颗粒粒度反演需要求解第一类Fredholm积分方程,此问题是动态光散射中的难点之一,其中,双峰颗粒的反演更是亟待解决的问题.为保证反演结果的非负性,采用了trust region reflective Newton和active set算法实现的非负Tikhonov,非负TSVD算...颗粒粒度反演需要求解第一类Fredholm积分方程,此问题是动态光散射中的难点之一,其中,双峰颗粒的反演更是亟待解决的问题.为保证反演结果的非负性,采用了trust region reflective Newton和active set算法实现的非负Tikhonov,非负TSVD算法对双峰颗粒数据进行了反演.结果表明采用前者实现的非负Tikhonov和非负TSVD不能区别间隔粒径较近双峰,而采用后者实现的非负Tikhonov和非负TSVD能区别出.展开更多
The Proper Orthogonal Decomposition(POD)-based ensemble four-dimensional variational(4DVar) assimilation method(POD4DEnVar) was proposed to combine the strengths of EnKF(i.e.,the ensemble Kalman filter) and 4DVar assi...The Proper Orthogonal Decomposition(POD)-based ensemble four-dimensional variational(4DVar) assimilation method(POD4DEnVar) was proposed to combine the strengths of EnKF(i.e.,the ensemble Kalman filter) and 4DVar assimilation methods.Recently,a POD4DEnVar-based radar data assimilation scheme(PRAS) was built and its effectiveness was demonstrated.POD4 DEnVar is based on the assumption of a linear relationship between the model perturbations(MPs)and the observation perturbations(OPs);however,this assumption is likely to be destroyed by the highly non-linear forecast model or observation operator.To address this issue,using the Gauss-Newton iterative method,the nonlinear least squares enhanced POD4 DEnVar algorithm(referred to as NLS-4DVar) was proposed.Naturally,the PRAS was upgraded to form the NLS-4DVar-based radar data assimilation scheme(NRAS).To evaluate the performance of NRAS against PRAS,observing system simulation experiments(OSSEs) were conducted to assimilate reflectivity and radial velocity individually,with one,two,and three iterations.The results demonstrated that the NRAS outperformed PRAS in improving the initial condition and the forecasting of model variables and rainfall.The NRAS,with a smaller number of iterations,can yield a convergent result.In contrast to the situation when assimilating radial velocity,the advantages of NRAS over PRAS were more obvious when assimilating reflectivity.展开更多
文摘颗粒粒度反演需要求解第一类Fredholm积分方程,此问题是动态光散射中的难点之一,其中,双峰颗粒的反演更是亟待解决的问题.为保证反演结果的非负性,采用了trust region reflective Newton和active set算法实现的非负Tikhonov,非负TSVD算法对双峰颗粒数据进行了反演.结果表明采用前者实现的非负Tikhonov和非负TSVD不能区别间隔粒径较近双峰,而采用后者实现的非负Tikhonov和非负TSVD能区别出.
基金partially supported by theNational Key Research and Development Program of China(Grant No.2016YFA0600203)the High-resolution Earth Observation System Major Special Project(CHEOS)(Grant No.32-Y20A17-9001-15/17)+1 种基金the National Natural Science Foundation of China(Grant No.41575100)the Special Fund for Meteorological Scientific Research in Public Interest(Grant No.GYHY201306045)
文摘The Proper Orthogonal Decomposition(POD)-based ensemble four-dimensional variational(4DVar) assimilation method(POD4DEnVar) was proposed to combine the strengths of EnKF(i.e.,the ensemble Kalman filter) and 4DVar assimilation methods.Recently,a POD4DEnVar-based radar data assimilation scheme(PRAS) was built and its effectiveness was demonstrated.POD4 DEnVar is based on the assumption of a linear relationship between the model perturbations(MPs)and the observation perturbations(OPs);however,this assumption is likely to be destroyed by the highly non-linear forecast model or observation operator.To address this issue,using the Gauss-Newton iterative method,the nonlinear least squares enhanced POD4 DEnVar algorithm(referred to as NLS-4DVar) was proposed.Naturally,the PRAS was upgraded to form the NLS-4DVar-based radar data assimilation scheme(NRAS).To evaluate the performance of NRAS against PRAS,observing system simulation experiments(OSSEs) were conducted to assimilate reflectivity and radial velocity individually,with one,two,and three iterations.The results demonstrated that the NRAS outperformed PRAS in improving the initial condition and the forecasting of model variables and rainfall.The NRAS,with a smaller number of iterations,can yield a convergent result.In contrast to the situation when assimilating radial velocity,the advantages of NRAS over PRAS were more obvious when assimilating reflectivity.
