Lidar has been used extensively in the area of atmospheric aerosol measurement.Two unknowns at the reference altitude,the lidar ratio and the backscatter coefficient,need to be resolved from the lidar equation.In the ...Lidar has been used extensively in the area of atmospheric aerosol measurement.Two unknowns at the reference altitude,the lidar ratio and the backscatter coefficient,need to be resolved from the lidar equation.In the actual application,these two values are difficult to obtain,particularly the backscatter coefficient.To better characterize the optical properties of aerosols,optical thickness,and attenuated backscatter obtained by other instruments are usually used as the input for joint inversion.However,this method is limited by location and time.In this study,the authors propose a new method for aerosol retrieval by using Mie scattering lidar data to solve this problem.The authors take the horizontal aerosol extinction coefficient as the constraint to begin the iteration until a self-consistent aerosol vertical profile was obtained.By comparing their results with Aerosol Robotic Network(AERONET) data,the authours determine that the aerosol extinction coefficient obtained by combining horizontal and vertical lidar observations is more precise than that obtained by using the traditional Fernald method.This new method has been adopted for retrieving the extinction coefficient of aerosols during the observation days.展开更多
In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net-...In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net- works based on the monthly average grid emission data （1979-2008） from different geographical regions. A three-dimensional global chemical transport model, God- dard Earth Observing System （GEOS）-Chem, was applied to verify the effectiveness of the networks. The results showed that the networks captured the annual increasing trend and interannual variation of ff well. The difference between the simulations with the original and predicted ff ranged from -1 ppmv to 1 ppmv globally. Meanwhile, the authors evaluated the observed and simulated north-south gradient of the atmospheric CO2 concentrations near the surface. The two simulated gradients appeared to have a similar changing pattern to the observations, with a slightly higher background CO2 concentration, - 1 ppmv. The results indicate that the Elman neural network is a useful tool for better understanding the spatial and tem- poral distribution of the atmospheric C02 concentration and ft.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.41127901)
文摘Lidar has been used extensively in the area of atmospheric aerosol measurement.Two unknowns at the reference altitude,the lidar ratio and the backscatter coefficient,need to be resolved from the lidar equation.In the actual application,these two values are difficult to obtain,particularly the backscatter coefficient.To better characterize the optical properties of aerosols,optical thickness,and attenuated backscatter obtained by other instruments are usually used as the input for joint inversion.However,this method is limited by location and time.In this study,the authors propose a new method for aerosol retrieval by using Mie scattering lidar data to solve this problem.The authors take the horizontal aerosol extinction coefficient as the constraint to begin the iteration until a self-consistent aerosol vertical profile was obtained.By comparing their results with Aerosol Robotic Network(AERONET) data,the authours determine that the aerosol extinction coefficient obtained by combining horizontal and vertical lidar observations is more precise than that obtained by using the traditional Fernald method.This new method has been adopted for retrieving the extinction coefficient of aerosols during the observation days.
基金supported by the Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Sciences (Grant No. XDA05040000)the National Natural Science Foundation of China (Grant Nos. 41005023 and 41275046)
文摘In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net- works based on the monthly average grid emission data （1979-2008） from different geographical regions. A three-dimensional global chemical transport model, God- dard Earth Observing System （GEOS）-Chem, was applied to verify the effectiveness of the networks. The results showed that the networks captured the annual increasing trend and interannual variation of ff well. The difference between the simulations with the original and predicted ff ranged from -1 ppmv to 1 ppmv globally. Meanwhile, the authors evaluated the observed and simulated north-south gradient of the atmospheric CO2 concentrations near the surface. The two simulated gradients appeared to have a similar changing pattern to the observations, with a slightly higher background CO2 concentration, - 1 ppmv. The results indicate that the Elman neural network is a useful tool for better understanding the spatial and tem- poral distribution of the atmospheric C02 concentration and ft.
