摘要
Polar motion depicts the slow changes in the locations of the poles due to the earth's internal instantaneous axis of rotation. The LS+AR model is recognized as one of the best models for polar motion prediction.Through statistical analysis of the time series of the LS+AR model's short-term prediction residuals,we found that there is a good correlation of model prediction residuals between adjacent terms.These indicate that the preceding model prediction residuals and experiential adjustment matrixes can be used to correct the next prediction results,thereby forming a new LS+AR model with additional error correction that applies to polar motion prediction.Simulated predictions using this new model revealed that the proposed method can improve the accuracy and reliability of polar motion prediction.In fact,the accuracies of ultra short-term and short-term predictions using the new model were equal to the international best level at present.
Polar motion depicts the slow changes in the locations of the poles due to the earth＇s internal instantaneous axis of rotation. The LS＋AR model is recognized as one of the best models for polar motion prediction. Through statistical analysis of the time series of the LS＋AR model＇s shortterm prediction residuals, we found that there is a good correlation of model prediction re siduals between adjacent terms. These indicate that the preceding model prediction residuals and experiential adjustment ma trixes can be used to correct the next prediction results, thereby forming a new LS＋AR model with additional error correction that applies to polar motion prediction. Simulated predictions using this new model revealed that the proposed method can im prove the accuracy and reliability of polar motion prediction. In fact, the accuracies of ultra shortterm and shortterm predic tions using the new model were equal to the international best level at present.
基金
supported by the National Natural Science Foundation of China(Grant Nos.41021061&41174012)
