CAJ | 학술논문

提出了一种利用集合卡尔曼滤波对电离层f_0F_2短期预报结果进行优化的方法.利用训练好的神经网络对f_0F_2进行提前1～24 h的预报,考虑前一天预报误差的反馈信息,动态跟踪f_0F_2的变化趋势,引入集合卡尔曼滤波对神经网络的预报结果实行进一步修正和优化.实验结果表明,此方法的预报效果优于单纯的神经网络模型和IRI模型.此方法还可以应用于其他电离层参量的短期预报.
제출료일충이용집합잡이만려파대전리층f_0F_2단기예보결과진행우화적방법.이용훈련호적신경망락대f_0F_2진행제전1～24 h적예보,고필전일천예보오차적반궤신식,동태근종f_0F_2적변화추세,인입집합잡이만려파대신경망락적예보결과실행진일보수정화우화.실험결과표명,차방법적예보효과우우단순적신경망락모형화IRI모형.차방법환가이응용우기타전리층삼량적단기예보.
The short-term ionospheric forecast mainly denotes a prediction from hours to days in advance on time scale. This task needs a nonlinear recursion between the training data and the target one picked from the measurements, even by using complicated mathematic operations. Recently, an optimized arithmetic in data recursions named as Ensemble Kalman Filter (EnKF) has been widely used in temperature and rainfall predictions and even in ionospheric data assimilations. In this paper an optimizing method for short-term ionospheric f_0F_2 forecast was provided based on the Ensemble Kalman Filter technique. Firstly, the hourly f_0F_2 values with 1～24 hour in advance were forecasted by the neural network method. Then the forecasted values by the neural network were adjusted and optimized by introducing the Ensemble Kalman Filter after taking into account of the anterior forecast error8 and the trend of f_0F_2 variations. The forecasted errors are binned with seasons and stations and compared with those of purely neural network and International Reference Ionosphere (IRI)to validate this method, The results show that the forecasting performance by the optimizing model is superior to that by the purely neural network and IRI. This indicated that the Ensemble Kalman Filter tecluuque could be an efficient tool in ionospheric short-term forecast. Furthermore, this optimizing method can also be applied to the short-term forecasting of other ionospheric parameters.