集合卡尔曼滤波在电离层短期预报中的应用

提出了一种利用集合卡尔曼滤波对电离层f0F2短期预报结果进行优化的方法. 利用训练好的神经网络对f0F2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 f0F2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.      

利用神经网络预报中国地区电离层f0F2

利用神经网络技术并考虑太阳和地磁活动对电离层的影响,提出了一种提前5 h预报中国地区电离层临界频率f0F2的方法.网络输入包括时间、季节、地理纬度、太阳天顶角、最近一天的12个观测值(F-23,F-22,F-21,F-20,F-19,F-18,F-5,F-4,F-3,F-2,F-1,F0)和前30天滑动平均值(A-24,A-23,A-22,A-4,A-3A-2,A-1,A0),网络输出分别为未来5 h的f0F2值F+1,F+2,F+3,F+4,F+5.选取乌鲁木齐、长春、重庆和广州站1958-1968年间的数据训练网络,利用中国9个电离层站的历史数据检验网络,根据均方根误差衡量网络性能的好坏.结果表明,神经网络的预报结果能较好地符合实测数据.这说明利用神经网络实现中国地区电离层f0F2的时空预报是可行的.     

利用F_(10.7)短期预报电离层F_0F_2

通过对电离层历史数据和太阳射电流量F10.7的回归分析,提出了一种单站电离层f0F2的短期预报方法,以F10.7的流动平均值fc为输入,以未米3天的f0F2为输出,分别利用中国地区8个台站的数据进行检验,分析不同太阳活动水平、季节以及地方时预报误差的分布特征.结果表明,该方法能有效地预测未来1～3天的f0F2.该方法还可应用于其他电离层参量的短期预报.     

基于深度学习递归神经网络的电离层总电子含量经验预报模型

利用行星际太阳风参数与太阳活动指数、地磁活动指数、电离层总电子含量格点化地图数据,首次基于一种能处理时间序列的深度学习递归神经网络（Recurrent Neural Network,RNN）,建立提前24h的单站电离层TEC预报模型.对北京站（40°N,115°E）的预测结果显示,RNN对扰动电离层的预测误差低于反向传播神经网络（Back Propagation Neural Network,BPNN）0.49～1.46TECU,将太阳风参数加入预报因子模型后对电离层正暴预测准确率的提升可达16.8%.RNN对2001和2015年31个强电离层暴预报的均方根误差比BPNN低0.2TECU,将太阳风参数加入RNN模型可使31个事件的平均预报误差降低0.36～0.47TECU.研究结果表明深度递归神经网络比BPNN更适用于电离层TEC的短期预报,且在预报因子中加入太阳风数据对电离层正暴的预报效果有明显改善.      

电离层f_0F_2参数提前24小时预测

利用中国9个垂测站(海口、广州、重庆、拉萨、兰州、北京、乌鲁木齐、长春、满洲里)一个太阳周(1976-1986年)的数据资料,采用三层前向反馈神经网络(BP网络)实现了电离层F2层临界频率(f0F2)参数提前24h预测.通过对f0F2参数的时间序列及其与日地活动之间进行相关分析,确定f(t)(当前时刻f0F2)、经过变换的F10.7指数等5个参数作为神经网络的输入参数,并通过同时段训练法获得不同时刻的预测值,本文与自相关分析法进行了预测性能比较.结果表明,上述方法构建的神经网络可以达到较高的预测精度.针对暴时数据,对神经网络算法进行了改进,提高了神经网络法对暴时数据的适用性.     

NeQuick模式在亚太地区扇区电离层参数f0F2的预测能力

利用亚洲、澳大利亚地区8个电离层观测台站的F2层临界频率f0F2的历史观测数据,考察了NeQuick模式预报电离层基本参数f0F2在亚太扇区的适应性.对比分析表明,此模式能比较好地预测各地的F2层临界频率,其绝对误差在南半球各站相对北半球各站较大,太阳活动高年相对太阳活动低年较大,春秋季相对夏冬季较大.其误差均方根在太阳活动高年相对太阳活动低年较大.      

基于多种观测手段的东亚低纬电离层不规则体事件分析

利用海南台站（19.5°N,109.1°E,dip:13.6°N）和磁赤道区的多种地基和天基观测数据,对2011年11月20日观测到的电离层不规则体事件进行了分析.海南台站VHF雷达、电离层闪烁和数字测高仪的综合观测结果表明,当天日落附近发生了强的电离层不规则体事件,主要表现为雷达羽和强闪烁的形态.结合磁赤道区GPS和C/NOFS卫星观测结果进行分析可知,海南台站日落附近出现的雷达羽和强闪烁与南海磁赤道区产生的主等离子体泡存在明显联系.      

