In variational problem, the selection of functional weighting factors (FWF) is one of the key points for discussing many relevant studies. To overcome arbitrariness and subjectivity of the empirical selecting methods ...In variational problem, the selection of functional weighting factors (FWF) is one of the key points for discussing many relevant studies. To overcome arbitrariness and subjectivity of the empirical selecting methods used widely at present, this paper tries to put forward an optimal objective selecting method of FWF. The focus of the study is on the weighting factors optimal selection in the variation retrieval single-Doppler radar wind field with the simple adjoint models. Weighting factors in the meaning of minimal variance are calculated out with the matrix theory and the finite difference method of partial differential equation. Experiments show that the result is more objective comparing with the factors obtained with the empirical method.展开更多
Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squ...Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squares estimation is unbiased. The condition number of the total least squares estimation is greater than the least squares estimation, so the total least squares estimation is easier to be affected by the data error than the least squares estimation. Then through the further derivation, the relationships of solutions, residuals and unit weight variance estimations between the total least squares and the least squares are given.展开更多
文摘In variational problem, the selection of functional weighting factors (FWF) is one of the key points for discussing many relevant studies. To overcome arbitrariness and subjectivity of the empirical selecting methods used widely at present, this paper tries to put forward an optimal objective selecting method of FWF. The focus of the study is on the weighting factors optimal selection in the variation retrieval single-Doppler radar wind field with the simple adjoint models. Weighting factors in the meaning of minimal variance are calculated out with the matrix theory and the finite difference method of partial differential equation. Experiments show that the result is more objective comparing with the factors obtained with the empirical method.
基金The research was supported by the National Natural Science Foundation of China(41204003)Scientific Research Foundation of ECIT(DHBK201113)Scientific Research Foundation of Jiangxi Province Key Laboratory for Digital Land(DLLJ201207)
文摘Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squares estimation is unbiased. The condition number of the total least squares estimation is greater than the least squares estimation, so the total least squares estimation is easier to be affected by the data error than the least squares estimation. Then through the further derivation, the relationships of solutions, residuals and unit weight variance estimations between the total least squares and the least squares are given.
文摘分析南极磷虾分布与环境因子的非线性和空间非静态性关系,对南极磷虾的高效捕捞和管理具有重要意义。本研究基于“龙腾”船2015、2016年在南设得兰群岛捕捞作业的渔捞日志数据,应用广义加模型(Generalized additive model,GAM)和地理权重回归模型(Geographical weighted regression,GWR)探究南极磷虾(Euphausia superba)渔场分布与环境因子的非线性和空间非静态性关系,并比较这2种模型的模拟性能,为南极磷虾的渔场渔情预报、资源评估和渔业管理提供基础数据。GAM模型结果显示,2015、2016年单位捕捞努力量渔获量(Catch per unit effort,CPUE)与作业水深均呈显著负相关关系(P<0.01),表明在作业水深范围内,南极磷虾在较浅水域集群密度较高;2015年CPUE与表层水温呈显著正相关关系(P<0.01),但在2016年呈显著负相关关系(P<0.01),推测是由于2年调查作业位置不同所致;CPUE与离岸距离关系不显著(P≥0.05)。GWR模型结果显示,作业水深对CPUE的影响无显著的空间变化(P>0.05);海水表温和离岸距离对CPUE的影响具显著的空间变化(P<0.01),表明这2个因子对南极磷虾渔场分布的影响在空间上不连续,存在显著空间非静态性。GAM模型可用于研究资源分布与驱动因子的一般规律;GWR模型作为全局回归模型的有效补充,可用于探究一般规律不适合的特殊区域,便于发现资源分布的“热点”区域,未来在海洋生物资源分布研究中将有广阔的应用前景。