Euler angle error model, rotation vector error model （RVE） and quaternion error model （QE） were qualitatively and quantitatively compared and an in-flight alignment filter algorithm was designed. This algorithm us...Euler angle error model, rotation vector error model （RVE） and quaternion error model （QE） were qualitatively and quantitatively compared and an in-flight alignment filter algorithm was designed. This algorithm used extended Kalman filter （EKF） based on RVE and QE separately avoi- ding the accuracy problem of the Euler angle model and used Rauch-Tung-Striebel（RTS） smoothing method to refine the accuracy recuperating the coning error for simplified RVE. Simulation results show that RVE and QE are more adapt for nonlinear filter estimation than the Euler angle model. The filter algorithm designed has more advantages in convergence speed, accuracy and stability comparing with the algorithm based on the three models separately.展开更多
Model errors offset by constant and time-variant optimal forcing vector approaches （termed COF and OFV, respectively） are analyzed within the framework of E1 Nifio simulations. Applying the COF and OFV approaches to...Model errors offset by constant and time-variant optimal forcing vector approaches （termed COF and OFV, respectively） are analyzed within the framework of E1 Nifio simulations. Applying the COF and OFV approaches to the well-known Zebiak-Cane model, we re-simulate the 1997 and 2004 E1 Nifio events, both of which were poorly degraded by a certain amount of model error when the initial anomalies were generated by coupling the observed wind forcing to an ocean com- ponent. It is found that the Zebiak-Cane model with the COF approach roughly reproduced the 1997 E1 Nifio, but the 2004 E1 Nifio simulated by this approach defied an ENSO classification, i.e., it was hardly distinguishable as CP-E1 Nifio or EP-E1 Nifio. In hoth E1 Nifio simulations, substituting the COF with the OFV improved the fit between the simulations and obser- vations because the OFV better manages the time-variant errors in the model. Furthermore, the OFV approach effectively corrected the modeled E1 Nifio events even when the observational data （and hence the computational time） were reduced. Such a cost-effective offset of model errors suggests a role for the OFV approach in complicated CGCMs.展开更多
文摘Euler angle error model, rotation vector error model （RVE） and quaternion error model （QE） were qualitatively and quantitatively compared and an in-flight alignment filter algorithm was designed. This algorithm used extended Kalman filter （EKF） based on RVE and QE separately avoi- ding the accuracy problem of the Euler angle model and used Rauch-Tung-Striebel（RTS） smoothing method to refine the accuracy recuperating the coning error for simplified RVE. Simulation results show that RVE and QE are more adapt for nonlinear filter estimation than the Euler angle model. The filter algorithm designed has more advantages in convergence speed, accuracy and stability comparing with the algorithm based on the three models separately.
基金sponsored by the National Basic Research Program of China(Grant No.2012CB955202)the National Public Benefit(Meteorology)Research Foundation of China(Grant No.GYHY201306018)the National Natural Science Foundation of China(Grant Nos.41176013 and41230420)
文摘Model errors offset by constant and time-variant optimal forcing vector approaches （termed COF and OFV, respectively） are analyzed within the framework of E1 Nifio simulations. Applying the COF and OFV approaches to the well-known Zebiak-Cane model, we re-simulate the 1997 and 2004 E1 Nifio events, both of which were poorly degraded by a certain amount of model error when the initial anomalies were generated by coupling the observed wind forcing to an ocean com- ponent. It is found that the Zebiak-Cane model with the COF approach roughly reproduced the 1997 E1 Nifio, but the 2004 E1 Nifio simulated by this approach defied an ENSO classification, i.e., it was hardly distinguishable as CP-E1 Nifio or EP-E1 Nifio. In hoth E1 Nifio simulations, substituting the COF with the OFV improved the fit between the simulations and obser- vations because the OFV better manages the time-variant errors in the model. Furthermore, the OFV approach effectively corrected the modeled E1 Nifio events even when the observational data （and hence the computational time） were reduced. Such a cost-effective offset of model errors suggests a role for the OFV approach in complicated CGCMs.
