A neural network-based ionospheric model for Arecibo

The Arecibo Observatory (18°N, 66°W) has the world’s largest single dish antenna (300 m diameter). Beyond radio astronomy it can also operate as an incoherent scatter radar and in that mode its figure-of-merit makes it also one of the most powerful world-wide. For the present purpose all electron density data available on the web, from the beginning with the first erratic measurements in 1966 up to 2004 inclusive, were downloaded. The measurements range from about 100 km to beyond 700 km and are essentially evenly distributed, i.e. not dedicated to measure specific geophysical events. From manually edited/inspected data a neural network (NN) was established with season, hour of the day, solar activity and Kp as the input parameters. The performance of this model is checked against a – likewise NN based – global model of foF2, a measure of the maximum electron density of the ionosphere. Considering the diverse data sources and assumptions of the two models it can be concluded that they agree remarkably well.     

Time constants in the ionosphere from neural network models

Neural network (NN) models for the low latitude and the polar ionosphere from the D- to the F-region were developed which are based on incoherent scatter radar data from Arecibo and EISCAT Svalbard, respectively. The various geophysical input parameters defining the NN are not only the ones that represent the time one wants to predict, but also the geophysical conditions prior to the time of the prediction. The optimum length of these preceding periods are derived for the two models are different, but a period of 60 days is a compromise acceptable for both latitudes. Furthermore from the Arecibo data time constants of electron density decay after sundown are derived which – arguably – are also relevant elsewhere, including the polar latitudes. Whereas at all altitudes the electron densities decay exponentially after sundown, below 300 km there is an additional variation with solar zenith angle.     

Neural network prediction of the topside electron content over the Euro-African sector derived from Swarm-A measurements

This study presents the first prediction results of a neural network model for the vertical total electron content of the topside ionosphere based on Swarm-A measurements. The model was trained on 5 years of Swarm-A data over the Euro-African sector spanning the period 1 January 2014 to 31 December 2018. The Swarm-A data was combined with solar and geomagnetic indices to train the NN model. The Swarm-A data of 1 January to 30 September 2019 was used to test the performance of the neural network. The data was divided into two main categories: most quiet and most disturbed days of each month. Each category was subdivided into two sub-categories according to the Swarm-A trajectory i.e. whether it was ascending or descending in order to accommodate the change in local time when the satellite traverses the poles. Four pairs of neural network models were implemented, the first of each pair having one hidden layer, and the second of each pair having two hidden layers, for the following cases: 1) quiet day-ascending, 2) quiet day-descending, 3) disturbed day-ascending, and 4) disturbed day-descending. The topside vertical total electron content predicted by the neural network models compared well with the measurements by Swarm-A. The model that performed best was the one hidden layer model in the case of quiet days for descending trajectories, with RMSE = 1.20 TECU, R = 0.76. The worst performance occurred during the disturbed descending trajectories where the one hidden layer model had the worst RMSE = 2.12 TECU, (R = 0.54), and the two hidden layer model had the worst correlation coefficient R = 0.47 (RMSE = 1.57).In all cases, the neural network models performed better than the IRI2016 model in predicting the topside total electron content. The NN models presented here is the first such attempt at comparing NN models for the topside VTEC based on Swarm-A measurements.     

Relationship between electron density and electron temperature as a function of solar activity

This paper attempts to examine the control of electron density and solar activity on the F-region electron temperature. This is achieved by obtaining coefficients relating electron temperature with electron density and solar activity by using incoherent scatter radar measurements at Arecibo for the period August 1966 to May 1977. These coefficients are then used to construct an empirical model of F-region electron temperature. The model values are compared with measurements at other locations and show reasonable agreement.     

