Stratospheric aerosol extinction profiles are retrieved from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography(SCIAMACHY) limb scatter measurements.In the process of retrieval,the SCIATRAN radiative...Stratospheric aerosol extinction profiles are retrieved from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography(SCIAMACHY) limb scatter measurements.In the process of retrieval,the SCIATRAN radiative transfer model is used to simulate the limb scattering radiation received by the SCIAMACHY instrument,and an optimal estimation algorithm is used to calculate the aerosol extinction profiles.Sensitivity analyses are performed to investigate the impact of the surface albedo on the accuracy of the retrieved aerosol extinction profiles in the northern midlatitudes.It is found that the errors resulting from the bias of the assumed surface albedo in the retrieval are generally below 6%.The retrieved SCIAMACHY aerosol extinction profiles are compared with corresponding Stratospheric Aerosol and Gas Experiment(SAGE) II measurements,and the results indicate that for the zonal mean profiles,the SCIAMACHY retrievals show good agreement with SAGE II measurements,with the absolute differences being less than 2.3×10-5 km-1 from 14–25 km,and less than 5.9×10-6 km-1 from 25–35 km;and the relative differences being within 20% over the latitude range of 14–35 km.展开更多
In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaus...In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. This step replaces the resampling stage needed by most particle filters and relieves the effect caused by sample impoverishment. A nonlinear tracking problem shows that this new approach outperforms other related particle filters.展开更多
基金funded by the National Natural Science Foundation of China (Grant No.41275047)the National Basic Research Program of China (Grant No.2013CB955801)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No.XDA05100300)
文摘Stratospheric aerosol extinction profiles are retrieved from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography(SCIAMACHY) limb scatter measurements.In the process of retrieval,the SCIATRAN radiative transfer model is used to simulate the limb scattering radiation received by the SCIAMACHY instrument,and an optimal estimation algorithm is used to calculate the aerosol extinction profiles.Sensitivity analyses are performed to investigate the impact of the surface albedo on the accuracy of the retrieved aerosol extinction profiles in the northern midlatitudes.It is found that the errors resulting from the bias of the assumed surface albedo in the retrieval are generally below 6%.The retrieved SCIAMACHY aerosol extinction profiles are compared with corresponding Stratospheric Aerosol and Gas Experiment(SAGE) II measurements,and the results indicate that for the zonal mean profiles,the SCIAMACHY retrievals show good agreement with SAGE II measurements,with the absolute differences being less than 2.3×10-5 km-1 from 14–25 km,and less than 5.9×10-6 km-1 from 25–35 km;and the relative differences being within 20% over the latitude range of 14–35 km.
基金Sponsored by the National Security Major Basic Research Project of China(Grant No.973 -61334)
文摘In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. This step replaces the resampling stage needed by most particle filters and relieves the effect caused by sample impoverishment. A nonlinear tracking problem shows that this new approach outperforms other related particle filters.
