Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm
Abstract In this work, we evaluated the quasi‐realistic ionosphere forecasting capability by an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is use...
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| Format: | Article |
| Language: | English |
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Wiley
2020-03-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2019SW002410 |
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| author | Jianhui He Xinan Yue Huijun Le Zhipeng Ren Weixing Wan |
| author_facet | Jianhui He Xinan Yue Huijun Le Zhipeng Ren Weixing Wan |
| author_sort | Jianhui He |
| collection | DOAJ |
| description | Abstract In this work, we evaluated the quasi‐realistic ionosphere forecasting capability by an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as the background model in the system. The slant total electron contents (TECs) from global International Global Navigation Satellite Systems Service ground‐based receivers and from the Constellation Observing System for Meteorology, Ionosphere and Climate are assimilated into the system, and the ionosphere is then predicted in advance during the quiet interval of 23 to 27 March 2010. The predicted ionosphere vertical TEC (VTEC) and the critical frequency foF2 are validated by the Massachusetts Institute of Technology VTEC and global ionosondes network, respectively. We found that the ionosphere forecast quality could be enhanced by optimizing the thermospheric neutral components via the EnKF method. The ionosphere electron density forecast accuracy can be improved by at least 10% for 24 hr. Furthermore, the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Global Ultraviolet Imager (TIMED/GUVI) [O/N2] observations are used to validate the predicted thermosphere [O/N2]. The validation shows that the [O/N2] optimized by EnKF has better agreement with the TIMED/GUVI observation. This study further demonstrates the validity of EnKF in enhancing the ionospheric forecast capability in addition to our previous observing system simulation experiments by He et al. (2019, https://doi.org/10.1029/2019JA026554). |
| format | Article |
| id | doaj-art-0b6450258eba4e4ebaab1f7e20a1921d |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2020-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-0b6450258eba4e4ebaab1f7e20a1921d2025-01-14T16:27:19ZengWileySpace Weather1542-73902020-03-01183n/an/a10.1029/2019SW002410Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation AlgorithmJianhui He0Xinan Yue1Huijun Le2Zhipeng Ren3Weixing Wan4Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics Chinese Academy of Sciences Beijing ChinaKey Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics Chinese Academy of Sciences Beijing ChinaKey Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics Chinese Academy of Sciences Beijing ChinaKey Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics Chinese Academy of Sciences Beijing ChinaKey Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics Chinese Academy of Sciences Beijing ChinaAbstract In this work, we evaluated the quasi‐realistic ionosphere forecasting capability by an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as the background model in the system. The slant total electron contents (TECs) from global International Global Navigation Satellite Systems Service ground‐based receivers and from the Constellation Observing System for Meteorology, Ionosphere and Climate are assimilated into the system, and the ionosphere is then predicted in advance during the quiet interval of 23 to 27 March 2010. The predicted ionosphere vertical TEC (VTEC) and the critical frequency foF2 are validated by the Massachusetts Institute of Technology VTEC and global ionosondes network, respectively. We found that the ionosphere forecast quality could be enhanced by optimizing the thermospheric neutral components via the EnKF method. The ionosphere electron density forecast accuracy can be improved by at least 10% for 24 hr. Furthermore, the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Global Ultraviolet Imager (TIMED/GUVI) [O/N2] observations are used to validate the predicted thermosphere [O/N2]. The validation shows that the [O/N2] optimized by EnKF has better agreement with the TIMED/GUVI observation. This study further demonstrates the validity of EnKF in enhancing the ionospheric forecast capability in addition to our previous observing system simulation experiments by He et al. (2019, https://doi.org/10.1029/2019JA026554).https://doi.org/10.1029/2019SW002410 |
| spellingShingle | Jianhui He Xinan Yue Huijun Le Zhipeng Ren Weixing Wan Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm Space Weather |
| title | Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm |
| title_full | Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm |
| title_fullStr | Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm |
| title_full_unstemmed | Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm |
| title_short | Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm |
| title_sort | evaluation on the quasi realistic ionospheric prediction using an ensemble kalman filter data assimilation algorithm |
| url | https://doi.org/10.1029/2019SW002410 |
| work_keys_str_mv | AT jianhuihe evaluationonthequasirealisticionosphericpredictionusinganensemblekalmanfilterdataassimilationalgorithm AT xinanyue evaluationonthequasirealisticionosphericpredictionusinganensemblekalmanfilterdataassimilationalgorithm AT huijunle evaluationonthequasirealisticionosphericpredictionusinganensemblekalmanfilterdataassimilationalgorithm AT zhipengren evaluationonthequasirealisticionosphericpredictionusinganensemblekalmanfilterdataassimilationalgorithm AT weixingwan evaluationonthequasirealisticionosphericpredictionusinganensemblekalmanfilterdataassimilationalgorithm |