The dual frequency radio signals of the Global Positioning System (GPS) allow measurements of the total number of electrons, called total electron content (TEC), along a ray path from GPS satellite to receiver. We have developed a new technique to construct two-dimensional maps of absolute TEC over Japan by using GPS data from more than 1000 GPS receivers. A least squares fitting procedure is used to remove instrumental biases inherent in the GPS satellite and receiver. Two-dimensional maps of absolute vertical TEC are derived with time resolution of 30 seconds and spatial resolution of 0.15° × 0.15° in latitude and longitude. Our method is validated in two ways. First, TECs along ray paths from the GPS satellites are simulated using a model for electron contents based on the IRI-95 model. It is found that TEC from our method is underestimated by less than 3 TECU. Then, estimated vertical GPS TEC is compared with ionospheric TEC that is calculated from simultaneous electron density profile obtained with the MU radar. Diurnal and day-to-day variation of the GPS TEC follows the TEC behavior derived from MU radar observation but the GPS TEC is 2 TECU larger than the MU radar TEC on average. This difference can be attributed to the plasmaspheric electron content along the GPS ray path. This method is also applied to GPS data during a magnetic storm of September 25, 1998. An intense TEC enhancement, probably caused by a northward expansion of the equatorial anomaly, was observed in the southern part of Japan in the evening during the main phase of the storm.

A new technique for mapping of total electron content using GPS network in Japan

Random forest solar power forecast based on classification optimization

Prominent advances in the field of artificial intelligence during the past decade and the breakthrough of deep learning would be useful for investigating ionospheric weather using ground and space-based ionospheric sensors data. The significance of deep learning algorithms needs to be assessed in forecasting the low latitude ionospheric disturbances (delays) for the global positioning system (GPS) signals. Total electron content (TEC) data sets prepared by taking advantage of GPS satellite radio frequency (RF) signals. This letter provides the application of deep learning models, long short-term memory (LSTM), gated recurrent unit (GRU), and a hybrid model that consists of LSTM combined with convolution neural network (CNN) to forecast the ionospheric delays for GPS signals. The deep learning models implemented using the vertical TEC (VTEC) time-series data estimated from GPS measurements over Bengaluru, Guntur, and Lucknow GPS stations. The LSTM-CNN model performs well when compared to other ionospheric deep learning forecasting algorithms with minimum root-mean-square error (RMSE) of 1.5 TEC units (TECUs) and a high degree of $R^{2} = 0.99$ .

Implementation of Hybrid Deep Learning Model (LSTM-CNN) for Ionospheric TEC Forecasting Using GPS Data

Machine learning algorithm to forecast ionospheric time delays using Global Navigation satellite system observations

The total electron content (TEC) is one of the most important parameters for studying the behavior of the ionosphere. The global ionosphere maps (GIMs) can be used to study the TEC time series variations. The time resolution of the GIM-TEC is 2 h, whereas the frequency of the ionospheric temporal behavior can be less than 2 h. To solve this problem, we present a new method for ionosphere time series modeling and prediction in Iran. The adaptive neuro-fuzzy inference system (ANFIS) and principal component analysis are combined to model the TEC of the ionosphere. In fact, the observations are decomposed into principal components before entering to the ANFIS network and only a few main components are used for training the network. The main advantage of this combination is to increase the accuracy of the results and reduce the time of convergence to achieve an optimal solution. To evaluate the proposed method, we used observations of a Tehran GNSS station in 2016 and 2017. The root-mean-square error, correlation coefficient, and dVTEC = |VTECGPS − VTECmodel| were used to assess the accuracy of the proposed method. Also, all results are compared with the International Reference Ionosphere 2016 (IRI2016), GIM-TEC, and artificial neural networks (ANNs) ionosphere models. The results indicate a 1 to 4.72 TECU improvement in the temporal resolution of TEC modeling with the proposed method, compared to the IRI2016, GIM, and ANNs in the Iranian region.

Ionosphere time series modeling using adaptive neuro-fuzzy inference system and principal component analysis

Forecasting the ionospheric space weather is crucial for improving the accuracy of the global navigation satellite systems (GNSS). Nonetheless, comprehending the nonhomogeneous ionospheric variability under space earth environmental conditions is a major challenge, and so is developing an accurate ionospheric forecasting model. The complex spatial and temporal variations in the ionospheric region are the results of the solar and interplanetary activities, in addition to the magnetosphere, mesosphere, thermosphere, stratosphere, troposphere, and lithosphere processes. Thus, this calls for an urgent need to develop a suitable ionospheric forecasting algorithm to capture the ionospheric perturbations. Total electron content (TEC) is the key parameter derived from GNSS receivers to represent the status of the ionosphere. This paper introduces a novel ionospheric forecasting algorithm based on the fusion of principal component analysis and artificial neural networks (PCA–NN) methods to forecast the ionospheric TEC values. Solar index (F10.7), geomagnetic index (Ap index), and 20-year TEC data (1997–2016) over a Japan Grid Point (34.95 °N and 134.05 °E) were used to apply artificial intelligence methodologies. The experimental results underscore the reliability of the proposed algorithm in forecasting the ionospheric time delay effects.

Implementation of Hybrid Ionospheric TEC Forecasting Algorithm Using PCA-NN Method

The single-frequency users of Global Navigation Satellite System (GNSS) require an effective mathematical model that mitigate the dominant errors due to ionospheric delays. Klobuchar model approximately reduces ionospheric effect up to 50% through its coefficients in the navigation message, which is not sufficient for the GNSS single frequency users at critical applications. Hence, a new model, for Single Frequency GNSS User Applications using Klobuchar model driven by Auto Regressive Moving Average Method (SAKARMA) is proposed to forecast and enhance the precision of ionospheric delay estimations for GNSS users. The hourly VTEC maps are obtained from the Assimilated Indian Regional Vertical Total Electron Content (AIRAVAT) by the process of data assimilation using the Kalman filter exclusively for the Indian region (longitude: 65°E to 100°E; latitude: 5°N to 40°N) using 26 GPS TEC stations over Indian region. The accuracy of the SAKARMA model is investigated using the AIRAVAT maps for various Indian geographic regions during both geomagnetic quiet and disturbed conditions of September month in 2016 year. Furthermore, in order to test SAKARMA model, a dual-frequency Navigation with Indian Constellation (NavIC) receiver located at the KL Education Foundation, Guntur, India (geographic: 16.37°N, 80.37°E; geomagnetic: 7.44°N, 153.75°E) is used to collect the observations during 2 - 12 September 2017. Furthermore, SAKARMA model is also validated with Klobuchar model, Klobuchar-style coefficients provided by the Center for Orbit Determination in Europe (CODE) (CODKlob) Model, BeiDou System (BDS2) Model and NeQuick 2 Model over a low latitude NavIC station in forecasting the ionospheric delays. The experimental results of SAKARMA for NavIC have revealed that the MAPE for proposed SAKARMA model is 9-17% (accuracy: 83-91%), while 34-53% (accuracy: 47-66%) for the Klobuchar model. Thus, the results illustrate that the proposed SAKARMA model is capable of predicting the ionospheric delays for single frequency GNSS/NavIC users.

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A New Ionospheric Model for Single Frequency GNSS User Applications Using Klobuchar Model Driven by Auto Regressive Moving Average (SAKARMA) Method Over Indian Region

I. Lakshmi Mallika

Papers

Implementation of Hybrid Ionospheric TEC Forecasting Algorithm Using PCA-NN Method

A New Ionospheric Model for Single Frequency GNSS User Applications Using Klobuchar Model Driven by Auto Regressive Moving Average (SAKARMA) Method Over Indian Region