电离层F2层临界频率的短期和暴时预报研究
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摘要
本文依据我国近四十年电离层f0F2数据,分析了电离层的主要控制因素及电离层的长期和短期变化规律,建立了中低纬电离层短期预报、区域预报和电离层暴的预报方法。上述研究对空间天气的感知、电波环境预报、警报和效应评估能力具有重要意义。主要研究成果如下：
1)基于F10.7指数的短期预报方法。
依据我国中低纬地区的满洲里、长春、乌鲁木齐、北京、兰州、重庆、广州和海口8个电离层观测站1958-2006的f0F2和太阳射电流量F10.7数据,通过对电离层历史数据和F10.7的回归分析,提出单站电离层f0F2的短期预报方法,并用不同台站数据对预报性能进行了检验。分析在不同太阳活动水平、季节以及地方时条件下其预报误差的变化特性。表明,该方法能够预测未来1-3天的f0F2。
2)基于非线性网络的电离层短期预报方法。
分析了太阳活动和地磁活动对电离层f0F2的非线性影响,利用人工神经网络(NN)对单站电离层f0F2进行预报,分析了其预报误差在太阳活动高、低年和不同季节的变化特征。引入卡尔曼滤波和集合卡尔曼滤波方法对神经网络的预报结果做进一步修正和优化。其预报效果优于单纯的神经网络模型和IRI模型。
建立了利用支持向量机(SVM)单站电离层f0F2预报方法,分别实现了提前1-5小时和24小时f0F2预报。SVM预报结果与观测数据符合良好,比自相关分析法和Persistence方法更具有实用性。
3)电离层区域预报。
根据f0F2时间和空间相关性,利用人工神经网络建立了提前1-5小时的电离层f0F2区域预报方法,结合Kriging算法,引入电离层距离、经度因子和纬度因子等参数实现了电离层区域重构。利用我国电离层f0F2的数据,对该方法的重构精度进行了评估,实现了中国地区的电离层区域预报。
4)电离层暴的预报。
研究中低纬电离层暴时季节、位置和地方时的扰动形态,分析了中低纬电离层在地磁暴期间的响应特性。
利用地磁指数Dst和AE以及电离层临界频率f0F2的数据,通过分析磁扰期间成分扰动带的赤道向传播以及穿透电场引起的等离子体漂移对电离层的影响,提出一种提前1小时预报暴时低纬电离层f0F2的经验方法。通过对2004-2005年10次磁暴期间的预报结果进行检验表明,该方法能够预测低纬电离层暴的演化。
基于地磁时间累积指数ap(τ)比瞬时地磁指数相关性好的特点,通过分析地磁时间累积指数ap(τ)与f0F2的相关性,确定了τ值的最优取值。利用支持向量机网络,建立了提前1小时预报暴时f0F2方法。对2001-2006年67次磁暴期间的预报结果的检验表明,该方法能够较好的预测中纬电离层暴的演化,其预报性能优于STORM模型和Persistence模型。
Based on the analysis of the main controlling factors of the ionosphere and its long-term and short-term variation characteristics by using the f0F2 data of spanning nearly 40 years in China, this dissertation mainly focuses on establishing the ionospheric short-term, regional, and storm-time forecasting models at low and middle latitudes. This investigate is important to the ability of the sensation of space weather, radio environmental forecast, alert and effective estimation. The main results are listed as follows:
1) A short-term f0F2 forecasting model using the index F10.7
Based on solar radio flux F10.7 and hourly f0F2 values that span the period 1958-2006, a short-term predicting technique of the ionosphere f0F2 is introduced by using regression analysis of the observed values f0F2 and F10.7. Eight ionosonde stations used are Manzhouli, Changchun, Wulumuqi, Beijing, Lanzhou, Chongqing, Huangzhou and Haikou stations. The data of the different stations are used to test the forecasting performance respectively, and the results are compared by giving their root-mean-square errors according to different solar activity, season and local time. The results indicate that the method can forecast the f0F2 values effectively one day and three days ahead.
2) Short-term forecasting models of the ionospheric f0F2 using the nonlinear network
Due to its non-linear dynamic process associated with the F2 region of the ionosphere with solar photon flux, geomagnetic activity and global thermospheric circulation, a short-term forecasting method of f0F2 at a single station is introduced by using artificial neural network (NN). Their forecasted errors are analyzed, which vary with different season and solar activity. By introducing the Kalman Filter and the Ensemble Kalman Filter, the forecasting values of the neural network were adjusted and optimized after considering the anterior forecast errors and the trend of f0F2 variations. The results show that forecasting performance of the optimizing model is superior to that of the purely neural network and IRI.
