Forecasting the ionospheric F2 Parameters over Jeju Station (33.43°N, 126.30°E) by Using Long Short-Term Memory

Moon, Su-In; Kim, Yong Ha; Kim, JeongHeon; Kwak, Young‐Sil; Yoon, Jong-Yeon

doi:10.3938/jkps.77.1265

Cited by 19 publications

(49 citation statements)

References 18 publications

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Section: Design Of Lstmmentioning

confidence: 99%

“…In this study, we adopt the structure of LSTM made by Moon et al (2020). Moon et al (2020) designed the LSTM model using the Jeju (33.43°N, 126.30°E) ionosonde data ( foF 2, hmF 2) (https://spaceweather.rra.go.kr/observation/service/iono). In addition, they included the observed solar and geomagnetic indices such as the F 10.7, sunspot number (SSN), and Kp index (https://omniweb.gsfc.nasa.gov/ow.html) because ionospheric parameters are strongly related to these indices.…”

Section: Design Of Lstmmentioning

confidence: 99%

“…To organize the training and validation data into 1‐hr units, Moon et al (2020) reconstructed the missing ionospheric data by filling in with the data observed in the same local time on the previous day. Since the time resolutions of the solar and geomagnetic indices are different, they used the running mean method of 81 days (2 days) for the sunspot number (ap index), and individual values for Kp and F 10.7 itself in units of 1 hr.…”

Section: Design Of Lstmmentioning

confidence: 99%

“…For example, Hu and Zhang (2018) attempted the hmF 2 prediction using the LSTM and bi‐LSTM algorithms. Most recently, Moon et al (2020) conducted a study using the LSTM model to perform long‐term prediction. They trained long‐term data of Jeju ionosonde, performed predictions for the next 24 hr, and produced better prediction values than existing models.…”

Section: Introductionmentioning

confidence: 99%

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Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

et al. 2020

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This paper presents a study on the possibility of predicting the regional ionosphere at midlatitude by assimilating the predicted ionospheric parameters from a neural network (NN) model into the Sami2 is Another Model of the Ionosphere (SAMI2). The NN model was constructed from the data set of Jeju ionosonde (33.43°N, 126.30°E) for the period of 1 January 2011 to 31 December 2015 by using the long-short term memory (LSTM) algorithm. The NN model provides 24-hr prediction of the peak density (NmF2) and peak height (hmF2) of the F2 layer over Jeju. The predicted NmF2 and hmF2 were used to compute two ionospheric drivers (total ion density and effective neutral meridional wind), which were assimilated into the SAMI2 model. The SAMI2-LSTM model estimates the ionospheric conditions over the midlatitude region around Jeju on the same geomagnetic meridional plane. We evaluate the performance of the SAMI2-LSTM by comparing predicted NmF2 and hmF2 values with measured values during the geomagnetic quiet and storm periods. The root-mean-square error values of NmF2 (hmF2) from Jeju ionosonde measurements are lower by 45% and 45% (30% and 11%) than those of the SAMI2 and IRI-2016 models during the geomagnetic quiet periods. However, during the geomagnetic storm periods, the performance of the SAMI2-LSTM model does not predict positive geomagnetic storms well. Comparing the quiet and storm periods for the SAMI2-LSTM model, the root-mean-square error (RMSE) of the storm period was calculated to be 2.76 (3.2) times higher at Jeju (Icheon) than in the quiet period. From these results, we demonstrated that in this study, the combination of the NN-LSTM model and physics-based model could improve the ionosphere prediction of existing theoretical and empirical models for midlatitude regions, at least in geomagnetically quiet conditions. We strongly suggest that this attempt, which has not been reported before, could be used as one of the keys to advance the physics-based model further.

