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1. Reconstruction of the Regional Total Electron Content Maps Over the Korean Peninsula Using Deep Convolutional Generative Adversarial Network and Poisson Blending

   https://doi.org/10.1029/2022SW003131  

   Jeong, Se‐Heon; Lee, Woo Kyoung; Jang, Soojeong; Kil, Hyosub; Kim, Jeong‐Heon; Kwak, Young‐Sil; Kim, Yong Ha; Hong, Junseok; Choi, Byung‐Kyu (August 2022, Space Weather)

   Abstract This study reconstructs total electron content (TEC) maps in the vicinity of the Korean Peninsula by employing a deep convolutional generative adversarial network and Poisson blending (DCGAN‐PB). Our interest is to rebuild small‐scale ionosphere structures on the TEC map in a local region where pronounced ionospheric structures, such as the equatorial ionization anomaly, are absent. The reconstructed regional TEC maps have a domain of 120°–135.5°E longitude and 25.5°–41°N latitude with 0.5° resolution. To achieve this, we first train a DCGAN model by using the International Reference Ionosphere‐based TEC maps from 2002 to 2019 (except for 2010 and 2014) as a training data set. Next, the trained DCGAN model generates synthetic complete TEC maps from observation‐based incomplete TEC maps. Final TEC maps are produced by blending of synthetic TEC maps with observed TEC data by PB. The performance of the DCGAN‐PB model is evaluated by testing the regeneration of the masked TEC observations in 2010 (solar minimum) and 2014 (solar maximum). Our results show that a good correlation between the masked and model‐generated TEC values is maintained even with a large percentage (∼80%) of masking. The performance of the DCGAN‐PB model is not sensitive to local time, solar activity, and magnetic activity. Thus, the DCGAN‐PB model can reconstruct fine ionospheric structures in regions where observations are sparse and distinguishing ionospheric structures are absent. This model can contribute to near real‐time monitoring of the ionosphere by immediately providing complete TEC maps. 

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2. Validation of Ionospheric Modeled TEC in the Equatorial Ionosphere During the 2013 March and 2021 November Geomagnetic Storms

   https://doi.org/10.1029/2023SW003480  

   Chou, Min‐Yang; Yue, Jia; Wang, Jack; Huba, J. D.; El Alaoui, Mostafa; Kuznetsova, Maria M.; Rastätter, Lutz; Shim, Ja Soon; Fang, Tzu‐Wei; Meng, Xing; et al (June 2023, Space Weather)

   Key Points Validation of ionospheric total electron content (TEC) by the state‐of‐the‐art ionospheric models hosted by NASA Community Coordinated Modeling Center, National Oceanic and Atmospheric Administration Space Weather Prediction Center, and NASA Jet Propulsion Laboratory (JPL) Multiple metrics and skill scores are used to assess the performance of ionospheric models in capturing storm time TEC anomaly GLObal Total Electron Content and JPL Global Ionospheric Map perform best, and physics‐based models perform better than the empirical model in capturing storm TEC variations 

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3. Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

   https://doi.org/10.1029/2023SW003763  

   Jeong, Se‐Heon; Lee, Woo Kyoung; Kil, Hyosub; Jang, Soojeong; Kim, Jeong‐Heon; Kwak, Young‐Sil (January 2024, Space Weather)

   Abstract This study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short‐term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN‐PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN‐PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction. 

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4. 3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA)

   https://doi.org/10.1029/2023SW003792  

   Aa, Ercha; Zhang, Shun‐Rong; Erickson, Philip J.; Wang, Wenbin; Coster, Anthea J.; Rideout, William (February 2024, Space Weather)

   Abstract This study has developed a new TEC‐based ionospheric data assimilation system for 3‐D regional ionospheric imaging over the South American sector (TIDAS‐SA) (45°S–15°N, 35°–85°W, and 100–800 km). The TIDAS‐SA data assimilation system utilizes a hybrid Ensemble‐Variational approach to incorporate a diverse set of ionospheric data sources, including dense ground‐based Global Navigation Satellite System (GNSS) line‐of‐sight Total Electron Content (TEC) data, radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (COSMIC‐2), and altimeter TEC data from the JASON‐3 satellite. TIDAS‐SA can produce a reanalyzed three‐dimensional (3‐D) electron density spatial variation with a high time cadence, yielding spatial‐temporal resolution of 1° (latitude) × 1° (longitude) × 20 km (altitude) × 5 min. This allows us to reconstruct and study the 3‐D ionospheric morphology with multi‐scale structures. The performance of the data assimilation system is validated against independent ionosonde and in situ measurements through an experiment for a strong geomagnetic storm event on 03–04 November 2021. The results demonstrate that TIDAS‐SA can provide detailed and altitude‐resolved information that accurately characterizes the storm‐time ionospheric disturbances in vertical and horizontal domains over the equatorial and low‐latitude regions of South America. 

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5. Direct EUV/X-ray Modulation of the Ionosphere during the August 2017 Total Solar Eclipse

   https://doi.org/DOI:10.1029/2017GL076771  

   Mrak, Sebastijan; Semeter, Joshua; Drob, Douglas; Huba, J. (May 2018, Geophysical research letters)

   The great American total solar eclipse of 21 August 2017 offered a fortuitous opportunity to study the response of the atmosphere and ionosphere using a myriad of ground instruments. We have used the network of U.S. Global Positioning System receivers to examine perturbations in maps of ionospheric total electron content (TEC). Coherent large-scale variations in TEC have been interpreted by others as gravity wave-induced traveling ionospheric disturbances. However, the solar disk had two active regions at that time, one near the center of the disk and one at the edge, which resulted in an irregular illumination pattern in the extreme ultraviolet (EUV)/X-ray bands. Using detailed EUV occultation maps calculated from the National Aeronautics and Space Administration Solar Dynamics Observatory Atmospheric Imaging Assembly images, we show excellent agreement between TEC perturbations and computed gradients in EUV illumination. The results strongly suggest that prominent large-scale TEC disturbances were consequences of direct EUV modulation, rather than gravity wave-induced traveling ionospheric disturbances. 

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