Using sparse regularization for multi-resolution tomography of the ionosphere

Global ionosphere models may not be accurate enough in regions where there are insufficient observations. Therefore, there is strong interest in regional ionosphere modeling using observations from dual-frequency Global Navigation Satellite System (GNSS) receivers in such regions. This paper presents a data assimilation method for hourly total electron content (TEC) modeling along with estimation of the receivers’ differential code biases (DCBs). For this purpose, first, initial values for slant TEC (STEC) and the receivers’ DCBs are estimated utilizing global ionosphere maps (GIMs). Second, STEC is expanded to a linear combination of spherical radial basis functions with time-dependent coefficients to reduce the number of unknowns, and a constrained least-squares adjustment is applied to resolve the corrected STEC and DCB values in the study area using observations from dual-frequency GNSS receivers. Third, the resolved STEC at ionospheric pierce points (IPPs) are converted to vertical TEC (VTEC) using a mapping function and assimilated into the background provided by spatial and temporal interpolation of GIM. Retrieved VTECs are weighted according to the satellites’ elevation angles, and an objective function is defined to determine the background’s covariance matrix. In this study, the Iranian permanent Global Positioning System (GPS) network is used to construct the regional hourly VTEC model and calculate hourly DCB variations. The proposed method is validated in two ways. First, some of the VTEC values are excluded from the dataset for use as test data and compared with modeling results. The root mean square (RMS) of the constructed regional assimilative model on test data is significantly better than GIM. Second, the RINEX file of Tehran station, whose observations did not play a role in modeling, is corrected using the regional model. The single-frequency positioning results with corrected observations show much better accuracy than GIMs.

Ionospheric total electron content (TEC) measurements, obtained simultaneously at several locations, can be processed using computerized tomography (CT) algorithms to obtain two‐dimensional images of ionospheric electron density. Using TEC data, computerized ionospheric tomography (CIT) reconstructs an image of the electron density structures in a vertical slice above the receiving stations. We successfully applied this technique to realistic simulations of ionospheric density variations over 16° of latitude and a height range of 50 to 1000 km. A method for approximating the peak height and scale height of the electron density profile will be discussed as well as a reconstruction technique based on the multiplicative algebraic reconstruction technique algorithm and a back projection based initial guess. The quality of reconstructions is considered for two geometries and image resolutions. In particular, the image of a mid‐latitude trough with background horizontal density gradient and large‐scale irregular structures has been reconstructed from TEC data generated from a model based on an incoherent scatter radar observation. The CT reconstructed image was compared with the original contour map obtained by the incoherent scatter radar. Good agreement has been achieved. The CIT technique has also been applied to a modeled ionosphere to calculate the range rate corrections for a Doppler‐tracking radar.

A fast algorithm for automatic detection of ionospheric disturbances: DROT

High-accuracy Total Electron Content (TEC) measurements are important for several applications, including GNSS time transfer with accuracies better than 10 ns. In addition, it is important to provide uncertainty estimates to algorithms that use the TEC measurements. Dual-frequency TEC errors are dominated by satellite inter-frequency biases, satellite inter-code biases, receiver inter-frequency biases, receiver noise and multipath, higher-order ionospheric errors, and receiver inter-code biases for certain receivers. To evaluate different algorithms and error mitigation methods, data were collected from four different GPS receivers in two locations at the Arecibo Observatory in Puerto Rico. Error mitigation methods include receiver hardware bias calibration, comparison with TEC maps provided by the Jet Propulsion Laboratory (JPL), receiver comparisons, TEC calibration during low TEC count as determined by the Arecibo radar, and multipath mitigation using the Code Noise Multi-Path (CNMP) algorithm [12]. The CNMP algorithm enables the definition of rigorous multipath error bounds on the TEC bias estimation.

The paper reports first simultaneous observations of artificial emission of 630‐nm oxygen red line, along with perturbations of slant total electron content (STEC). They were induced by the high‐frequency (HF) ionospheric heating produced by the SURA facility situated near Nijniy Novgorod, Russia. The HF heating affects differently the optical emission and STEC. While the patches of the artificial airglow are close to the area of the reduced STEC, the STEC increases at the periphery of the patches. Thus, the artificial airglow occurs mostly inside the large plasma cavities.