地球同步轨道高能电子增强事件预报方法

分析了地球同步轨道高能电子通量增强事件的发生规律及其与太阳风和行星际磁场参数的关系，并在此基础上建立了基于人工神经网络的高能电子增强事件模式，经实测数据检验，预报模式可以对未来1天的高能电子通量进行预报，误差为8．2％，达到了较高水平．     

Study of the Enhancement Events of Relativistic Electron at the Geosynchronous Orbit

The high flux of energetic electron on geostationary orbit can induce many kinds of malfunction of the satellite there, within which the bulk-charging is the most significant that several broadcast satellite failures were confirmed to be due to this effect. The electron flux on geostationary orbit varies in a large range even up to three orders accompanied the passage of interplanetary magnetic cloud and the following geomagnetic disturbances. Upon the investigation of electron flux enhancement events, two types of events were partitioned as recurrent events and random ones. Both of the two kinds of events relate to the interplanetary conditions such as solar wind parameters, IMF etc and their evolution characters as well. As for the recurrent events, we found that, (1) all of the events exhibits periodic recurrence about 27 days, (2) significant increase of electron flux relates to interplanetary index and characters of their distribution, (3) the electron flux also has relation to solar activity index. An artificial neural network was constructed to estimate the flux I day ahead. The random electron flux enhancement events are rare and present different distribution figures to the recurrent ones. The figure of the random events and the conditions of their occurrence is also discussed in this paper.      

非相干雷达探测低电离层的结果分析

位于波多黎各的Arecibo非相干雷达可以获得低电离层电子和离子密度, 利用此非相干雷达数据对中纬度低电离层的运动特征进行研究. 得到了电子密度随时间和高度的变化 情况, 结果显著呈现出周日变化特征, 并分析了电子密度随高度的变化规律. 进一步对数据进行频谱分析, 深入研究低电离层电子密度的周日变化效应. 得到电子密度的高度剖面, 发现从F层底部到E层有明显的等离子体沉降. 低电离层的层结构特征及电子密度变化表明, 在该区域还存在不同程度的等离子体扰动, 由此对低电离层的作用因素 进行分析, 认为大气潮汐或声重波可能对低电离层产生扰动, 即低电离层与大气存在一定程度的耦合作用.      

Forecasting of DST variations from polar cap indices using neural networks

The 15-min averaged polar cap (PC) index was used as an input parameter for the Dst variation forecasting. The PC index is known to describe well the principal features of the solar wind as well as the total energy input to the magnetosphere. This allowed us to design a neural network able to forecast the Dst variations from 1 to 4 h ahead. 1998 PC and Dst data sets were used for training and testing and 1997 data sets was used for validation proposes. From the 15 moderate and strong geomagnetic storms observed during 1997, nine were successfully forecasted. In three cases the observed minimum Dst value was less than the predicted one, and only in three cases the neural network was not able to reproduce the features of the geomagnetic storm.     

Spectroscopy peculiarities of thermal plasma of electric arc discharge between electrodes with Zn admixtures

Plasma of the free burning electric arc between Ag–SnO₂–ZnO composite electrodes as well as brass electrodes were investigated. The plasma temperature distributions were obtained by Boltzmann plot method involving Cu I, Ag I or Zn I spectral line emissions. The electron density distributions were obtained from the width and from absolute intensity of spectral lines. The laser absorption spectroscopy was used for measurement of copper atom concentration in plasma. Plasma equilibrium composition was calculated using two independent groups of experimental values (temperature and copper atom concentration, temperature and electron density). It was found that plasma of the free burning electric arc between brass electrodes is in local thermodynamical equilibrium. The experimental verification of the spectroscopic data of Zn I spectral lines was carried out.     

Characteristics of nighttime E-region over Arecibo: Dependence on solar flux and geomagnetic variations

Electron concentration (Ne) inferred from Incoherent Scatter Radar (ISR) measurements has been used to determine the influence of solar flux and geomagnetic activity in the ionospheric E-region over Arecibo Observatory (AO). The approach is based on the determination of column integrated Ne, referred to as E-region total electron content (ErTEC) between 80 and 150 km altitude regions. The results discussed in this work are for the AO nighttime period. The study reveals higher ErTEC values during the low solar flux periods for all the seasons except for summer period. It is found that the E-region column abundance is higher in equinox periods than in the winter for low solar activity conditions. The column integrated Ne during the post-sunset/pre-sunrise periods always exceeds the midnight minima, independent of season or solar activity. This behavior has been attributed to the variations in the coupling processes from the F-region. The response of ErTEC to the geomagnetic variability is also examined for different solar flux conditions and seasons. During high solar flux periods, changes in Kp cause an ErTEC increase in summer and equinox, while producing a negative storm-like effect during the winter. Variations in ErTEC due to geomagnetic activity during low solar flux periods produce maximum variability in the E-region during equinox periods, while resulting in an increase/decrease in ErTEC before local midnight during the winter/summer periods, respectively.     

