Evaluation of foF2 and hmF2 Parameters of IRI-2016 Model in Different Latitudes over China under High and Low Solar Activity Years

Zhang, Bingbing; Wang, Zhengtao; Shen, Yi; Li, Wang; Xu, Feng; Li, Xiaoxiao

doi:10.3390/rs14040860

Cited by 9 publications

(6 citation statements)

References 31 publications

(39 reference statements)

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“…When we use the IRI‐2016 model, CCIR storm is turned on when foF2 is predicted at low and high latitude stations, and URSI storm is turned on when foF2 is predicted at mid‐latitude stations. At the same time, we select “F‐peak storm model” on option in geomagnetic‐disturbed days (Zhang et al., 2022). All models use the same training set, test set segmentation method and input data to further compare and analyze the different models.…”

Section: Resultsmentioning

confidence: 99%

Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism

Tang,

Yang,

Ding

2023

Space Weather

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The critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM‐Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM‐Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM‐Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model.

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Section: Resultsmentioning

confidence: 99%

Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism

Tang,

Yang,

Ding

2023

Space Weather

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“…The ionospheric models primarily recreate ongoing ionospheric responses, and their reliance on real observations presents a difficulty in areas with a sparse and uneven distribution of ionosonde stations. The ionospheric models, on the other hand, provide averaged electron density measurements in both time and space, which may differ from the real values found using ionosondes [ 58 , 76 , 78 , 79 , 85 , 86 ]. The IRI-Plas 2017 model overestimates totally on June 17, 2014 at 14 UT ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Performance evaluation of IRI-Plas 2017 model with ionosonde data measurements of ionospheric parameters

Endeshaw,

Seyoum

2023

Heliyon

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“…The occurrence of this anomaly has been attributed to the effects of neutral wind composition, temperature, electric field, and other chemical parameters, such as the O/N2 ratio. Neutral particles are transported from the southern hemisphere to the northern hemisphere during different solar activity epochs [16,20,[32][33][34][35][36][37][38][39]. Winter anomaly is usually an equatorial and lowlatitude ionospheric phenomenon.…”

Section: Seasonal Variations In Nmf2mentioning

confidence: 99%

Variations in the Maximum Electron Density of the F2 Layer (NmF2) over the Middle Latitude Station of Grahamstown, South Africa, during Solar Cycle 23

Ogwala¹,

Onori²,

Ogabi³

et al. 2022

Adv Environ Eng Res

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Ultraviolet (UV) and X-ray radiation are the primary causes of ionization that produce electron density in sufficient quantities to promote the propagation of satellite radio signals in the ionosphere. The electron densities suffer from spatio-temporal variations, and this poses different degrees of threats to satellite radio signals propagating through the ionosphere. We aimed to characterize the maximum electron density of the F2 layer (NmF2) in the middle-latitude ionosphere over Grahamstown, South Africa (Geographic latitude: 33.30°S, Geographic longitude: 26.50°E; Geomagnetic Latitude: 33.92°S, Geomagnetic Longitude: 89.37°E). The mean NmF2 data for solar cycle 23 (1998–2008) were used for the studies. The data were grouped into the high solar activity (HSA: 2000–2002), moderate solar activity (MSA: 1998–1999, 2003–2005), and low solar activity (LSA: 2006–2008) years. NmF2 variations were characterized based on the diurnal, seasonal, monthly, and annual data. Also, the correlation between NmF2 and the sunspot number was investigated. Results on diurnal and seasonal variations revealed that noontime bite-out of NmF2 was observed during the June solstice every year. However, it was not observed in the other three seasons. Equinoctial asymmetry is observed to show insignificant annual and solar cycle variations. The seasonal and annual variations of NmF2 with sunspot number were linear (exception: June solstice for MSA, the year 1999; HSA, years 2000–2001). The results reveal that the correlation between NmF2 and the sunspot number was insignificant under conditions of the annual, solar cycle, and latitudinal variations (exception: MSA, the year 2005; negative correlation (0.64)).

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Evaluation of foF2 and hmF2 Parameters of IRI-2016 Model in Different Latitudes over China under High and Low Solar Activity Years

Cited by 9 publications

References 31 publications

Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism

Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism

Performance evaluation of IRI-Plas 2017 model with ionosonde data measurements of ionospheric parameters

Variations in the Maximum Electron Density of the F<sub>2</sub> Layer (NmF<sub>2</sub>) over the Middle Latitude Station of Grahamstown, South Africa, during Solar Cycle 23

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