Abstract
It is critically meaningful to accurately predict the ionospheric F2 layer critical frequency (foF2), which greatly limits the efficiency of communications, radar, and navigation systems. This paper introduced the entropy weight method to develop the combination prediction model (CPM) for long-term foF2 at Darwin (12.4° S, 131.5° E) in Australia. The weight coefficient of each individual model in the CPM is determined by using the entropy weight method after completing the simulation of the individual model in the calibration period. We analyzed two sets of data to validate the method used in this study: One set is from 2000 and 2009, which are included in the calibration period (1998–2016), and the other set is outside the calibration cycle (from 1997 and 2017). To examine the performance, the root mean square error (RMSE) of the observed monthly median foF2 value, the proposed CPM, the Union Radio Scientifique Internationale (URSI), and the International Radio Consultative Committee (CCIR) are compared. The yearly RMSE average values calculated from CPM were less than those calculated from URSI and CCIR in 1997, 2000, 2009, and 2017. In 2000 and 2009, the average percentage improvement between CPM and URSI is 9.01%, and the average percentage improvement between CPM and CCIR is 13.04%. Beyond the calibration period, the average percentage improvement between CPM and URSI is 13.2%, and the average percentage improvement between CPM and CCIR is 12.6%. The prediction results demonstrated that the proposed CPM has higher precision of prediction and stability than that of the URSI and CCIR, both within the calibration period and outside the calibration period.
Highlights
The critical frequency of the F2 layer of the ionosphere is one of the most important parameters in various civil and military applications [1,2]
The foF2 prediction results of the Union Radio Scientifique Internationale (URSI) and CCIR were simulated during the calibration period
This paper proposed a combined prediction model of the ionospheric foF2 based on the entropy
Summary
The critical frequency of the F2 layer of the ionosphere (foF2) is one of the most important parameters in various civil and military applications [1,2]. The improvement of existing foF2 prediction models will play an important role in the planning and frequency management of HF radio systems, HF automatic link establishment, and global positioning satellites. Popular methods used to predict the foF2 include the International Reference Ionosphere (IRI) [6,7,8,9,10,11,12], Artificial Neural Network (ANN) model [13,14,15,16,17,18,19], Support Vector Machine technique. Researchers compared each newly developed ionospheric parameter model with the IRI model to ensure that the new method has better performance than the IRI model [1]
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