干扰,导致风切变信号难以检测的问题,提出了一种基于空时自回归的直接数据域算法(Space-Time Autoregressive Direct Data Domain,D3AR)的低空风切变风速估计方法。该方法首先将待检测距离单元的数据从空域、时域以及空时域进行信号对消处理;然后将处理后的数据矩阵描述为空时自回归(Autoregression,AR)模型并估计模型参数;再通过构造与杂波子空间正交的空间来实现对杂波的抑制,最后通过提取待检测单元的最大多普勒频率来估计风场速度。根据仿真结果显示,该方法有效地实现了地杂波抑制,并且能够精确估计风速。
基金The National Natural Science Foundation of China under contract Nos 41076011, 41206023, 41222038key program under contract No.40531006the National Basic Research Program of China ("973"Program) under contract No.2011CB403606
文摘To understand the response of marine ecosystem to environmental factors, the oceanographic (physical and biochemical) data are analyzed to examine the spatio-temporal distributions of chlorophyll a (Chl a) associated with surface temperature, winds and height anomaly for long periods (1997-2008) in the western South China Sea (SCS). The results indicate that seasonal and spatial distributions of Chl a are primarily influenced by monsoon winds and hydrography. A preliminary Empirical Orthogonal Function (EOF) analysis of remotely sensed data is used to assess basic characteristics of the response process of Chl a to physical changes, which reveals interannual variability of anomalous low Chl a values corresponding to strong El Ni o (1997-1998), high values corresponding to strong La Ni a (1999-2000), low Chl a corresponding to moderate El Ni o (2001-2003), upward Chl a after warm event in 2005 off the east coast of Vietnam. The variability of Chl a in nearshore and the Mekong River Estuary (MER) waters also suggests its response to these warm or cold processes. Considering the evidence for covariabilities between Chl a and sea surface temperature, winds, height anomaly (upwelling or downwelling), cold waters input and strong winds mixing may play important roles in the spatial and temporal variability of high Chl a. Such research activities could be very important to gain a mechanistic understanding of ecosystem responses to the climate change in the SCS.