基于电离层暴时f_0F_2经验模型Kalman滤波短期预报

利用时间累积地磁指数印ap(T),建立了强地磁扰动条件下电离层f0F2与月中值相对偏差经验模型.该经验模型只在春秋季节和夏季特强地磁扰动条件ap(T)>100,即时间累积地磁指数大于100时达到理想精度.尝试利用气象预报中常用的Kalman滤波方法对模型的系数进行实时修正,以提高预报精度,并对长春站1986-1995年近一个太阳周f0F2数据进行提前一小时预报试验.冬季预报均方根误差为0.76MHz,春秋季节为0.68MHz,夏季为0.61MHz.在特强地磁扰动条件下,预测误差在0.87～1.43MHz之间.该预报方法同时与包含暴时修正模型STORM的国际参考电离层IRI2001进行了比较,展示了Kalman滤波方法实时修正模型系数的能力和良好的应用前景.     

1998年5月磁暴事件期间全球电离层响应形态与机制的研究

利用全球40余个电离层台站的f0F2观测数据，采取对经度进行分区处理的方法，通过计算各台站f0F2参数对其月中的偏离百分比，对1998年5月大磁爆期间的电离层扰动形态进行了分析，并对可能的扰动机制进行了探讨，结果表明本次磁暴事件中，在磁暴主要活动相期间的电离层扰动与暴环流理论所描述的电脑层扰动特征相符，但在恢复相后期欧洲扇区台站出现的正相扰动似不能用暴环流理论来解释，它可能对应期间的行星行条件（太阳风与行星际磁场）的变化有关。     

中低纬电离层不同季节地磁暴响应的事例分析

利用中国中低纬台站漠河（53.5°N,122.3°E）、北京（40.3°N,116.2°E）、武汉（30.5°N,114.2°E）和三亚（18.3°N,109.6°E）的电离层观测数据,对比分析了4个台站电离层参数在2015年不同季节4个地磁扰动事件期间的变化特征.结果表明,4个磁暴事件期间电离层的响应特征并不完全一致,有着明显的季节特征,春季、夏季和秋季电离层以负相扰动为主,冬季以正相扰动为主.分析发现,中性成分O/N₂的降低与电离层负相扰动有关,但三亚地区的负相扰动还与扰动发电机电场相关.正相扰动的机制在不同事件中并不相同,穿透电场可能是引起春季磁暴事件期间电离层短时正暴效应的原因,而冬季长时间的正暴效应则是扰动电场和中性风共同作用的结果.      

The variability and predictability of the IRI B0, B1 parameters over Grahamstown,South Africa

The International Reference Ionosphere (IRI) parameters B₀ and B₁ provide a representation of the thickness and shape, respectively, of the F2 layer of the bottomside ionosphere. These parameters can be derived from electron density profiles that are determined from vertical incidence ionograms. This paper aims to illustrate the variability of these parameters for a single mid latitude station and demonstrate the ability of the Neural Network (NN) modeling technique for developing a predictive model for these parameters. Grahamstown, South Africa (33.3°S, 26.5°E) was chosen as the mid latitude station used in this study and the B₀ and B₁ parameters for an 11 year period were determined from electron density profiles recorded at that station with a University of Massachusetts Lowell Center for Atmospheric Research (UMLCAR) Digisonde. A preliminary single station NN model was then developed using the Grahamstown data from 1996 to 2005 as a training database, and input parameters known to affect the behaviour of the F2 layer, such as day number, hour, solar and magnetic indices. An analysis of the diurnal, seasonal and solar variations of these parameters was undertaken for the years 2000, 2005 and 2006 using hourly monthly median values. Comparisons between the values derived from measured data and those predicted using the two available IRI-2001 methods (IRI tables and Gulyaeva, T. Progress in ionospheric informatics based on electron density profile analysis of ionograms. Adv. Space Res. 7(6), 39–48, 1987.) and the newly developed NN model are also shown in this paper. The preliminary NN model showed that it is feasible to use the NN technique to develop a prediction tool for the IRI thickness and shape parameters and first results from this model reveal that for the mid latitude location used in this study the NN model provides a more accurate prediction than the current IRI model options.     

