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A comparison of neural network-based predictions of foF2 with the IRI-2012 model at conjugate points in Southeast Asia
(2017-06-15)
Wichaipanich, Noraset
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Hozumi, Kornyanat
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Supnithi, Pornchai
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Tsugawa, Takuya
This paper presents the development of Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at three ionosonde stations near the magnetic equator of Southeast Asia. Two of these stations including Chiang Mai (18.76°N, 98.93°E, dip angle 12.7°N) and Kototabang (0.2°S, 100.3°E, dip angle 10.1°S) are at the conjugate points while Chumphon (10.72°N, 99.37°E, dip angle 3.0°N) station is near the equator. To produce the model, the feed forward network with backpropagation algorithm is applied. The NN is trained with the daily hourly values of foF2 during 2004–2012, except 2009, and the selected input parameters, which affect the foF2 variability, include day number (DN), hour number (HR), solar zenith angle (C), geographic latitude (θ), magnetic inclination (I), magnetic declination (D) and angle of meridian (M) relative to the sub-solar point, the 7-day mean of F10.7 (F10.7_7), the 81-day mean of SSN (SSN_81) and the 2-day mean of Ap (Ap_2). The foF2 data of 2009 and 2013 are then used for testing the NN model during the foF2 interpolation and extrapolation, respectively. To examine the performance of the proposed NN, the root mean square error (RMSE) of the observed foF2, the proposed NN model and the IRI-2012 (CCIR and URSI options) model are compared. In general, the results show the same trends in foF2 variation between the models (NN and IRI-2012) and the observations in that they are higher during the day and lower at night. Besides, the results demonstrate that the proposed NN model can predict the foF2 values more closely during daytime than during nighttime as supported by the lower RMSE values during daytime (0.5 ≤ RMSE ≤ 1.0 for Chumphon and Kototabang, 0.7 ≤ RMSE ≤ 1.2 at Chiang Mai) and with the highest levels during nighttime (0.8 ≤ RMSE ≤ 1.5 for Chumphon and Kototabang, 1.2 ≤ RMSE ≤ 2.0 at Chiang Mai). Furthermore, the NN model predicts the foF2 values more accurately than the IRI model at the three sites on average, as clearly seen on the yearly RMSE averages. The RMSE values of NN model are lower than those of both CCIR and URSI options, and in terms of the yearly percentage improvements, the NN model gives improvement of around 10–15% in 2009 and 10% in 2013 for Chiang Mai, 20–25% in 2009 and 5–10% in 2013 for Chumphon, and around 18–25% in 2009 and 20–30% in 2013 at Kototabang. Although the NN model predicts the foF2 values closely to the observed data and produces more accurate prediction than the IRI models, in some cases, the IRI model performs better than the NN model. Hence, there is still room for further improvement of the proposed NN model.
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The occurrence of equatorial spread-F at conjugate stations in Southeast Asia
(2015-04-15)
Klinngam, Somjai
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Supnithi, Pornchai
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Rungraengwajiake, Sarawoot
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Tsugawa, Takuya
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Ishii, Mamoru
In this study, the probability of equatorial spread-F (ESF) occurrence at the conjugate stations in Southeast Asia: Chiangmai station (CMU), Thailand, and Kototabang station (KTB), Indonesia, and near the magnetic equator, Chumphon station (CPN), Thailand, is presented. We analyze the ionogram data recorded by the Frequency Modulated Continuous Wave (FM/CW) ionosondes for the periods of minimum solar activity from September 2008 to April 2009 and in the equinoctial months (March and April) from 2006 to 2013. The spread-F signatures are manually categorized into three types: the frequency spread-F (FSF), the range spread-F (RSF) and the mixed spread-F (MSF) and the monthly average percentage of the occurrence of each ESF type is presented. The results show that the percentage of RSF occurrence at CPN, which is located around the magnetic equator, is higher than at other stations and the RSF mostly occurs during the equinoctial months. On the other hand, the FSF occurrence at CMU and KTB, that are located in the northern and southern hemispheres, respectively, are higher than at CPN. The RSF occurrence typically has the peaks before midnight, while the maximum occurrence rate of FSF is after midnight. Furthermore, the RSF onsets normally precede the FSF onsets by about 1-2 h. As the solar activity levels go up, the percentages of RSF occurrence increase, but the percentages of FSF tends to decrease. In addition, we compare the statistics of observed RSF occurrence with the prediction of the IRI-2012 model. The results show that the IRI model overestimates the observed RSF occurrence at all stations during most of the solar activity levels and seasons, except in June 2008 (21:00-03:00 LT), March 2011 (23:30-01:00 LT) and March 2013 (01:00-02:30 LT) when the IRI model underestimates our observations. However, the IRI model gives closer probability of RSF occurrence to our observed values at CPN, especially in equinoctial months and during the periods of medium solar activity. This work is important for an improvement of the IRI model in the prediction of the spread F occurrence probability in the low-latitude region.
