A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akai...A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.展开更多
Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation me...Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.展开更多
文摘A state space aproach for modeling nonstationary time series is employed in analysing gyro transient process. Based on the concept of smoothness priors constraint, the overall model is using the Kalman filter and Akaike's AIC criterion.Some numerical results of gyro drift models are obtained for analysis of gyro system. As the trend and irregular components of the observed time series can be modeled simultaneously, it is statistically more accurate and efficient than that modeled separately.
基金the Ministry of Finance of China and China Meteorological Administration for the Special Project of Meteorological Sector (Grant No. GYHY(QX)2007-615)the National Basic Research Program of China (Grant No. 2005CB321703)
文摘Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.
