Investigation of Two Prediction Models of Maximum Usable Frequency for HF Communication Based on Oblique- and Vertical-Incidence Sounding Data

Abstract

As one of the key technologies of HF communication, the maximum usable frequency (MUF) prediction method has been widely discussed. To experimentally confirm the reliability of commonly used MUFs prediction models for high-frequency communication, we have compared maximum observed frequencies (MOFs) and predicted MUFs to assess the accuracy of two typical prediction models. The root-mean-square error (RMSE) and relative RMSE (RRMSE) between oblique sounding MOFs and the predicted MUFs were used to assess the model’s accuracy. The oblique sounding path was from Changchun to Jinyang, and the vertical-sounding ionosonde was located in Beijing, which was approximately the midpoint of the oblique sounding circuit. The statistical analysis results show that: (a) the trend of prediction results from the Lockwood and the Istituto Nazionale di Geofisica e Vulcanologia (INGV) model are in good agreement with the observations: the mean RMSE and RRMSE of the INGV model are less than those of the Lockwood model; (b) in the four different periods (sunrise, daytime, sunset, and nighttime) of the whole day, the maximum difference of RMSE between the Lockwood and INGV model is 0.14 MHz (the INGV performs better than the LWM), with the corresponding differences of RRMSE being 0.31% at sunrise and 0.68% at daytime; (c) in the four seasons of spring, summer, autumn, and winter, the minimum RMSE values of the Lockwood and INGV models are 1.51 MHz and 1.37 MHz, respectively, which are obtained in winter, and the corresponding RRMSEs are 11.47% and 11.79%, respectively; (d) in the high and low solar activity epochs, the mean RMSEs of the Lockwood and INGV models are 1.63 MHz, and 1.54 MHz, with corresponding mean RRMSE values of 11.47% and 11.55%. In conclusion, the INGV model is more suitable for MUF prediction over Beijing and its adjacent mid-latitude regions from the RMSE comparison of the two models.

A New Determining Method for Ionospheric F2-Region Peak Electron Density Height

“…(4) SML needs to set model evaluation criteria to evaluate the merits and demerits of the trained model, in which root mean square error (RMSE) and relative root mean square error (RRMSE) are selected. RMSE can evaluate performance changes and characterize the impact caused by data perturbations, while RRMSE is used to assess the percentage of relative performance changes [26].…”

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

“…The modeling data used in this paper come from the global ionospheric radio observatory (GIRO) (GIRO, 2022), which can automatically measure the vertical electron density profile through the ionospheric digital detectors 10.1029/2023SW003663 3 of 16 deployed around the world such as DCS-1, DGS-256, DCS-4, etc., and give the VTEC value and other classical ionospheric characteristic parameters (Wang et al, 2022). Considering that TEC at different stations has various sampling intervals, and this paper focuses on the modeling of spatial characteristics of TEC, to ensure the uniformity of data format, we choose 60 min as the unified sampling interval.…”

A Prediction Method of Ionospheric hmF2 Based on Machine Learning

“…Moreover, estimating and predicting the characteristics of hmF2 is vital for identifying adverse space weather [8] and hmF2 is a major aeronautical parameter involved in aeronautical [9] and ionospheric electrodynamic [10] studies. In general, the hmF2 can be observed at the sounding station using ionospheric sounders [11]. Without sounding stations, hmF2 can be predicted based on the ionospheric models that provide helpful empirical values for educators, engineers, and scientists.…”