Estimation Of Ionospheric Parameters Research Articles

Application of Classical Kalman filtering technique in assimilation of multiple data types to NeQuick model

This study attempts to improve the estimation of ionospheric electron density profiles over Korea and adjacent areas by employing the classical Kalman filtering technique to assimilate Total Electron Content (TEC) data from various sources into the NeQuick model. Successive corrections method was applied to spread the effect of TEC data assimilation at a given location to others that lacked TEC observations. In order to reveal that the assimilation results emulate the complex ionospheric changes during geomagnetic storms, the selected study days included both quiet (Kp ≤ 3) and disturbed geomagnetic conditions in the year 2015. The results showed that assimilation of TEC data derived from ground-based Global Positioning System (GPS) receivers could improve the root mean squared error (RMSE) associated with the NeQuick model estimation of ionospheric parameters by ≥56%. The improvement of RMSE achieved by assimilating TEC data measured using ionosondes was ~50%. The assimilation of TEC observations made by the COSMIC radio occultation technique yielded results that depicted RMSE improvement of >10%. The assimilation of TEC data measured by GPS receiver onboard Low Earth Orbiting satellites yielded results that revealed deterioration of RMSE. This outcome might be due to either the fact that the receivers are on moving platforms, and these dynamics might not have been accounted for during TEC computation or the limitation of the assimilation process. Validation of our assimilation results with global ionosphere TEC data maps as processed at the Center for Orbit Determination in Europe (CODE) revealed that both depicted similar TEC changes, showing response to a geomagnetic storm.

Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

AbstractThis paper presents a study on the possibility of predicting the regional ionosphere at midlatitude by assimilating the predicted ionospheric parameters from a neural network (NN) model into the Sami2 is Another Model of the Ionosphere (SAMI2). The NN model was constructed from the data set of Jeju ionosonde (33.43°N, 126.30°E) for the period of 1 January 2011 to 31 December 2015 by using the long‐short term memory (LSTM) algorithm. The NN model provides 24‐hr prediction of the peak density (NmF2) and peak height (hmF2) of the F2 layer over Jeju. The predicted NmF2 and hmF2 were used to compute two ionospheric drivers (total ion density and effective neutral meridional wind), which were assimilated into the SAMI2 model. The SAMI2‐LSTM model estimates the ionospheric conditions over the midlatitude region around Jeju on the same geomagnetic meridional plane. We evaluate the performance of the SAMI2‐LSTM by comparing predicted NmF2 and hmF2 values with measured values during the geomagnetic quiet and storm periods. The root‐mean‐square error values of NmF2 (hmF2) from Jeju ionosonde measurements are lower by 45% and 45% (30% and 11%) than those of the SAMI2 and IRI‐2016 models during the geomagnetic quiet periods. However, during the geomagnetic storm periods, the performance of the SAMI2‐LSTM model does not predict positive geomagnetic storms well. Comparing the quiet and storm periods for the SAMI2‐LSTM model, the root‐mean‐square error (RMSE) of the storm period was calculated to be 2.76 (3.2) times higher at Jeju (Icheon) than in the quiet period. From these results, we demonstrated that in this study, the combination of the NN‐LSTM model and physics‐based model could improve the ionosphere prediction of existing theoretical and empirical models for midlatitude regions, at least in geomagnetically quiet conditions. We strongly suggest that this attempt, which has not been reported before, could be used as one of the keys to advance the physics‐based model further.

Global Navigation Satellite Systems for Ionospheric Error Correction in Radio-Engineering Systems: Challenges and Prospects

Global navigation satellite systems (GNSS), such as GPS, GLONASS, Galileo, and Bei-Dou/Compass are widely used for solving a variety of research and applied problems. This work considers the use of GNSS signals for monitoring of the ionosphere to provide an ionospheric error correction in radio-engineering systems in quasireal time. The problems arising in this case, among which the absolute ionospheric parameter estimation is the key problem, are discussed.

A modified carrier-to-code leveling method for retrieving ionospheric observables and detecting short-term temporal variability of receiver differential code biases

Sensing the ionosphere with the global positioning system involves two sequential tasks, namely the ionospheric observable retrieval and the ionospheric parameter estimation. A prominent source of error has long been identified as short-term variability in receiver differential code bias (rDCB). We modify the carrier-to-code leveling (CCL), a method commonly used to accomplish the first task, through assuming rDCB to be unlinked in time. Aside from the ionospheric observables, which are affected by, among others, the rDCB at one reference epoch, the Modified CCL (MCCL) can also provide the rDCB offsets with respect to the reference epoch as by-products. Two consequences arise. First, MCCL is capable of excluding the effects of time-varying rDCB from the ionospheric observables, which, in turn, improves the quality of ionospheric parameters of interest. Second, MCCL has significant potential as a means to detect between-epoch fluctuations experienced by rDCB of a single receiver.

Combination of different space-geodetic observations for regional ionosphere modeling

Most of the space-geodetic observation techniques can be used for modeling the distribution of free electrons in the Earth’s ionosphere. By combining different techniques one can take advantage of their different spatial and temporal distributions as well as their different observation characteristics and sensitivities concerning ionospheric parameter estimation. The present publication introduces a procedure for multi-dimensional ionospheric modeling. The model consists of a given reference part and an unknown correction part expanded in terms of B-spline functions. This approach is used to compute regional models of Vertical Total Electron Content (VTEC) based on the International Reference Ionosphere (IRI 2007) and GPS observations from terrestrial Global Navigation Satellite System (GNSS) reference stations, radio occultation data from Low Earth Orbiters (LEOs), dual-frequency radar altimetry measurements, and data obtained by Very Long Baseline Interferometry (VLBI). The approach overcomes deficiencies in the climatological IRI model and reaches the same level of accuracy than GNSS-based VTEC maps from IGS. In areas without GNSS observations (e.g., over the oceans) radio occultations and altimetry provide valuable measurements and further improve the VTEC maps. Moreover, the approach supplies information on the offsets between different observation techniques as well as on their different sensitivity for ionosphere modeling. Altogether, the present procedure helps to derive improved ionospheric corrections (e.g., for one-frequency radar altimeters) and at the same time it improves our knowledge on the Earth’s ionosphere.

An efficient near‐optimal approach to incoherent scatter radar parameter estimation

We present a computationally efficient, near‐optimal approach to the estimation of ionospheric parameters from incoherent scatter radar measurements. The method consists of removing the range smearing of ionospheric autocorrelation function via a set of 1‐D deconvolutions and performing nonlinear least squares fitting on the deconvolved autocorrelation functions. To stabilize the solution in the presence of noise, we incorporate regularization techniques. The computational cost is reduced significantly by estimating the ionospheric parameters at individual altitudes, in comparison to full‐profile‐type analysis, which attempts to estimate ionospheric parameters at all altitudes simultaneously. The performance of the new technique is evaluated in a numerical example and is shown to give estimates of almost equal quality as the full‐profile technique but at a 95% reduction in computation.