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Joule heating is one of the main energy inputs into the thermosphere‐ionosphere system. Precise modeling of this process is essential for any space weather application. Existing thermosphere‐ionosphere models tend to underestimate the actual Joule heating rate quite significantly. The Thermosphere‐Ionosphere‐Electrodynamics General‐Circulation‐Model applies an empirical scaling factor of 1.5 for compensation. We calculate vertical profiles of Joule heating rates from approximately 2,220 hr of measurements with the EISCAT incoherent scatter radar and the corresponding model runs. We investigate model runs with the plasma convection driven by both the Heelis and the Weimer model. The required scaling of the Joule heating profiles is determined with respect to the Kp index, the Kan‐Lee merging electric field EKL, and the magnetic local time. Though the default scaling factor of 1.5 appears to be adequate on average, we find that the required scaling varies strongly with all three parameters ranging from 0.46 to ∼20 at geomagnetically disturbed and quiet times, respectively. Furthermore, the required scaling is significantly different in runs driven by the Heelis and Weimer model. Adjusting the scaling factor with respect to the Kp index, EKL, the magnetic local time, and the choice of convection model would reduce the difference between Joule heating rates calculated from measurement and model plasma parameters. Plain Language Summary: The vast majority of the energy input to the Earth system originates from the sun. This includes the absorption of various types of radiation, for example, ultraviolet radiation in the ozone layer or visible light and infrared radiation at the surface. In the upper atmosphere above about 80 km altitude, the absorption of extreme ultraviolet radiation and soft X‐rays plays a major role. However, other processes also contribute significantly to the heating of this region, for example, the energy dissipation in the form of electric currents flowing along electric fields, also known as Joule heating. Joule heating is highly variable and can be drastically enhanced especially during solar storms, which can have potentially disastrous effects on satellites. Accurate modeling, and therefore also prediction, of Joule heating is not possible at the moment since thermosphere‐ionosphere models have to scale the Joule heating empirically to fit the actual values. We investigate how the required scaling changes under different geophysical conditions. Key Points: The constant Joule heating scaling factor in TIE‐GCM is inadequate to compensate for the underestimation under various geomagnetic conditionsJoule heating rates calculated from 2,220 hr of EISCAT measurements are compared to TIE‐GCM runs driven with the Heelis and Weimer convection modelsThe required scaling factor varies significantly with the Kp index, the Kan‐Lee merging electric field, and the magnetic local time [ABSTRACT FROM AUTHOR]

Photochemistry can significantly affect the ionospheric status. Adopting a comprehensive photochemical scheme with 60 reactions, primarily based on the recent systematic study of ion chemistry by Richards in 2011, we revised the open-source SAMI2 (Sami2 is another model of the ionosphere) model to SAMI2−ph. The scheme includes both ground state and metastable/vibrational excited compositions (e.g., N(2D), N2(ν), and O2(ν)) and associated reactions, which can remarkably affect the ionospheric electron density. The model accuracy is tested using the most widely used ionospheric data foF2 derived from mid-latitude ionosonde stations. The correlation coefficients are larger for SAMI2−ph than for SAMI2. In addition, the linear slope k is significantly closer to 1 than the default run for the NmF2 comparisons. The smaller RMSE and b indicate that the modified model provides a reasonably good match with the ionosonde NmF2 measurements. The above results demonstrate that the model with the chosen photochemical scheme performs better than the original SAMI2 at mid-latitude. [ABSTRACT FROM AUTHOR]

NeQuick2 is a three-dimensional ionospheric electron density empirical model that uses numerical integration to calculate the total electron content along any line-of-sight (LOS). As one of the most commonly used three-dimensional ionospheric models, it is necessary to objectively evaluate the accuracy and stability of NeQuick2 over a long period, especially over the mid-latitudes of the northern hemisphere where most of the ground-based GNSS stations are distributed. Therefore, different methods are used in this study to evaluate the accuracy of the NeQuick2 model from 2008 to 2021, including comparison with the International Global Navigation Satellite System Global Ionosphere Maps (IGSG), Jason2 Vertical Electron content (VTEC), and self-consistent evaluation. The comparison with IGSG shows that the standard deviation (STD) value is about 2.59 TECU. The accuracy of the IGSG and NeQuick2 model over ocean regions shows that the bias of IGSG is more significant than that of the NeQuick2 model. The mean STD value is 2.09 TECU for IGSG, and the corresponding value is 3.18 TECU for the NeQuick2 model, which is about 50% worse than IGSG. The dSTEC assessment results indicate that the variation in bias for IGSG is more stable than that of the NeQuick2 model. The mean STD value is 0.86 and 1.52 TECU for IGSG and NeQuick2 model, respectively. The conclusion could be made that NeQuick2 model represents the average ionosphere electron content and its accuracy fluctuates with solar conditions. Compared with the IGSG, the NeQuick2 model always underestimates TEC value, especially in low solar activity periods and compared with Jason2, the TEC values obtained by NeQuick2 model are overestimated, but the degree of overestimation is smaller than that of IGSG. [ABSTRACT FROM AUTHOR]