We present two case studies of large‐scale traveling ionospheric disturbances (LSTIDs) triggered by the 23–24 April 2023 and the 10–12 May 2024 geomagnetic storms. The LSTIDs are observed in topside ionospheric total electron content (TEC) measurements obtained from precise orbit determination (POD) receivers onboard LEO satellites. The POD TEC data used in this work are from Spire Global, PlanetiQ, Meteorological Operational satellite‐C (MetOp‐C), TerraSAR‐X/TanDEM‐X (TSX/TDX), and COSMIC‐2. The POD TEC captures the “frozen‐in‐time” wave structures of LSTIDs in the topside ionosphere (>550 km altitude) due to its GNSS rays rapid scan velocity. During the April 2023 geomagnetic storm, we observed LSTIDs with a magnitude of ∼20 TEC unit in slant TEC (sTEC) over Antarctica and a LSTIDs with an apparent wavelength of 1,600 km over North America. For the May 2024 storm, the observed maximum sTEC disturbances were ∼35 TECu over the ocean near Antarctica and a LSTIDs with an apparent wavelength of ∼1,800 km over North America. To convert the sTEC to vertical TEC (vTEC), we applied Foelsche and Kirchengast mapping function with a centroid ionosphere effective height (IEH) to signals with zenith angle ≤50° and the Thin Layer Model mapping function with integral medium value IEH method for the signal with zenith angle >50°. The POD vTEC are detrended and are compared with co‐located ground‐based GNSS TEC from Madrigal database. The results indicate that the POD TEC contributes a 11%–40% of the TEC disturbance magnitude of the LSTIDs observed from ground receivers.

Combination of swarm, Jason-3, and GNSS observations to construct a new modeling of global ionospheric maps

A new postprocessing technique has been developed to detect and characterize equatorial plasma bubbles (EPBs) from slant total electron content values calculated from GPS observations. The technique identifies an EPB when a slant total electron content depletion is detected. This technique is based on the analysis of the slope and the population variance variations of the slant total electron content values. The Ionospheric Bubble Seeker, based on the Java programming language, has been developed to implement this technique, allowing its use in all operating systems and on the Internet. Several thousands of slant total electron content profiles from different stations around the equatorial region have been analyzed to configure it. Different stations distributed worldwide have been analyzed to evaluate the robustness of the technique. The results reveal a clear daily dependence, showing the influence of the seasonal, longitudinal, and solar activity conditions. These results are compared with previous studies accomplished from different techniques and measurements and show clear agreements in the results.

Wavelet modelling in support of IRI

As an important component of the Earth's near-Earth space environment, the ionosphere is subject to irregular perturbations that may occur with natural phenomena such as earthquakes, typhoons, and thunderstorms, and have important effects on space safety, radio communications, and navigation and positioning. The rapid development of the Global Navigation Satellite System (GNSS) provides a good opportunity for the study of GNSS space environment science. In general, the Slant Total Electron Content (STEC) between receivers and satellites can be estimated from phase and pseudorange observations for the study of ionospheric characteristics. In this paper, we use GPS data from the dense Continuously Operating Reference Stations (CORS) in North America to study traveling ionospheric disturbances (TID). First, the oblique ionospheric total electron content of the North American stations is estimated by combining GNSS Precise Point Positioning (PPP) techniques, excluding satellite altitude angle <50° data. Then, Singular Spectrum Analysis (SSA) is adopted to remove the perturbation term of the STEC sequence to obtain the detrended STEC (DSTEC). Finally, the characteristics (e.g., occurrence location, velocity, horizontal direction, etc.) of the medium-scale traveling ionospheric disturbances (MSTIDs) are estimated based on a network of GNSS stations in the region close to each other (tens of kilometers). Using the U.S. CORS data on July 20, 2006, the DSTEC map of the two-dimensional region was obtained, and the detected MSTIDs phenomenon (wavelength ~300 km) propagated toward the equator with a velocity of about 200 m/s. This paper demonstrates the effectiveness of PPP/SSA-based extraction of ionospheric TIDs, which can be further applied to TID extraction and monitoring in China.

Ionosphere is an atmospheric layer that has a high electron concentration and anisotropic, dispersive, inhomogeneous nature. Total Electron Content (TEC) is one of the most important parameters that provides necessary information to observe the ionospheric variability. In this study, spatial-temporal variability of the ionosphere is investigated using Symmetric Kullback Leibler Distance (SKLD), Metric Distance (L2N) and Cross Correlation Coefficient (CC) on Slant Total Electron Content (STEC) data provided by a midlatitude Global Positioning System (GPS) network. STEC data that are used are obtained by using IONOLAB-STEC method on Turkish National Permanent GPS Network Active (TNPGN-Active) stations during 2011 for quiet and disturbed days of ionosphere.