Prediction of geosynchronous electron fluxes using an artificial neural network driven by solar wind parameters

There are hundreds of satellites operating at the geosynchronous (GEO) orbit where relativistic electrons can cause severe damage. Thus, predicting relativistic electron fluxes is significant for spacecraft safety. In this study, using GOES satellite data during 2011–2020, we propose two neural network models with two hidden layers to predict geosynchronous relativistic electron fluxes at two energy channels (>0.8 MeV and > 2 MeV). The number of input neurons of the two channels (>0.8 MeV and > 2 MeV) are determined to be 36 and 44, respectively. The > 0.8 MeV model has 22 and 9 neurons in the hidden layers, while the > 2 MeV model has 25 and 15 neurons in the hidden layers. The input parameters include the north–south component of the interplanetary magnetic field, solar wind speed, solar wind dynamic pressure and solar wind proton density. Through the analysis of different time delays, we determine that the optimal time delays of two energy channels (>0.8 MeV and > 2 MeV) are 8 days and 10 days, respectively. The training set and validation set (Jan 2011-Dec 2018) are divided by the 10-fold cross-validation method, and the remaining data (Jan 2019-Feb 2020) is used to analyze the model performance as a test set. The prediction results of both energy channels show good agreement with satellite observations indicated by low RMSE (～0.3 cm^-2sr^-1s^?1), high PE (～0.8) and CC (～0.9). These results suggest that only using solar wind parameters is capable of obtaining reasonable predictions of geosynchronous relativistic electron fluxes.     

基于神经网络的磁平静期赤道电集流预测

利用BP神经网络技术分别对2008年后磁平静期印度扇区、秘鲁扇区以及CHAMP卫星的赤道电集流(EEJ)变化进行预测,其中神经网络训练数据为对应的2000-2007年磁平静期EEJ观测数据,输入参量为天数、地方时、太阳天顶角、太阳活动指数(F10.7)、太阴时以及卫星地理经度,输出参量为EEJ.对EEJ预测结果进行了统...     

1981年7月31日漠河地区日食的外电离层效应

本文利用漠河(磁径190°17′E, 磁纬42°18′N)地区接收的哨声和同时观测的电离层资料, 采用南北半球电子浓度不一定对称的假设, 以电离层垂测资料和哨声联合换算的模式法, 得到了1981年7月31日哨声色散常数的日食效应;并粗略地测定了日食期间, 漠河上空沿磁力线分布的电子浓度剖面, 磁通量管电子含量和等效标高的部分结果;此外, 还初步讨论了日食的外电离层效应.      

利用行星际监测数据进行地磁暴预报

利用全连接神经网络方法应用于地磁Dst指数的预报中.对ACE卫星探测的太阳风和行星际磁场及其变化对未来几小时的Dst指数的影响进行了统计分析,发现在这些行星际实测参数中,对Dst指数作用较为明显的是太阳风速度、太阳风质子密度和行星际磁场南向分量,同时,当前Dst指数实测值对今后几小时的Dst指数已有很强的制约作用.在统计分析的基础上,建立了全连接神经网络预报模型.由于采用了全连接神经网络结构,模式能够反映出太阳风、行星际磁场等参数与地磁Dst指数参数的复杂联系,可以自动建立输入参量的最佳组合方式,提高了预报精度.通过利用大量实测数据对神经网络模式进行训练,最终建立了利用优选的ACE卫星行星际监测数据提前2 h对Dst指数进行预报.通过检测,预报的误差为14.3%.      