Global empirical models (GMF2 – the F2 layer Global Model) of the critical frequency, foF2, of the ionospheric F2-layer and the peak height, hmF2, are built up with the re-calibrated sunspot number time series, SSN2, for the quiet geomagnetic conditions (ap < 15 nT). The input parameters of the model are the geographic coordinates, time UT, day, month, year, and the sunspot number SSN2. Two options of solar proxy are presented by 12-month smoothed SSN2 12 and the 27-day integral index SSN2(τ) of solar activity (τ = 0.96) for a given day. The effect of the saturation of foF2 at high solar activity is taken into account by a combination of linear and logarithmic dependences of foF2 on SSN2. The GMF2 model is incorporated into the IRI-Plas system to provide 3-D electron density profile and total electron content TEC through the ionosphere and plasmasphere. GMF2 and IRI-Plas model predictions are compared with foF2, hmF2 and TEC observations and other models for low, medium and high solar activity demonstrating improved accuracy by 20–50% as compared to other modelling results. • Re-calibrated sunspot numbers SSN2 are used as driver of foF2/hmF2 ionosphere model. • First sets of foF2/hmF2 Spherical Harmonic coefficients in terms of SSN2 are produced. • GMF2 input parameters are geographic coordinates, time UT, day, month, year, SSN2. • IRI-Plas ionosphere-plasmasphere model driven by SSN2 incorporates GMF2 model. • GMF2 and IRI-Plas data-model comparisons show 20 to 50% improvements of results. [ABSTRACT FROM AUTHOR]

High frequency ionosphere vertical sounding, as an important and representative application for detecting the ionosphere and studying the characteristics of radio propagation, can be utilized to monitor the ionosphere continuously variation and to acquire the ionosphere asymmetrical features of diverse scale above the ionosphere vertical sounding stations. This is a first article on real time application of numerical methods to obtain the parameters of traveling ionosphere disturbances (TIDs) using vertical incident ionograms. In this paper, the distribution of ionosphere electron density with TIDs is constructed using a background ionosphere model superimposed a perturbation theory model. The background ionosphere electron density is modelled by the inversion of vertical incident ionograms which are observed before the appearance of the disturbance. Based on the fourth order Adams-Bashforth-Moulton (the so-called ABM) predictor corrector method, instead of Runge-Kutta method, the fast digital ray tracing method is established. According to process of the disturbed trace simulation and parameters inversion, the characteristic parameters of ionosphere disturbance at different detection time can be obtained in real time. The numerical analysis of TIDs is then captured completely. [ABSTRACT FROM AUTHOR]

An ionosphere layer makes inaccurate global navigation satellite systems (GNSS) signal. The ionosphere layer contains free electrons delaying the speed of GNSS signals. A suitable selection of ionosphere model is very useful to eliminate this inaccurate GNSS signal. This article presents a comparative study by applying various ionosphere models in Thailand including: Klobuchar model, Global Ionosphere Maps, Ionosphere Model from Quasi-Zenith Satellite System and local ionosphere model. The local ionosphere model is generated from GNSS observations at with the use of locally available GNSS tracking stations by the Bernese GPS software 5.0. Coordinates derived from the above-mentioned models were compared and statistical tests in two observed scenarios; namely, single-frequency data and ionosphere-free linear combination from dual-frequency data. These experiment results proved that applying the local ionosphere model to the estimation produces the most accurate positioning results. They are similar to the results obtained from an ionosphere-free linear combination, but with significant differences. [ABSTRACT FROM AUTHOR]

Abstract 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. [ABSTRACT FROM AUTHOR]

Zero-difference (ZD) ambiguity resolution (AR) reveals the potential to further improve the performance of precise point positioning (PPP). Traditionally, PPP AR is achieved by Melbourne-Wübbena and ionosphere-free combinations in which the ionosphere effect are removed. To exploit the ionosphere characteristics, PPP AR with L1 and L2 raw observable has also been developed recently. In this study, we apply this new approach in uncalibrated phase delay (UPD) generation and ZD AR and compare it with the traditional model. The raw observable processing strategy treats each ionosphere delay as an unknown parameter. In this manner, both a priori ionosphere correction model and its spatio-temporal correlation can be employed as constraints to improve the ambiguity resolution. However, theoretical analysis indicates that for the wide-lane (WL) UPD retrieved from L1/L2 ambiguities to benefit from this raw observable approach, high precision ionosphere correction of better than 0.7 total electron content unit (TECU) is essential. This conclusion is then confirmed with over 1 year data collected at about 360 stations. Firstly, both global and regional ionosphere model were generated and evaluated, the results of which demonstrated that, for large-scale ionosphere modeling, only an accuracy of 3.9 TECU can be achieved on average for the vertical delays, and this accuracy can be improved to about 0.64 TECU when dense network is involved. Based on these ionosphere products, WL/narrow-lane (NL) UPDs are then extracted with the raw observable model. The NL ambiguity reveals a better stability and consistency compared to traditional approach. Nonetheless, the WL ambiguity can be hardly improved even constrained with the high spatio-temporal resolution ionospheric corrections. By applying both these approaches in PPP-RTK, it is interesting to find that the traditional model is more efficient in AR as evidenced by the shorter time to first fix, while the three-dimensional positioning accuracy of the RAW model outperforms the combination model by about $$7.9\,\%$$ . This reveals that, with the current ionosphere models, there is actually no optimal strategy for the dual-frequency ZD ambiguity resolution, and the combination approach and raw approach each has merits and demerits. [ABSTRACT FROM AUTHOR]