The computerized ionospheric tomography is a method for imaging the Earth’s ionosphere using a sounding technique and computing the slant total electron content (STEC) values from data of the global positioning system (GPS). The most common approach for ionospheric tomography is the voxel-based model, in which (1) the ionosphere is divided into voxels, (2) the STEC is then measured along (many) satellite signal paths, and finally (3) an inversion procedure is applied to reconstruct the electron density distribution of the ionosphere. In this study, a computationally efficient approach is introduced, which improves the inversion procedure of step 3. Our proposed method combines the empirical orthogonal function and the spherical Slepian base functions to describe the vertical and horizontal distribution of electron density, respectively. Thus, it can be applied on regional and global case studies. Numerical application is demonstrated using the ground-based GPS data over South America. Our results are validated against ionospheric tomography obtained from the constellation observing system for meteorology, ionosphere, and climate (COSMIC) observations and the global ionosphere map estimated by international centers, as well as by comparison with STEC derived from independent GPS stations. Using the proposed approach, we find that while using 30 GPS measurements in South America, one can achieve comparable accuracy with those from COSMIC data within the reported accuracy (1 × 1011 el/cm3) of the product. Comparisons with real observations of two GPS stations indicate an absolute difference is less than 2 TECU (where 1 total electron content unit, TECU, is 1016 electrons/m2).

Abstract. The GPS data provides an effective way to estimate the total electron content (TEC) from the differential time delay of L1 and L2 transmissions from the GPS. The spacing of the constellation of GPS satellites in orbits are such that a minimum of four GPS satellites are observed at any given point in time from any location on the ground. Since these satellites are in different parts of the sky and the electron content in the ionosphere varies both spatially and temporally, the ionospheric pierce point (IPP) altitude or the assumed altitude of the centroid of mass of the ionosphere plays an important role in converting the vertical TEC from the measured slant TEC and vice versa. In this paper efforts are made to examine the validity of the IPP altitude of 350 km in the Indian zone comprising of the ever-changing and dynamic ionosphere from the equator to the ionization anomaly crest region and beyond, using the simultaneous ionosonde data from four different locations in India. From this data it is found that the peak electron density height (hpF2) varies from about 275 to 575 km at the equatorial region, and varies marginally from 300 to 350 km at and beyond the anomaly crest regions. Determination of the effective altitude of the IPP employing the inverse method suggested by Birch et al. (2002) did not yield any consistent altitude in particular for low elevation angles, but varied from a few hundred to one thousand kilometers and beyond in the Indian region. However, the vertical TEC computed from the measured GPS slant TEC for different IPP altitudes ranging from 250 to 750 km in the Indian region has revealed that the TEC does not change significantly with the IPP altitude, as long as the elevation angle of the satellite is greater than 50 degrees. However, in the case of satellites with lower elevation angles (&lt;50°), there is a significant departure in the TEC computed using different IPP altitudes from both methods. Therefore, the IPP altitude of 350 km may be taken as valid even in the Indian sector but only in the cases of satellite passes with elevation angles greater than 50°.

Multi-dimensional distribution of near-field ionospheric disturbances produced by the 2015 Mw7.8 Nepal earthquake

Clinical trial data can be presented in ways that exaggerate treatment effectiveness. Physicians consider therapy more effective, and may be more likely to make inappropriate practice changes, when data are presented in relative terms such as relative risk reduction rather than in absolute terms such as absolute risk reduction and number needed to treat. Our purpose was to determine (1) how frequently continuing medical education (CME) speakers present research data in relative terms compared to absolute terms; (2) how knowledgeable CME speakers and learners are about these terms; and (3) how CME learners want these terms presented. Analysis of videotapes and PowerPoint slides of 26 CME presentations, questionnaire survey of CME speakers and learners, and focus groups with learners. Speakers presented data more frequently in relative than absolute terms, but most frequently in general terms such as frequencies, percentages, graphs, and P-values with no data. Of 1367 PowerPoint slides, 269 presented research data, and of these, 225 (84%) presented data in general terms, 50 (19%) in relative terms and 19 (7%) in absolute terms. CME speakers understood relative and absolute terms better than learners. Approximately 25-35% of speakers and 45-65% of learners could not correctly calculate relative risk reduction, absolute risk reduction, and number needed to treat. Learners wished to have these terms presented in CME programs in a consistent and easily understood format and requested a brief review of them at the beginning of CME programs. Presentation of research data in most CME programs is inadequate to allow learners to make fully informed therapeutic decisions. Speakers and learners need professional development to improve their presentation and understanding of research data.