武汉地区电离层电子浓度总含量的统计经验模式研究

由武汉电离层观象台一个太阳黑子周期（1980-1990年）的实测电离层电子浓度总含量（TEC）资料，统计分析得出了武汉地区的一个TEC经验模式，模式很好地再现了武汉地区的TEC观测值，其预测误差在太阳活动高年稍太，低年较小；在春秋两季稍大，冬夏两季较小；在当地时间白天和傍晚稍大，夜间和早晨较小。此外，与国际参考电离层模式IRI的计算结果比较，本模式预测的TEC值更接近于实际观测结果，同时，本文也初步探讨了TEC的半年变化特征和冬季异常现象。     

Predicting indices of the ionosphere response to solar activity for the ascending phase of the 25th solar cycle

The international reference ionosphere, IRI, and its extension to plasmasphere, IRI-Plas, models require reliable prediction of solar and ionospheric proxy indices of solar activity for nowcasting and forecasting of the ionosphere parameters. It is shown that IRI prediction errors could increase for the F2 layer critical frequency foF2 and the peak height hmF2 due to erroneous predictions of the ionospheric global IG index and the international sunspot number SSN1 index on which IRI and IRI-Plas models are built. Regression relation is introduced to produce daily SSN1 proxy index from new time series SSN2 index provided from June 2015, after recalibration of sunspots data. To avoid extra errors of the ionosphere model a new solar activity prediction (SAP) model for the ascending part of the solar cycle SC25 is proposed which expresses analytically the SSN1 proxy index and the 10.7-cm radio flux F10.7 index in terms of the phase of the solar cycle, Φ. SAP model is based on monthly indices observed during the descending part of SC24 complemented with forecast of time and amplitude for SC25 peak. The strength of SC25 is predicted to be less than that of SC24 as shown with their amplitudes for eight types of indices driving IRI-Plas model.     

Comparative study of foF2 measurements with IRI-2007 model predictions during extended solar minimum

The unusually deep and extended solar minimum of cycle 23/24 made it very difficult to predict the solar indices 1 or 2 years into the future. Most of the predictions were proven wrong by the actual observed indices. IRI gets its solar, magnetic, and ionospheric indices from an indices file that is updated twice a year. In recent years, due to the unusual solar minimum, predictions had to be corrected downward with every new indices update. In this paper we analyse how much the uncertainties in the predictability of solar activity indices affect the IRI outcome and how the IRI values calculated with predicted and observed indices compared to the actual measurements. Monthly median values of F2 layer critical frequency (foF2) derived from the ionosonde measurements at the mid-latitude ionospheric station Juliusruh were compared with the International Reference Ionosphere (IRI-2007) model predictions. The analysis found that IRI provides reliable results that compare well with actual measurements, when the definite (observed and adjusted) indices of solar activity are used, while IRI values based on earlier predictions of these indices noticeably overestimated the measurements during the solar minimum. One of the principal objectives of this paper is to direct attention of IRI users to update their solar activity indices files regularly. Use of an older index file can lead to serious IRI overestimations of F-region electron density during the recent extended solar minimum.     

Monthly mean foF2 model for an African low-latitude station and comparison with IRI

We have employed the hourly values of the ionospheric F-region critical frequency (foF2) obtained from Ouagadougou ionosonde, Burkina Faso (geographic coordinates 12° N, 1.8° W) during the interval of 1985–1995 (solar cycle 22) and solar radio flux of 10 cm wavelength (F10.7) to develop a local model (LM) for the African low-latitude station. The model was developed from regression analysis method, using the two-segmented regression analysis. We validated LM with foF2 data from Korhogo observatory, Cote d’Ivorie (geographical coordinates 9.3° N, 5.4° W). LM as well as the International Reference Ionosphere (IRI) agrees well with observations. LM gave some improvement on the IRI-predicted foF2 values at the sunrise (06 LT) at all solar flux levels and in all seasons except June solstice. The performance of the models at the representing the salient features of the equatorial foF2 was presented. Considering daytime and nighttime performances, LM and IRI are comparable in low solar activity (LSA), LM performed better than IRI in moderate solar activity (MSA), while IRI performed better than LM in high solar activity (HSA). CCIR has a root mean square error (r.m.s.e), which is only 0.10 MHz lower than that of LM while LM has r.m.s.e, which is about 0.05 MHz lower than that of URSI. In general, our result shows that performance of IRI, especially the CCIR option of the IRI, is quite comparable with the LM. The improved performance of IRI is a reflection of the numerous contributions of ionospheric physicists in the African region, larger volume of data for the IRI and the diversity of data sources, as well as the successes of the IRI task force activities.     