An empirical model of electron temperature for low and middle latitudes in the 100–200 km height region

An empirical model of electron temperature (T_e) for low and middle latitudes is proposed in view of IRI. It is constructed on the basis of experimental data obtained at 100 to 200 km by probe and incoherent scatter methods. Below 150 km the model gives two T_e values: one from incoherent scatter data and another from probe measurements. The model can be used for all seasons for quiet geomagnetic conditions (Kp not greater 3) and at almost all levels of solar activity (F_10.7 between 70 and 200). It is presented in an analytical form that allows one to calculate T_e profiles for different latitudes, longitudes and at any season (day). Depending on geomagnetic latitude and solar zenith angle, electron temperature distributions are presented for two heights along with T_e profile variations during the day (at middle latitudes).     

An investigation of the spatial variations of the energetic trapped electrons at low altitudes

We have analyzed the trapped electron data (0.19–3.2 MeV) taken by the Japanese OHZORA satellite operated at 350–850 km altitude in polar orbit during 1984–1987 near solar minimum. The electron observations reveal all the global attributes of the quiet-time electron radiation belts, such as the South Atlantic Anomaly, the electron “slot”, and the outer radiation belt regions. The electron data are in general agreement with the NASA AE-8 electron model, but there are differences, particularly with respect to distinctive local-time variations in the slot region. In this paper, we present results from analyses of variations of the electron pitch angle distributions with local time, L-shell and altitude.     

Forecasting space weather: Can new econometric methods improve accuracy?

Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A_p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A_p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.     

Specific features of electron distributions at altitudes of 400 km

New experimental data obtained on the orbital station ‘MIR’ in 1991 during solar maximum are discussed. Electron fluxes with E_e>75 keV were registered for three different directions as well as for electrons with E_e>300 and 600 keV. Spatial and time distributions of electron fluxes in the trapping region are presented. In the inner radiation belt an additional maximum is observed at L=1.25–1.35, and the fluxes in the 22-05^h MLT interval are 2–3 orders of magnitude smaller, than during other local times. In this region a flattening of the electron spectrum is observed. The results obtained were compared with the AE-8 model.     

Search for a thickness parameter for the bottomside ionospheric F-region from incoherent-scatter electron-density profiles

Electron density-height profiles from the Arecibo incoherent scatter radar have been analysed for the period August 1974 to May 1977, to look for a thickness parameter for the bottomside F-region of the ionosphere. These profiles were obtained using short pulse lengths of 24 μs resulting in high altitude resolution. In the analysis, Gulyaeva's empirical relationship that an electron density of 0.5 NmF occurs at the height of 0.8 hmF2 is tested. Arecibo profiles indicate this electron density arises close to (0.84+0.02) hmF2 for most of the cases. However, there are some instances where large departures occur.     

Neutron monitor asymptotic directions of viewing during the event of 13 December 2006

During the recent ground level enhancement of 13 December 2006, also known as GLE70, solar cosmic ray particles of energy bigger that ∼500 MeV/nucleon propagated inside the Earth’s magnetosphere and finally accessed low-altitude satellites and ground level neutron monitors. The magnitude and the characteristics of this event registered at different neutron monitor stations of the worldwide network can be interpreted adequately on the basis of an estimation of the solar particle trajectories in the near Earth interplanetary space. In this work, an extended representation of the Earth’s magnetic field was realized applying the Tsyganenko 1989 model. Using a numerical back-tracing technique the solar proton trajectories inside the magnetospheric field of the Earth were calculated for a variety of particles, initializing their travel at different locations, covering a wide range of energies. In this way, the asymptotic directions of viewing were calculated for a significant number of neutron monitor stations, providing crucial information on the Earth’s “magnetospheric optics” for primary solar cosmic rays, on the top of the atmosphere, during the big solar event of December 2006. The neutron monitor network has been treated, therefore, as a multidimensional tool that gives insights into the arrival directions of solar cosmic ray particles as well as their spatial and energy distributions during extreme solar events.