Comparison of observed ionospheric vertical TEC over the sea in Indian region with IRI-2016 model

The vertical ionospheric TEC values obtained from GAGAN grid based ionospheric delay correction values over the sea in the Indian equatorial region have been compared with the corresponding values derived from the International Reference Ionosphere model, IRI-2016. The objective of this work is to study the deviation of the vertical TEC derived from the IRI model from ground truths over the sea for different conditions. This will serve the basic intention of assessing the candidature of the IRI model as an alternative ionospheric correction model in navigation receivers in terms of accuracy. We have chosen different solar activity periods, seasons, geomagnetic conditions, locations etc. for our comparison and analysis. The TEC values by the IRI-2016 were compared with the actual measured values for the given conditions and errors were obtained. The measured vertical TEC values at the ionospheric grid points were derived from the GAGAN broadcast ionospheric delay data and used as reference. The IRI model with standard internal functions was used in estimating the TEC at the same ionospheric grid points. The errors in the model derived values are statistically analysed. Broadly, the results show that, for the Indian sector over the sea, the IRI model performs better on quiet days in off equatorial regions, particularly in the northern region. The overall performance degrades for other conditions with the model generally underestimating the true TEC values and most severely in the equatorial region. The performance is worst in this region for the disturbed days of the equinoctial period. The comparison study is also done with the TEC data measured directly by dual frequency GPS receivers. The results were found to be in general agreement with those obtained by comparing the model with GAGAN broadcast data as reference. This study will be useful in considering the IRI-2016 model for real time estimates of TEC as an alternative to the current parametric model in a satellite navigation receiver in absence of other options.     

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM,IRI2016 and Kriging

In this paper, a new method of temporal extrapolation of the ionosphere total electron content (TEC) is proposed. Using 3-layer wavelet neural networks (WNNs) and particle swarm optimization (PSO) training algorithm, TEC time series are modeled. The TEC temporal variations for next times are extrapolated with the help of training model. To evaluate the proposed model, observations of Tehran GNSS station (35.69°N, 51.33°E) from 2007 to 2018 are used. The efficiency of the proposed model has been evaluated in both low and high solar activity periods. All observations of the 2015 and 2018 have been removed from the training step to test the proposed model. On the other hand, observations of these 2 years are not used in network training. According to the F10.7, the 2015 has high solar activity and the 2018 has quiet conditions. The results of the proposed model are compared with the global ionosphere maps (GIMs) as a traditional ionosphere model, international reference ionosphere 2016 (IRI2016), Kriging and artificial neural network (ANN) models. The root mean square error (RMSE), bias, dVTEC = |VTEC_GPS ? VTEC_Model| and correlation coefficient are used to assess the accuracy of the proposed method. Also, for more accurate evaluation, a single-frequency precise point positioning (PPP) approach is used. According to the results of 2015, the maximum values of the RMSE for the WNN, ANN, Kriging, GIM and IRI2016 models are 5.49, 6.02, 6.34, 6.19 and 13.60 TECU, respectively. Also, the maximum values of the RMSE at 2018 for the WNN, ANN, Kriging, GIM and IRI2016 models are 2.47, 2.49, 2.50, 4.36 and 6.01 TECU, respectively. Comparing the results of the bias and correlation coefficient shows the higher accuracy of the proposed model in quiet and severe solar activity periods. The PPP analysis with the WNN model also shows an improvement of 1 to 12 mm in coordinate components. The results of the analyzes of this paper show that the WNN is a reliable, accurate and fast model for predicting the behavior of the ionosphere in different solar conditions.     

A topside investigation over a mid-latitude digisonde station in Cyprus

We examine the systematic differences between topside electron density measurements recorded by different techniques over the low-middle latitude operating European station in Nicosia, Cyprus (geographical coordinates: 35.14^oN, 33.2^oE), (magnetic coordinates 31.86^oN, 111.83 ^oE). These techniques include space-based in-situ data by Langmuir probes on board.European Space Agency (ESA) Swarm satellites, radio occultation measurements on board low Earth orbit (LEO) satellites from the COSMIC/FORMOSAT-3 mission and ground-based extrapolated topside electron density profiles from manually scaled ionograms. The measurements are also compared with International Reference Ionosphere Model (IRI-2016) topside estimations and IRI-corrected NeQuick topside formulation (method proposed by Pezzopane and Pignalberi (2019)). The comparison of Swarm and COSMIC observations with digisonde and IRI estimations verifies that in the majority of cases digisonde underestimates while IRI overestimates Swarm observations but in general, IRI provides a better topside representation than the digisonde. For COSMIC and digisonde profiles matched at the F layer peak the digisonde systematically underestimates topside COSMIC electron density values and the relative difference between COSMIC and digisonde increases with altitude (above hmF2), while IRI overestimates the topside COSMIC electron density but after a certain altitude (~150 km above hmF2) this overestimation starts to decrease with altitude. The IRI-corrected NeQuick underestimates the majority of topside COSMIC electron density profiles and relative difference is lower up to approximately 100 km (above the hmF2) and then it increases. The overall performance of IRI-corrected NeQuick improves with respect to IRI and digisonde.     

