The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful ch...The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced initial perturbations for the ensemble Kalman filter (EnKF) data assimilation, with a reasonable consideration of the physical relationships between different model variables. The scheme is applied in assimilation experiments with a global spectral atmospheric model and with real observations. The proposed perturbation method is compared to the commonly used method of spatially-correlated random perturbations. The comparisons show that the model uncertainties prior to the first analysis time, which are forecasted from the balanced ensemble initial fields, maintain a much more reasonable spread and a more accurate forecast error covariance than those from the randomly perturbed initial fields. The analysis results are further improved by the balanced ensemble initialization scheme due to more accurate background information. Also, a 20-day continuous assimilation experiment shows that the ensemble spreads for each model variable are still retained in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the randomly perturbed initialization scheme decrease and collapse rapidly.展开更多
The present study compares the spatial and temporal characteristics of the Madden-Julian Oscillation(MJO)in Fengyun-3B(FY-3B)polar-orbiting satellite reprocessed outgoing longwave radiation(OLR)data and NOAA OLR data ...The present study compares the spatial and temporal characteristics of the Madden-Julian Oscillation(MJO)in Fengyun-3B(FY-3B)polar-orbiting satellite reprocessed outgoing longwave radiation(OLR)data and NOAA OLR data during 2011-2020.The spatial distributions of climatological mean and intraseasonal standard deviation of FY-3B OLR during boreal winter(November-April)and boreal summer(May-October)are highly consistent with those of NOAA OLR.The FY-3B and NOAA OLRs display highly consistent features in the wavenumber-frequency spectra,the occurrence frequency of MJO active days,the eastward propagation of MJO along the equator,and the interannual variability of MJO according to diagnoses using the all-season multivariate EOF analysis.These results indicate that the FY-3B OLR produced by the polar-orbiting satellites is of high quality and worthy of global application.展开更多
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX1-YW-12-03)the National Basic Research Program of China (Grant No. 2010CB951901)the National Natural Science Foundation of China (Grant No. 40805033)
文摘The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced initial perturbations for the ensemble Kalman filter (EnKF) data assimilation, with a reasonable consideration of the physical relationships between different model variables. The scheme is applied in assimilation experiments with a global spectral atmospheric model and with real observations. The proposed perturbation method is compared to the commonly used method of spatially-correlated random perturbations. The comparisons show that the model uncertainties prior to the first analysis time, which are forecasted from the balanced ensemble initial fields, maintain a much more reasonable spread and a more accurate forecast error covariance than those from the randomly perturbed initial fields. The analysis results are further improved by the balanced ensemble initialization scheme due to more accurate background information. Also, a 20-day continuous assimilation experiment shows that the ensemble spreads for each model variable are still retained in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the randomly perturbed initialization scheme decrease and collapse rapidly.
基金Supported by the National Key Research and Development Program of China (2018YFB0504900 and 2018YFB0504905)。
文摘The present study compares the spatial and temporal characteristics of the Madden-Julian Oscillation(MJO)in Fengyun-3B(FY-3B)polar-orbiting satellite reprocessed outgoing longwave radiation(OLR)data and NOAA OLR data during 2011-2020.The spatial distributions of climatological mean and intraseasonal standard deviation of FY-3B OLR during boreal winter(November-April)and boreal summer(May-October)are highly consistent with those of NOAA OLR.The FY-3B and NOAA OLRs display highly consistent features in the wavenumber-frequency spectra,the occurrence frequency of MJO active days,the eastward propagation of MJO along the equator,and the interannual variability of MJO according to diagnoses using the all-season multivariate EOF analysis.These results indicate that the FY-3B OLR produced by the polar-orbiting satellites is of high quality and worthy of global application.
