The variational adjoint method was applied to retrieving the turbulivity of the atmospheric Ekman boundary layer along with the regularization principle, The validity of the method was verified by using the idealized ...The variational adjoint method was applied to retrieving the turbulivity of the atmospheric Ekman boundary layer along with the regularization principle, The validity of the method was verified by using the idealized data, and then the turbulivity profile and the geostrophic wind profile were retrieved through it for real observational wind filed data.展开更多
It is well known that retrieval of parameters is usually ill-posed and highly nonlinear, so parameter retrieval problems are very difficult. There are still many important theoretical issues under research, although g...It is well known that retrieval of parameters is usually ill-posed and highly nonlinear, so parameter retrieval problems are very difficult. There are still many important theoretical issues under research, although great success has been achieved in data assimilation in meteorology and oceanography. This paper reviews the recent research on parameter retrieval, especially that of the authors. First, some concepts and issues of parameter retrieval are introduced and the state-of-the-art parameter retrieval technology in meteorology and oceanography is reviewed briefly, and then atmospheric and oceanic parameters are retrieved using the variational data assimilation method combined with the regularization techniques in four examples： retrieval of the vertical eddy diffusion coefficient; of the turbulivity of the atmospheric boundary layer; of wind from Doppler radar data, and of the physical process parameters. Model parameter retrieval with global and local observations is also introduced.展开更多
Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest i...Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.展开更多
Considering the characteristics of nonlinear problems,a new method based on the L-curve method and including the concept of entropy was designed to select the regularization parameter in the one-dimensional variationa...Considering the characteristics of nonlinear problems,a new method based on the L-curve method and including the concept of entropy was designed to select the regularization parameter in the one-dimensional variational analysis-based sounding retrieval method.In the first iteration,this method uses an empirical regularization parameter derived by minimizing the entropy of variables.During subsequent iterations,it uses the L-curve method to select the regularization parameter in the vicinity of the regularization parameter selected in the last iteration.The new method was employed to select the regularization parameter in retrieving atmospheric temperature and moisture profiles from Atmospheric Infrared Sounder radiance measurements selected from the first day of each month in 2008.The results show that compared with the original L-curve method,the new method yields 5.5%and 2.5%improvements on temperature and relative humidity profiles,respectively.Compared with the discrepancy principle method,the improvements on temperature and relative humidity profiles are 1.6%and 2.0%,respectively.展开更多
Because the nonlinearity of actual physical processes can be expressed more precisely by the introduction of a non- linear term, the weakly nonlinear Prandtl model is one of the most effective ways to describe the pur...Because the nonlinearity of actual physical processes can be expressed more precisely by the introduction of a non- linear term, the weakly nonlinear Prandtl model is one of the most effective ways to describe the pure katabatic flow （no backgrotmd flow）. Features of the weak nonlinearity are reflected by two factors： the small parameter c and the gradually varying eddy thermal conductivity. This paper first shows how to apply the Wentzel-Kramers-Brillouin （WKB） method for the approximate solution of the weakly nonlinear Prandtl model, and then describes the retrieval of gradually varying eddy thermal conductivity from observed wind speed and perturbed potential temperature. Gradually varying eddy thermal conductivity is generally derived from an empirical parameterization scheme. We utilize wind speed and potential temperature measurements, along with the variational assimilation technique, to de- rive this parameter. The objective function is constructed by the square of the differences between the observation and model value. The new method is validated by numerical experiments with simulated measurements, revealing that the order of the root mean squre error is 10-2 and thus confirming the method＇s robustness. In addition, this me- thod is caoable of anti-interference, as it effectivelv reduces the influence of observation error.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No:90411006).
文摘The variational adjoint method was applied to retrieving the turbulivity of the atmospheric Ekman boundary layer along with the regularization principle, The validity of the method was verified by using the idealized data, and then the turbulivity profile and the geostrophic wind profile were retrieved through it for real observational wind filed data.
基金This work was supported by the National Natural Science Foundation of China （Grant No. 90411006）by the Shanghai Science and Technology Association （Grant No. 02DJ14032）.
文摘It is well known that retrieval of parameters is usually ill-posed and highly nonlinear, so parameter retrieval problems are very difficult. There are still many important theoretical issues under research, although great success has been achieved in data assimilation in meteorology and oceanography. This paper reviews the recent research on parameter retrieval, especially that of the authors. First, some concepts and issues of parameter retrieval are introduced and the state-of-the-art parameter retrieval technology in meteorology and oceanography is reviewed briefly, and then atmospheric and oceanic parameters are retrieved using the variational data assimilation method combined with the regularization techniques in four examples： retrieval of the vertical eddy diffusion coefficient; of the turbulivity of the atmospheric boundary layer; of wind from Doppler radar data, and of the physical process parameters. Model parameter retrieval with global and local observations is also introduced.
基金supported by the National Natural Science Foundation of China(Project No.42171361)the Research Grants Council of the Hong Kong Special Administrative Region,China,under Project PolyU 25211819the Hong Kong Polytechnic University under Projects 1-ZE8E and 1-ZVN6.
文摘Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY201406011)National Natural Science Foundation of China(41405038)
文摘Considering the characteristics of nonlinear problems,a new method based on the L-curve method and including the concept of entropy was designed to select the regularization parameter in the one-dimensional variational analysis-based sounding retrieval method.In the first iteration,this method uses an empirical regularization parameter derived by minimizing the entropy of variables.During subsequent iterations,it uses the L-curve method to select the regularization parameter in the vicinity of the regularization parameter selected in the last iteration.The new method was employed to select the regularization parameter in retrieving atmospheric temperature and moisture profiles from Atmospheric Infrared Sounder radiance measurements selected from the first day of each month in 2008.The results show that compared with the original L-curve method,the new method yields 5.5%and 2.5%improvements on temperature and relative humidity profiles,respectively.Compared with the discrepancy principle method,the improvements on temperature and relative humidity profiles are 1.6%and 2.0%,respectively.
基金Supported by the National Natural Science Foundation of China(41575026)
文摘Because the nonlinearity of actual physical processes can be expressed more precisely by the introduction of a non- linear term, the weakly nonlinear Prandtl model is one of the most effective ways to describe the pure katabatic flow （no backgrotmd flow）. Features of the weak nonlinearity are reflected by two factors： the small parameter c and the gradually varying eddy thermal conductivity. This paper first shows how to apply the Wentzel-Kramers-Brillouin （WKB） method for the approximate solution of the weakly nonlinear Prandtl model, and then describes the retrieval of gradually varying eddy thermal conductivity from observed wind speed and perturbed potential temperature. Gradually varying eddy thermal conductivity is generally derived from an empirical parameterization scheme. We utilize wind speed and potential temperature measurements, along with the variational assimilation technique, to de- rive this parameter. The objective function is constructed by the square of the differences between the observation and model value. The new method is validated by numerical experiments with simulated measurements, revealing that the order of the root mean squre error is 10-2 and thus confirming the method＇s robustness. In addition, this me- thod is caoable of anti-interference, as it effectivelv reduces the influence of observation error.
