TitleEvaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm
Publication TypeJournal Article
Year of Publication2020
AuthorsHe, J, Yue, X, Le, H, Ren, Z, Wan, W
JournalSpace Weather
Volume18
Issue3
Date Published02/2020
ISSN1542-7390
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).

URLhttps://onlinelibrary.wiley.com/doi/abs/10.1029/2019SW002410
DOI10.1029/2019SW002410
Short TitleSpace Weather


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