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Prediction of foF2 using Neural Network at Thailand equatorial latitude station, Chumphon
(2014-01-01)
Wichaipanich, Noraset
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Supnithi, Pornchai
This paper proposes the development of a Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at the magnetic equator over Chumphon (10.72°N, 99.37°E, dip angle 3.3°N), Thailand and then compared with the IRI model and the experimental ones. The feed forward network with backpropagation algorithm has been developed for predicting the foF2 values. The NN is trained with the daily hourly values of foF2 during the period from 2004 to 2008 and the input parameters affecting the foF2 variability including the hour number, day number, F10.7 index and sunspot number (SSN). To examine the performance of the proposed NN, the root mean square error (RMSE) of the observed foF2, the proposed NN model and the IRI (both CCIR and URSI options) model are compared in 2009. The results show that the NN model predicts the foF2 values close to the observed data, particularly during daytime. Moreover, the NN model can predicts more accurate than the IRI model that is supported by the lower RMSE. However, the NN model provides slightly deviation of prediction during pre-sunrise hours since the observed foF2 data for NN training in this periods are fewer than those during daytime. © 2014 IEEE.
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The variation of critical frequency of E layer over Chumphon, Thailand
(2013-12-09)
Wongcharoen, Poramintra
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Kenpankho, Prasert
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Sepsirisuk, Kasemsuk
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Supnithi, Pornchai
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Noppanakeepong, Suthichai
In this research paper presents the observed critical frequency of E layer (foE) over Chumphon, Thailand which is located at nearly the magnetic equator (lat. 10.72° long. 99.37° dip. 3°) during September 2004 to August 2005. The foE is one of essential parameters in the International Reference Ionsphere (IRI) model. We investigate the variation of the observed foE by comparing to the Sun spot number, ionospheric index, and 10.7 cm. flux. Due to essentially of accurate IRI model can be used to applied in communication such as it may be implied tendency of phenomena in E layer effect on high frequency communication over Chumphon, Thailand. © 2013 IEEE.
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Ionospheric variation at Thailand equatorial latitude station: Comparison between observations and IRI-2001 model predictions
(2010-01-15)
Wichaipanich, Noraset
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Supnithi, Pornchai
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Ishii, Mamoru
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Maruyama, Takashi
In this paper, the F2-layer critical frequency (foF2) and peak height (hmF2) measured by the FM/CW ionosonde at Thailand equatorial latitude station, namely Chumphon (10.72°N, 99.37°E, dip 3.22) are presented. The measurement data during low solar activity from January 2004 to December 2006 are analyzed based on the diurnal, seasonal variation. The results are then compared with IRI-2001 model predictions. Our study shows that: (1) In general, both the URSI and CCIR options of the IRI model give foF2 close to the measured ones, but the CCIR option produces a smaller range of deviation than the URSI option. The agreement during daytime is generally better than during nighttime. Overestimation mostly occurs in 2004 and 2006, while underestimation is during pre-sunrise hours in June solstice in 2005. The peak foF2 around sunset is higher during March equinox and September equinox than the other seasons, with longer duration of maximum levels in March equinox than September equinox. Large coefficients of variability foF2 occur during pre-sunrise hours. Meanwhile, the best agreement between the observed foF2 and the IRI model is obtained in June solstice. (2) In general, The IRI (CCIR) model predicts the observed hmF2 well during daytime in June solstice from 2004-2006, but it overestimates during March equinox, September equinox and December solstice. For nighttime, the model overestimates hmF2 values for all seasons especially during March equinox and September equinox. However, the model underestimates hmF2 values during September equinox and for some cases during June solstice and December solstice at pre-sunrise. The agreement between the IRI model and the hmF2(M3000 <inf>OBS</inf> ) is worst around noontime, post-sunset and pre-sunrise hours. All comparative studies give feedback for new improvements of CCIR and URSI IRI models. © 2009.

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