By using Support Vector Machine (SVM), a different method for forecasting the ionosphere f0F2 at a single station one hour ahead, up to fives and 24 hours ahead is introduced, respectively. The results show that the predicted f0F2 has good agreement with observed data and the performance of the SVM model is superior to that of the autocorrelation and Persistence models. It reflects the potential application of this technique for forecasting f0F2.
3) The ionospheric f0F2 regional forecasting
Taken into account of the temporal and spatial correlativity of f0F2, a method for the ionospheric f0F2 regional forecasting, up to 5 hours ahead, is introduced by using the neural network. In addition, by introducing the ionospheric distance, latitude factor and longitude factor, the Kriging method has been proposed for the reconstruction of ionospheric foF2 in China region. Based on the measurements of the Chinese stations, the ionospheric reconstruction has been done, which give the estimates of the reconstruction accuracy in Chinese region.
4) The storm-time ionospheric f0F2 predictions
As the variation of f0F2 at storm-time depends obviously on latitude, season and local time, the responses of ionospheric f0F2 to the ionospheric storm at low and middle latitude are studied respectively.
Using the geomagnetic indices of Dst and AE and data of ionospheric critical frequency f0F2, an empirical method in predicting storm-time ionospheric f0F2 at a single station an hour in advance is brought out by analyzing the impacts resulted from the equatorward propagation of the composition disturbance zone and the plasma drift induced by the penetration electric field, as well as the local time effect. Ten storms during 2004-2005 are employed to validate the method by studying their predicting errors. They turn out that the empirical model can capture the evolutions of the storm fairly well at low-latitude.
As the correlation coefficient of f0F2 and the integrated geomagnetic index ap(τ) is better than that of foF2 and the instantaneous geomagnetic index ap. By analyzing the correlation coefficients of ap(τ) and f0F2, the best fit ofτis determined. Using the support vector machine (SVM), an empirical local ionospheric forecasting model has been developed to predict foF2 during disturbed geomagnetic conditions. The forecasted values foF2 are compared with that by the Persistence model and the STORM model during geomagnetic storm occurring from 2001 to 2006 at Lanzhou, which includes 67 storm events. As for the data sets used in this paper, the result shows the forecasting performance of SVM is better than the latter.
1)基于F10.7指数的短期预报方法。
依据我国中低纬地区的满洲里、长春、乌鲁木齐、北京、兰州、重庆、广州和海口8个电离层观测站1958-2006的f0F2和太阳射电流量F10.7数据,通过对电离层历史数据和F10.7的回归分析,提出单站电离层f0F2的短期预报方法,并用不同台站数据对预报性能进行了检验。分析在不同太阳活动水平、季节以及地方时条件下其预报误差的变化特性。表明,该方法能够预测未来1-3天的f0F2。
2)基于非线性网络的电离层短期预报方法。
分析了太阳活动和地磁活动对电离层f0F2的非线性影响,利用人工神经网络(NN)对单站电离层f0F2进行预报,分析了其预报误差在太阳活动高、低年和不同季节的变化特征。引入卡尔曼滤波和集合卡尔曼滤波方法对神经网络的预报结果做进一步修正和优化。其预报效果优于单纯的神经网络模型和IRI模型。
建立了利用支持向量机(SVM)单站电离层f0F2预报方法,分别实现了提前1-5小时和24小时f0F2预报。SVM预报结果与观测数据符合良好,比自相关分析法和Persistence方法更具有实用性。
3)电离层区域预报。
根据f0F2时间和空间相关性,利用人工神经网络建立了提前1-5小时的电离层f0F2区域预报方法,结合Kriging算法,引入电离层距离、经度因子和纬度因子等参数实现了电离层区域重构。利用我国电离层f0F2的数据,对该方法的重构精度进行了评估,实现了中国地区的电离层区域预报。
4)电离层暴的预报。
研究中低纬电离层暴时季节、位置和地方时的扰动形态,分析了中低纬电离层在地磁暴期间的响应特性。
利用地磁指数Dst和AE以及电离层临界频率f0F2的数据,通过分析磁扰期间成分扰动带的赤道向传播以及穿透电场引起的等离子体漂移对电离层的影响,提出一种提前1小时预报暴时低纬电离层f0F2的经验方法。通过对2004-2005年10次磁暴期间的预报结果进行检验表明,该方法能够预测低纬电离层暴的演化。
基于地磁时间累积指数ap(τ)比瞬时地磁指数相关性好的特点,通过分析地磁时间累积指数ap(τ)与f0F2的相关性,确定了τ值的最优取值。利用支持向量机网络,建立了提前1小时预报暴时f0F2方法。对2001-2006年67次磁暴期间的预报结果的检验表明,该方法能够较好的预测中纬电离层暴的演化,其预报性能优于STORM模型和Persistence模型。
Based on the analysis of the main controlling factors of the ionosphere and its long-term and short-term variation characteristics by using the f0F2 data of spanning nearly 40 years in China, this dissertation mainly focuses on establishing the ionospheric short-term, regional, and storm-time forecasting models at low and middle latitudes. This investigate is important to the ability of the sensation of space weather, radio environmental forecast, alert and effective estimation. The main results are listed as follows:
1) A short-term f0F2 forecasting model using the index F10.7