A prediction model of short-term ionospheric foF2 based on AdaBoost

In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.     

A regional model for the prediction of M(3000)F2 over East Asia

Research on empirical or physical models of ionospheric parameters is one of the important topics in the field of space weather and communication support services. To improve the accuracy of predicting the monthly median ionospheric propagating factor at 3000 km of the F2 layer (identified as M(3000)F2) for high frequency radio wave propagation, a model based on modified orthogonal temporal–spatial functions is proposed. The proposed model has three new characteristics: (1) The solar activity parameters of sunspot number and the 10.7-cm solar radio flux are together introduced into temporal reconstruction. (2) Both the geomagnetic dip and its modified value are chosen as features of the geographical spatial variation for spatial reconstruction. (3) A series of harmonic functions are used to represent the M(3000)F2, which reflects seasonal and solar cycle variations. The proposed model is established by combining nonlinear regression for three characteristics with harmonic analysis by using vertical sounding data over East Asia. Statistical results reveal that M(3000)F2 calculated by the proposed model is consistent with the trend of the monthly median observations. The proposed model is better than the International Reference Ionosphere (IRI) model by comparison between predictions and observations of six station, which illustrates that the proposed model outperforms the IRI model over East Asia. The proposed method can be further expanded for potentially providing more accurate predictions for other ionospheric parameters on the global scale.     

Equatorial predictions from a new neural network based global foF2 model

A new neural network (NN) based global empirical model for the foF2 parameter, which represents the peak ionospheric electron density, has been developed using extended temporal and spatial geophysical relevant inputs. It has been proposed that this new model be considered as a suitable replacement for the International Union of Radio Science (URSI) and International Radio Consultative Committee (CCIR) model options currently used within the International Reference Ionosphere (IRI) model for the purpose of F2 peak electron density predictions. The most recent version of the model has incorporated data from 135 global ionospheric stations including a number of equatorial stations.     

Correlation of IRTAM and FPMU data confirming the application of IRTAM to support ISS Program safety

A “Real-Time” plasma hazard assessment process was developed to support International Space Station (ISS) Program real-time decision-making providing solar array constraint relief information for Extravehicular Activities (EVAs) planning and operations. This process incorporates real-time ionospheric conditions, ISS solar arrays’ orientation, ISS flight attitude, and where the EVA will be performed on the ISS. This assessment requires real-time data that is presently provided by the Floating Potential Measurement Unit (FPMU) which measures the ISS floating potential (FP), along with ionospheric electron number density (Ne) and electron temperature (Te), in order to determine the present ISS environment. Once the present environment conditions are correlated with International Reference Ionosphere (IRI) values, IRI is used to forecast what the environment could become in the event of a severe geomagnetic storm. If the FPMU should fail, the Space Environments team needs another source of data which is utilized to support a short-term forecast for EVAs. The IRI Real-Time Assimilative Mapping (IRTAM) model is an ionospheric model that uses real-time measurements from a large network of digisondes to produce foF2 and hmF2 global maps in 15?min cadence. The Boeing Space Environments team has used the IRI coefficients produced in IRTAM to calculate the Ne along the ISS orbital track. The results of the IRTAM model have been compared to FPMU measurements and show excellent agreement. IRTAM has been identified as the FPMU back-up system that will be used to support the ISS Program if the FPMU should fail.     

A new global F2 peak electron density model for the International Reference Ionosphere (IRI)

A new neural network (NN) based global empirical model for the F2 peak electron density (NmF2) has been developed using extended temporal and spatial geophysical relevant inputs. Measured ground based ionosonde data, from 84 global stations, spanning the period 1995 to 2005 and, for a few stations from 1976 to 1986, obtained from various resources of the World Data Centre (WDC) archives (Space Physics Interactive Data Resource SPIDR, the Digital Ionogram Database, DIDBase, and IPS Radio and Space Services) have been used for training a NN. The training data set includes all periods of quiet and disturbed magnetic activity. A comprehensive comparison for all conditions (e.g., magnetic storms, levels of solar activity, season, different regions of latitudes, etc.) between foF2 value predictions using the NN based model and International Reference Ionosphere (IRI) model (including both the International Union of Radio Science (URSI) and International Radio Consultative Committee (CCIR) coefficients) with observed values was investigated. The root-mean-square (RMS) error differences for a few selected stations are presented in this paper. The results of the foF2 NN model presented in this work successfully demonstrate that this new model can be used as a replacement option for the URSI and CCIR maps within the IRI model for the purpose of F2 peak electron density predictions.