Since 2010 a 2nd generation NOAA MF/HF radar, also referred to as the VIPIR ionosonde, has been operated at Hualien, Taiwan (23.8973°N, 121.5503°E). The Hualien VIPIR ionosonde is a modern ionospheric radar, fully digitizing complex signal records and using multiple parallel receiver channels for simultaneous signal measurements from multiple spaced receiving antennas. This paper considers radio direction finding based on interferometric phase measurements from a horizontal antenna array in the Hualien VIPIR ionosonde system. We applied the Hermite normal form method to solve the phasemeasurement aliasing and least squares problems and improve the radio angle-of-arrival (AOA) measurements. Backward ray-tracing simulation has been proposed to determine radio transmitter position. This paper presents a numerical, step by step ray-tracing method based on the IGRF superimposed onto a phenomenological ionospheric electron density model, the TaiWan Ionospheric Model (TWIM). The proposed methodology is successfully applied to locate two experimental HF radio transmitters at Longquan and Chungli with distance errors within 5 km and less than 5% of the great circle distances. [ABSTRACT FROM AUTHOR]

Abstract: Given the severe effects of the ionosphere on global navigation satellite system (GNSS) signals, single-frequency (SF) precise point positioning (PPP) users can only achieve decimeter-level positioning results. Ionosphere-free combinations can eliminate the majority of ionospheric delay, but increase observation noise and slow down dual-frequency (DF) PPP convergence. In this paper, we develop a regional ionosphere modeling and rapid convergence approach to improve SF PPP (SFPPP) accuracy and accelerate DF PPP (DFPPP) convergence speed. Instead of area model, ionospheric delay is modeled for each satellite to be used as a priori correction. With the ionospheric, wide-lane uncalibrated phase delay (UPD) and residuals satellite DCBs product, the wide-lane observations for DF users change to be high-precision pseudorange observations. The validation of a continuously operating reference station (CORS) network was analyzed. The experimental results confirm that the approach considerably improves the accuracy of SFPPP. For DF users, convergence time is substantially reduced. [Copyright &y& Elsevier]

Abstract: The purpose of this research work is to validate the ionospheric models (IRI and CHIU) to assess its suitability and usefulness as an operational tool. The ionospheric model is a computer model designed to predict the state of the global ionosphere for 24h. The scope was limited to conduct comparisons between the predicted F2 layer critical frequencies (f0F2) against observed ionosonde data. The ionospheric prediction model (IPM) was designed to predict by using monthly median sunspot number, while the observation data are taken from two digital ionospheric sounding stations (Okinawa, 26.28N, 127.8E and Wakkanai, 45.38N, 141.66E) which lies within the mid-latitude region of the globe. Analysis of the f0F2 data from stations for year (2001) with high solar activity and year (2004) with low solar activity, four months (March, June, September and December) chosen based primarily on data availability. From results it seen that the ratio between monthly median predicted and observed f0F2 values for each model used in this research work and for the chosen months was nonlinear with local time, so the empirical formula for applying correction factors were determined, these formula can be used to correct the error occurred in predicted f0F2 value. [Copyright &y& Elsevier]

The distribution of total electron content (TEC) in the ionosphere is irregular and complex, and it is hard to model accurately. The polynomial (POLY) model is used extensively for regional ionosphere modeling in two-dimensional space. However, in the active period of the ionosphere, the POLY model is difficult to reflect the distribution and variation of TEC. Aiming at the limitation of the regional POLY model, this paper proposes a new ionosphere modeling method with combining the support vector machine (SVM) regression model and the POLY model. Firstly, the POLY model is established using observations of regional continuously operating reference stations (CORS). Then the SVM regression model is trained to compensate the model error of POLY, and the TEC SVM-P model is obtained by the combination of the POLY and the SVM. The fitting accuracies of the models are verified with the root mean square errors (RMSEs) and static single-frequency precise point positioning (PPP) experiments. The results show that the RMSE of the SVM-P is 0.980 TECU (TEC unit), which produces an improvement of 17.3% compared with the POLY model (1.185 TECU). Using SVM-P models, the positioning accuracies of single-frequency PPP are improved over 40% compared with those using POLY models. The SVM-P is also compared with the back-propagation neural network combined with POLY (BPNN-P), and its performance is also better than BPNN-P (1.070 TECU). [ABSTRACT FROM AUTHOR]