Based on solar radio flux F10.7 and hourly f0F2 values that span the period 1958-2006, a short-term predicting technique of the ionosphere f0F2 is introduced by using regression analysis of the observed values f0F2 and F10.7. Eight ionosonde stations used are Manzhouli, Changchun, Wulumuqi, Beijing, Lanzhou, Chongqing, Huangzhou and Haikou stations. The data of the different stations are used to test the forecasting performance respectively, and the results are compared by giving their root-mean-square errors according to different solar activity, season and local time. The results indicate that the method can forecast the f0F2 values effectively one day and three days ahead.
2) Short-term forecasting models of the ionospheric f0F2 using the nonlinear network
Due to its non-linear dynamic process associated with the F2 region of the ionosphere with solar photon flux, geomagnetic activity and global thermospheric circulation, a short-term forecasting method of f0F2 at a single station is introduced by using artificial neural network (NN). Their forecasted errors are analyzed, which vary with different season and solar activity. By introducing the Kalman Filter and the Ensemble Kalman Filter, the forecasting values of the neural network were adjusted and optimized after considering the anterior forecast errors and the trend of f0F2 variations. The results show that forecasting performance of the optimizing model is superior to that of the purely neural network and IRI.
By using Support Vector Machine (SVM), a different method for forecasting the ionosphere f0F2 at a single station one hour ahead, up to fives and 24 hours ahead is introduced, respectively. The results show that the predicted f0F2 has good agreement with observed data and the performance of the SVM model is superior to that of the autocorrelation and Persistence models. It reflects the potential application of this technique for forecasting f0F2.
3) The ionospheric f0F2 regional forecasting
Taken into account of the temporal and spatial correlativity of f0F2, a method for the ionospheric f0F2 regional forecasting, up to 5 hours ahead, is introduced by using the neural network. In addition, by introducing the ionospheric distance, latitude factor and longitude factor, the Kriging method has been proposed for the reconstruction of ionospheric foF2 in China region. Based on the measurements of the Chinese stations, the ionospheric reconstruction has been done, which give the estimates of the reconstruction accuracy in Chinese region.
4) The storm-time ionospheric f0F2 predictions
As the variation of f0F2 at storm-time depends obviously on latitude, season and local time, the responses of ionospheric f0F2 to the ionospheric storm at low and middle latitude are studied respectively.
Using the geomagnetic indices of Dst and AE and data of ionospheric critical frequency f0F2, an empirical method in predicting storm-time ionospheric f0F2 at a single station an hour in advance is brought out by analyzing the impacts resulted from the equatorward propagation of the composition disturbance zone and the plasma drift induced by the penetration electric field, as well as the local time effect. Ten storms during 2004-2005 are employed to validate the method by studying their predicting errors. They turn out that the empirical model can capture the evolutions of the storm fairly well at low-latitude.
As the correlation coefficient of f0F2 and the integrated geomagnetic index ap(τ) is better than that of foF2 and the instantaneous geomagnetic index ap. By analyzing the correlation coefficients of ap(τ) and f0F2, the best fit ofτis determined. Using the support vector machine (SVM), an empirical local ionospheric forecasting model has been developed to predict foF2 during disturbed geomagnetic conditions. The forecasted values foF2 are compared with that by the Persistence model and the STORM model during geomagnetic storm occurring from 2001 to 2006 at Lanzhou, which includes 67 storm events. As for the data sets used in this paper, the result shows the forecasting performance of SVM is better than the latter.
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