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Section: Design Of Lstmmentioning

confidence: 99%

Section: Design Of Lstmmentioning

confidence: 99%

Section: Design Of Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

et al. 2020

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“…Especially, the long‐range high frequency (HF) communication is dominantly affected by reflection from the ionosphere F2 layer. Although numerous models have been developed to simulate and predict changes in the ionosphere of the F2 layer, it is relatively recent that the deep‐learning technology began to be utilized in the ionospheric models (e.g., Altinay et al., 1997; Athieno et al., 2017; Fan et al., 2019; Hu & Zhang, 2018; Kim et al., 2020; McKinnell & Poole, 2000; Moon et al., 2020; Nakamura et al., 2007; Oyeyemi et al., 2005; Poole & Poole, 2002; Sai Gowtam & Tulasi Ram, 2017; Tulasi Ram et al., 2018; Wichaipanich et al., 2017; Williscroft & Poole, 1996; Wintoft & Cander, 2000; Yue et al., 2006).…”

Section: Introductionmentioning

confidence: 99%

Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period

et al. 2021

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In our previous study (Moon et al., 2020, https://doi.org/10.3938/jkps.77.1265), we developed a long short‐term memory (LSTM) deep‐learning model for geomagnetic quiet days (LSTM‐quiet) to perform effective long‐term predictions for the regional ionosphere. However, their model could not predict geomagnetic storm days effectively at all. This study developed an LSTM model suitable for geomagnetic storms using the new training data set and redesigning input parameters and hyper‐parameters. We collected 131 days of geomagnetic storm cases from January 1, 2009 to December 31, 2019, provided by the Japan Meteorological Agency's Kakioka Magnetic Observatory, and obtained the interplanetary magnetic field Bz, Dst, Kp, and AE indices related to the geomagnetic storm corresponding to each storm date from the OMNI database. These indices and F2 parameters (foF2 and hmF2) of Jeju ionosonde (33.43°N, 126.30°E) were used as input parameters for the LSTM model. To test and verify the predictive performance and the usability of the LSTM model for geomagnetic storms developed in this manner, we created and diagnosed the 0.5, 1, 2, 3, 6, 12, and 24‐h predictive LSTM models. According to the results of this study, the LSTM storm model for 24‐h developed in this study achieved a predictive performance during the three geomagnetic storms about 32% (10%), 34% (17%), and 37% (5%) better in root mean square error of foF2 (hmF2) than the LSTM quiet model (Moon et al., 2020, https://doi.org/10.3938/jkps.77.1265), SAMI2, and IRI‐2016 models. We propose that the short‐term predictions of less than 3 h are sufficiently competitive than other traditional ionospheric models. Thus, this study suggests that our model can be used for short‐term prediction and monitoring of the regional mid‐latitude ionosphere.

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Potential of regional ionosphere prediction using a long short-term memory (LSTM) deep-learning algorithm specialized for geomagnetic storm period

Kim

Kwak

Kim

et al. 2021

Preprint

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In our previous study (Moon et al., 2020), we developed a Long Short Term Memory (LSTM) deep-learning model for geomagnetic quiet days to perform effective long-term predictions for the regional ionosphere. However, their model could not predict geomagnetic storm days effectively at all. This study developed an LSTM model suitable for geomagnetic storms using the new training data set and re-designing input parameters and hyper-parameters. We collected 131 days of geomagnetic storm cases from 1 January 2009 to 31 December 2019, and obtained the IMF Bz, Dst, Kp, and AE indices related to the geomagnetic storm corresponding to each storm date. These indices and F2 parameters of Jeju ionosonde (33.43@N, 126.30@E) were used as input parameters for the LSTM model. To test and verify the predictive performance and the usability of the LSTM model for geomagnetic storms developed in this manner, we created and diagnosed the 0.5, 1, 2, 3, 6, 12, and 24-hour predictive LSTM models. According to the results of this study, the LSTM storm model for 24-hour developed in this study achieved a predictive performance during the geomagnetic storms about 32% (10%), 34% (17%), and 37% (5%) better in RMSE of foF2 (hmF2) than the LSTM quiet model (Moon et al., 2020), SAMI2, and IRI-2016 models. We propose that the short-term predictions of less than 3 hours are sufficiently competitive compared to other traditional ionospheric models. Thus, this study suggests that our model can be used for short-term prediction and monitoring of the regional mid-latitude ionosphere.

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Forecasting the ionospheric F2 Parameters over Jeju Station (33.43°N, 126.30°E) by Using Long Short-Term Memory

Cited by 19 publications

References 18 publications

Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model

Potential of Regional Ionosphere Prediction Using a Long Short‐Term Memory Deep‐Learning Algorithm Specialized for Geomagnetic Storm Period

Potential of regional ionosphere prediction using a long short-term memory (LSTM) deep-learning algorithm specialized for geomagnetic storm period

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