近地小行星2016HO3表面温度建模研究

2019年4月18日,中国国家航天局(CNSA)公布了小行星探测计划,将近地小行星2016HO3作为探测任务目标之一。主要梳理了2016HO3热环境分析的要素,通过调研国际上目前观测数据,得到2016HO3的初步环境参数,使用近地小行星热模型(NEATM)与小行星热物理模型(TPM)开展了小行星2016HO3表面温度场建模与分析,综合得出小行星温度上限为412 K;同时结合其可能的自转条件,仿真分析了不同位置的昼夜温差变化特性,发现2016HO3最大温差大约为30 K。由于两个模型均不能直接处理极夜情况,在TPM模型基础上采用对自转周期光照进行平均思路,给出了极夜条件下的温度分析方法,并获得小行星2016HO3的温度下限。     

A comparison of ionosonde measured foF2 and IRI-2016 predictions over China

A comparison of the ionospheric F-region critical frequency (foF2) between ionosonde measurements and IRI-2016 predictions is studied over China during the period from January 2008 to October 2016. Four stations are selected, and the latitude coverage starts at 49.4°N and ends at 23.2°N with a sequential latitude interval of about 10°, the corresponding geomagnetic latitudes are from 39.5°N to 13.2°N. The results show that the variability of the observed foF2 versus latitudes, seasons, local time and levels of solar activity could be well reproduced by IRI-2016. However, the daily lowest value of foF2 from the IRI-2016 prediction occurs earlier than that from the ionosonde. Around the sunrise, the IRI-2016 prediction shows a very sharp rise and grows much faster than the observed foF2 in every month. The foF2 difference between the two options (URSI and CCIR) in IRI-2016 increases as the F10.7 index decreases. During 2008–2009, the annual average deviations of URSI and CCIR range from ?5% to ?10% and from 5% to ?5%, respectively. Generally, the CCIR performs better than URSI during postsunset under low solar activity or in Equatorial Ionization Anomaly (EIA) region over China, while it shows no large difference in performance with URSI in other locations or for other time.     

Study the behavior of the ionospheric frequencies at Karachi during three consecutive solar minima of cycle 21, 22 & 23 and their comparison with IRI-2016

The behavior of critical frequencies of ionospheric E and F2 layers (foE & foF2) along with minimum ionospheric frequency (fmin) is studied for solar minima of cycle 21 (1986), 22 (1996) and 23 (2008) over Karachi (24.95$°$N, 67.13$°$E), Pakistan. The station is located at the crest of equatorial ionization anomaly region. Beside seasonal differences, pronounced change in the values of frequencies is noted from one solar minimum to another solar minimum. A strong and direct correlation of foF2 with Smoothed Sunspot Number (SSN) and F10.7?cm solar flux is observed. In the minimum of cycle 23, reduction in foF2 is noted due to reduction of solar EUV as compared to other minima. Also disappearance of semi-annual variations in foF2 is noted in cycle 23 minimum. Unexpectedly higher values of foE and fmin are observed in minimum of cycle 23 as compared to other minima. It is difficult to explain this unusual behavior of fmin and foE along with disappearance of semi-annual variation in foF2. It is possible that during very low solar activity, thermospheric conditions are changed which in turn altered the ionosphere. Further investigation of atmosphere-ionosphere coupling is required to understand this complex behavior. On comparison of observed values with IRI-2016, higher deviations are observed in foE before noon hours while in case of foF2, large deviations are noted during daytime. The absence of foF2 semi-annual variation in cycle 23 is not reproduced by IRI-2016. It is suggested that IRI-2016 need some modification for extremely low solar activity condition.     

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.     

Improving the modeling of bottomside thickness parameters over midlatitudes and high latitudes

This paper investigates bottomside thickness parameters at Digisonde stations over midlatitude and high latitude regions, and compares the diurnal, seasonal, and solar activity variations in 2014 and 2009. The geographic latitudes of high latitude considered in this work are located beyond ±60° and those of midlatitude are located between ±40° and ±60°. The IRI-modeled B0 with ABT-2009 option (B0_IRI) are also examined and compared with four kinds of the B0 values, i.e., the observed B0 (B0_obs) from GIRO, the computed B0 following to Jamjareegulgarn et al. (2017a) (B0_old), the calculated B0 with a correction factor regarding to Jamjareegulgarn et al. (2017b) (B0_new), and the B0 with an average correction factor (B0_new_c_av). The average correction factors are proposed additionally in this work so as to assist occasionally the experimental B0 nonexistence of Digisonde which are equal to 0.2658 and 0.2058 for midlatitudes and high latitudes, respectively. Results show that the diurnal variations of B0_new and B0_new_c_av are in a good agreement with those of B0_obs evidently compared with those of B0_IRI and B0_old at every station during the three seasons over high and middle latitudes. During the three seasons, the diurnal variations of B0_new_c_av show similar trends and are close to one another with the B0_obs and the B0_new with small deviations. The differences between the B0obs and the B0_new_c_av also show similar trends and are close to one another with those between the B0obs and the B0_new. In contrast, the B0_IRI with ABT-2009 option seems to predict the B0 values poorly during the three seasons at high latitudes and some seasons at midlatitudes. The proposed B0_new is useful for computing approximately the observed B0 and the ionogram-based total electron content (ITEC) of Digisonde, and the plasma scale height over midlatitudes and high latitudes.