Abstract
The height of F2 peak (hmF2) is an essential ionospheric parameter and its variations can reflect both the earth magnetic and solar activities. Therefore, reliable prediction of hmF2 is important for the study of space, such as solar wind and extreme weather events. However, most current models are unable to forecast the variation of the ionosphere effectively since real-time measurements are required as model inputs. In this study, a new Australian regional hmF2 forecast model was developed by using ionosonde measurements and the bidirectional Long Short-Term Memory (bi-LSTM) method. The hmF2 value in the next hour can be predicted using the data from the past five hours at the same location. The inputs chosen from a location of interest include month of the year, local time (LT), K p , F 10 . 7 and hmF2 as an independent variable vector. The independent variable vectors in the immediate past five hours are considered as an independent variable set, which is used as an input of the new Australian regional hmF2 forecast model developed for the prediction of hmF2 in the hour to come. The performance of the new model developed is evaluated by comparing with those from other popular models, such as the AMTB, Shubin, ANN and LSTM models. Results showed that: (1) the new model can substantially outperform all the other four models. (2) Compared to the LSTM model, the new model is proven to be more robust and rapidly convergent. The mew model also outperforms that of the ANN model by around 30%. (3) the minimum sample number for the bi-LSTM method (i.e., 2000) to converge is about 50% less than that is required for the LSTM method (i.e., 3000). (4) Compared to the Shubin model, the bi-LSTM method can effectively forecast the hmF2 values up to 5 h. This research is a first attempt at using the deep learning-based method for the application of the ionospheric prediction.
Highlights
The height of F2 peak is an essential ionospheric parameter that is defined by the altitude of electron density (Ne) peak in the ionosphere
The mew model outperforms that of the artificial neural network (ANN) model by around 30%. (3) the minimum sample number for the bidirectional Long Short-Term Memory (bi-Long Short-Term Memory (LSTM)) method (i.e., 2000) to converge is about 50% less than that is required for the LSTM method (i.e., 3000)
Only the stations that have more than 1000 sample sets are selected
Summary
The height of F2 peak (hmF2) is an essential ionospheric parameter that is defined by the altitude of electron density (Ne) peak in the ionosphere. Tulasi [14] further enhanced his model by including other RO measurements (i.e., GRACE and CHAMP) as well as ionosonde data as data sources He assessed the performance of the ANN model by comparing with IRI-2016 model, and proved that the anomalies of ionosphere (e.g., equatorial ionosphere anomaly (EIA) and mid-latitude summer night-time anomaly (MSNA)) are well captured by the model [13,14]. In comparison with ANN, the bi-LSTM method can be considered as a special type of the recurrent neural network (RNN) technique which takes into account of the sequential variation of hmF2 value This advantage offers us an opportunity to perform the prediction by using the data in recent epochs.
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