An Adaptive Forecasting Method for Ionospheric Critical Frequency of F2 Layer

Abstract

To achieve further improvements in quantitative predictability, a chaos-based adaptive forecasting method for the critical frequency of the F2 layer (f o F 2 ) is proposed for the development of an ionospheric forecasting technique for one hour ahead. This method has three new characteristics. (1) It is based on Volterra filters and it has a simplified structure with easy implementation.(2) Based only on past measured data, it can forecast f o F 2 values without the requirement for past or forecast values of any solar and geomagnetic indices. (3) It can achieve high forecast accuracy with a small training dataset of 27 days (one solar rotation period). Diurnal, seasonal, and annual comparisons of measured and forecasted f o F 2 values are presented to illustrate the applicability and suitability of the proposed method. Statistical results reveal that the f o F 2 values calculated using the proposed model are consistent with the trend of measurements irrespective of whether geomagnetic conditions are quiet or disturbed. The average RMSE and RRMSE values were 0.86 MHz and 17.36%, respectively, when using measured data from periods of past 27 days during 2008-2015. The proposed method has potential to forecast other ionospheric characteristic parameters, and that it could achieve satisfactory regional or global 1-hr forecasting if combined with a spatial reconstruction technique. Plain Language SummaryThe critical frequency of F2 layer of ionosphere (f o F 2 ) is a very important characteristic parameter in various civil and military applications. To achieve further improvements in quantitative predictability, a chaos-based adaptive forecasting method for f o F 2 is proposed for the development of an ionospheric forecasting technique for 1 hr ahead. This method has three advantages: simplified structure with easy implementation, high forecast accuracy with a small training dataset, and forecast without the requirement for any solar and geomagnetic indices. Comparisons between the measured and forecasting values of f o F 2 have shown the usability and appropriateness of the proposed method. Statistical results reveal that the f o F 2 calculated by the proposed model is consistent with the trend of the measurements whenever quiet and disturbed geomagnetic conditions. The average RMSE and RRMSE are 0.86 MHz and 17.36% using the past 27 days measured data during the year of 2008-2015. Moreover, the analysis results have proved the proposed method has the potential to forecasting any other ionospheric characteristic parameters and can achieve 1-hr forecasting for global by combining spatial reconstruction technique.

Scale‐Separated Dynamic Mode Decomposition and Ionospheric Forecasting

“…We will show that a short-term forecast of the foF2 and hmF2 for a single-station sounder is obtainable purely through modeling the variations observed during a 10-day period. While other attempts at forecasting ionospheric parameters without specifying drivers or control variables have seen success, see (Grzesiak et al, 2018;Stanislawska & Zbyszynski, 2001;Wang et al, 2020), we show that straightforward scale separation enables the use of powerful data-driven methods such as DMD. Of course, such an approach will not capture storms or large perturbations to the EDP that one would see with the appropriate exogenous control variables.…”

“…SSDMD is one among many recent attempts to improve short‐term forecasts of the foF2 and hmF2 parameters (cf., Mikhailov & Perrone, 2014; Perrone & Mikhailov, 2022; Tsagouri et al., 2018; Wang et al., 2020; Zhang et al., 2014). While other methods generally treat past foF2 or hmF2 measurements as inputs to the model, SSDMD instead uses the full EDP.…”

Scale-Separated Dynamic Mode Decomposition and Ionospheric Forecasting

“…We performed an av- SSDMD is one among many recent attempts to improve short-term forecasts of the foF2 and hmF2 parameters (cf. Perrone & Mikhailov, 2022;Wang et al, 2020;Tsagouri et al, 2018;Mikhailov & Perrone, 2014;Zhang et al, 2014). While other methods generally treat past foF2 or hmF2 measurements as inputs to the model, SSDMD instead uses the full EDP.…”

“…In this paper, however, we will show that a short-term forecast of the foF2 and hmF2 for a single-station sounder is indeed obtainable purely through modeling the variations observed during a 10-day period. While other attempts at forecasting ionospheric parameters without specifying drivers or control variables have seen success, see (Wang et al, 2020;Grzesiak et al, 2018;Stanislawska & Zbyszynski, 2001), we show that straightforward scale separation enables the use of powerful data-driven methods such as DMD. Of course, such an approach will not capture storms or large perturbations to the EDP that one would see with the appropriate exogenous control variables.…”

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

“…The effect of the proposed method in this paper during the ionospheric quiet period was explained above. Considering the completeness of the validation, we took the test results during the ionospheric storm period on 16 March 2015, as an example (Wang, Feng, & Ma, 2019), and gave the HASR iteration results at 0 UTC of El Arensillo station and 12 UTC of Fairford station respectively, as shown in Figure 10a. It can be seen that compared with the ionospheric quiet period, the HASR method can still achieve a better convergence effect during ionospheric storms with more iterations, and the predicted RRMSE of the two stations with 50 iterations is 6.35% and 7.98%, respectively.…”