Empirical forecast of quiet time ionospheric Total Electron Content maps over Europe

Badeke, Ronny; Borries, Claudia; Hoque, Mohammed Mainul; Minkwitz, David

doi:10.1016/j.asr.2018.04.010

Cited by 28 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section compares the results of methods such as SPM, Med, presented in (Badeke et al, 2018), with the results of the proposed method according to TEC for three stations in 2015. A brief description of the proposed method is as follows.…”

Section: Comparison Of Short-term Tec Results For 2015mentioning

confidence: 99%

“…Example of forecast for the day following the 10th 27-day period A comparison of the forecast accuracy for the three stations is given in Table 2 for the SPM method, for using the median (Med), and for the proposed method (Pred). These results should be compared with data of (Badeke et al, 2018), which can be presented as Table 3. It can be seen that in both tables the SPM model gives the best result, while the climatological model gives the worst result.…”

Section: Comparison Of Short-term Tec Results For 2015mentioning

confidence: 99%

“…Moreover, these deviations are smaller than those provided by the IRI-Plas and NeQuick models, shown in the Table 1. It should be noted that in contrast to (Badeke et al, 2018), quiet days were not selected. This allows us to reveal the dependence of the predicted values on the index Dst, which is chosen to characterize the disturbed conditions.…”

Section: Comparison Of Short-term Tec Results For 2015mentioning

confidence: 99%

“…No less important role is played by short-term forecasting methods. For a short-term forecast, are compared the Standard Persistence Model (SPM), a 27 day median model, considered in (Badeke et al, 2018), and the proposed method for 1 day in advance, using data from 2015. (Okoh et al, 2018), in which at low latitudes the best results were obtained for the NeQuick model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of TEC Prediction Methods for High Latitudes with GIM Maps

Glebova

Maltseva

2019

2019 Radiation and Scattering of Electromagnetic Waves (RSEMW)

View full text Add to dashboard Cite

For the high-latitude part of the low-latitude region, the errors in determining the TEC from the data of Rostov and Ankara stations in 2015 were estimated. The estimates obtained for the IRI-Plas model turned out to be better than for the NeQuick model, with an average absolute error in the range of 3-3.6 TECU, a relative RMS error in the range of 21-27% which is acceptable for use in positioning systems. For a short-term forecast, the best results were obtained by the Standard Persistence Model with an average absolute error in the range of 1.95-2.5 TECU, a relative RMS error in the range of 17-21%. For the proposed method, these estimates were 2.04-2.5 TECU and 12-14%.

show abstract

Section: Comparison Of Short-term Tec Results For 2015mentioning

confidence: 99%

Section: Comparison Of Short-term Tec Results For 2015mentioning

confidence: 99%

Section: Comparison Of Short-term Tec Results For 2015mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of TEC Prediction Methods for High Latitudes with GIM Maps

Glebova

Maltseva

2019

2019 Radiation and Scattering of Electromagnetic Waves (RSEMW)

View full text Add to dashboard Cite

show abstract

“…The forecasted map results as a sum of the actual vTEC map and a weighted sum of the temporal vTEC gradient of the previous hour and the temporal gradient of the previous day at the same time. The NCTM and other empirical models have been recently tested over Europe revealing the NCTM capability to extend the forecast up to 24 h in advance under quiet geospatial conditions (Badeke et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Neural network based model for global Total Electron Content forecasting

Cesaroni

Spogli

Aragón‐Àngel

et al. 2020

J. Space Weather Space Clim.

View full text Add to dashboard Cite

We introduce a novel empirical model to forecast, 24 h in advance, the Total Electron Content (TEC) at global scale. The technique leverages on the Global Ionospheric Map (GIM), provided by the International GNSS Service (IGS), and applies a nonlinear autoregressive neural network with external input (NARX) to selected GIM grid points for the 24 h single-point TEC forecasting, taking into account the actual and forecasted geomagnetic conditions. To extend the forecasting at a global scale, the technique makes use of the NeQuick2 Model fed by an effective sunspot number R12 (R12eff), estimated by minimizing the root mean square error (RMSE) between NARX output and NeQuick2 applied at the same GIM grid points. The novel approach is able to reproduce the features of the ionosphere especially during disturbed periods. The performance of the forecasting approach is extensively tested under different geospatial conditions, against both TEC maps products by UPC (Universitat Politècnica de Catalunya) and independent TEC data from Jason-3 spacecraft. The testing results are very satisfactory in terms of RMSE, as it has been found to range between 3 and 5 TECu. RMSE depend on the latitude sectors, time of the day, geomagnetic conditions, and provide a statistical estimation of the accuracy of the 24-h forecasting technique even over the oceans. The validation of the forecasting during five geomagnetic storms reveals that the model performance is not deteriorated during disturbed periods. This 24-h empirical approach is currently implemented on the Ionosphere Prediction Service (IPS), a prototype platform to support different classes of GNSS users.

show abstract

Influence of Advance Time on Accuracy of the Ionospheric Total Electron Content Forecast

Maltseva,

Kharakhashyan

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Empirical forecast of quiet time ionospheric Total Electron Content maps over Europe

Cited by 28 publications

References 31 publications

Comparison of TEC Prediction Methods for High Latitudes with GIM Maps

Comparison of TEC Prediction Methods for High Latitudes with GIM Maps

Neural network based model for global Total Electron Content forecasting

Influence of Advance Time on Accuracy of the Ionospheric Total Electron Content Forecast

Contact Info

Product

Resources

About