Comparison of peak characteristics of F2 ionospheric layer over Tehran region at a low solar activity period with IRI-2001 and IRI-2007 models predictions

In the present work values of peak electron density (NmF2) and height of F2 ionospheric layer (hmF2) over Tehran region at a low solar activity period are compared with the predictions of the International Reference Ionosphere models (IRI-2001 and IRI-2007). Data measured by a digital ionosonde at the ionospheric station of the Institute of Geophysics, University of Tehran from July 2006 to June 2007 are used to perform the calculations. Formulations proposed by  and  are utilized to calculate the hmF2. The International Union of Radio Science (URSI) and International Radio Consultative Committee (CCIR) options are employed to run the IRI-2001 and IRI-2007 models. Results show that both IRI-2007 and IRI-2001 can successfully predict the NmF2 and hmF2 over Tehran region. In addition, the study shows that predictions of IRI-2007 model with CCIR coefficient has closer values to the observations. Furthermore, it is found that the monthly average of the percentage deviation between the IRI models predictions and the values of hmF2 and NmF2 parameters are less than 10% and 21%, respectively.     

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.     

Investigation of the relationship between ionospheric foF2 and earthquakes

Variations of the ionospheric F2 region critical frequency (foF2) have been investigated statistically before earthquakes during 1980–2008 periods in Japan area. Ionosonde data was taken from Kokubunji station which is in the earthquake preparation zone for all earthquakes. Standard Deviations and Inter-Quartile Range methods are applied to the foF2 data. It is observed that there are anomalous variations in foF2 before earthquakes. These variations can be regarded as ionospheric precursors and may be used for earthquake prediction.     

Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks

The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models from limited input–output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. In this study, the process of the Dst index is treated to be a structure-unknown system, where the solar wind parameter (VBs) and the solar wind dynamic pressure (P) are the system inputs, and the Dst index is the system output. A novel multiscale RBF (MSRBF) network is introduced to represent such a two-input and single-output system, where the Dst index is related to the solar wind parameter and the dynamic pressure, via a hybrid network model consisting of two submodels: a linear part that reflects the linear relationship between the output and the inputs, and a nonlinear part that captures the effect of the interacting contribution of past observations of the inputs and the output, on the current output. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using a forward orthogonal regression (FOR) algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems.     

A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network

There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde’s observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde’s data in the polar regions. The differences between ANN-based maps and references show that the discrepancies between two ionospheric detecting techniques are proportional to the intensity of solar radiation. Besides, a new method based on the ANN technique is proposed to reduce the discrepancies for improving ionospheric models developed by multiple measurements, the results indicate that the RMSEs of ANN models optimized by the method are 14–25% lower than the models without the application of the method. Furthermore, the ionospheric model built by the multiple measurements with the application of the method is more powerful in capturing the ionospheric dynamic physics features, such as equatorial ionization, Weddell Sea, mid-latitude summer nighttime and winter anomalies. In conclusion, the new method is significant in improving the accuracy and physical characteristics of an ionospheric model based on multi-source observations.     

利用神经网络逆控制系统提高 Turbo码译码性能

对Turbo码译码逆模型建立问题,提出使用神经网络结构的非线性滤波器来建立Turbo码译码自适应逆模型.采用最优常系数比例因子统计得到Turbo码期望衰减系数,通过利用期望衰减系数训练神经网络非线性自回归外输入NARX滤波器,建立全局范围内的Turbo码译码逆输入输出映射模型.在线性逆控制系统中采用该自适应逆模型,与非线性逆控制结构的自适应逆控制系统相比,具有系统结构简单、运算量小等特点.仿真结果表明在信噪比大于0?dB时,该自适应逆模型算法收敛迅速、稳定,计算误差保持在较小的范围之内.自适应逆译码模型从译码机理角度提供了一种改善译码性能的新途径.     

Short-term forecasting regional model to predict M(3000)F2 over the European sector: Comparisons with the IRI model during moderate,disturbed, and very disturbed geomagnetic conditions

The hourly measurements of M(3000)F2 (M(3000)F2_meas) and the hourly quiet-time values of M(3000)F2 (M(3000)F2_QT) relative to the ionospheric observatories of Poitiers, Lannion, Dourbes, Slough, Rome, Juliusruh, Kaliningrad, Uppsala, Lyckesele, Sodankyla, and Kiruna as well as the hourly time-weighted accumulation series derived from the geomagnetic planetary index ap (ap(τ)), were considered during the period January 1957–December 2003 and used for the development of 11 short-term forecasting local models (STFLM) of M(3000)F2.     

Pre-storm behaviour of NmF2 and TEC (GPS) over equatorial and low latitude stations in the Indian sector

The pre-storm behavior of NmF₂ and TEC over an equatorial station, Trivandrum (8.47°N, 76.91°E, dip 0.6°S) and a low latitude station, Waltair (17.7°N,83.3°E, dip 20°N) has been studied for a total of 18 strong geomagnetic storms with DST ? −100 nT. The simultaneous measurements of GPS-TEC and NmF₂ over Trivandrum and Waltair during the period 2000–2005 have been considered for the present study. It is found that there is a substantial increase in NmF₂ and TEC before the onset of the storm over Waltair, while the increase is not present at Trivandrum. The origin of pre-storm enhancements in electron density still remains unresolved owing to several conditions in their potential sources and occurrence mechanisms. In the present study an attempt is made to identify the possible mechanisms responsible for such enhancements in electron density of the F-region.