Abstract
Utilising data assimilation (DA) techniques to improve the background estimates of a physics-based model has become increasingly common among the upper atmospheric modelling community. However, pragmatic implementations of the DA methods such as the Ensemble Kalman Filter (EnKF) and its variants often assume independent observations mainly for the ease of computation. As a result, the observation error covariance matrix R is diagonal, which results in filter performance being sub-optimal when the potentially correlated observation error statistics are neglected. In this work, we implement iterative estimation of the observation covariance matrix using the statistical averages of background and analysis innovations within the local ensemble Transform Kalman Filter (LETKF) algorithm of the Advanced Ensemble electron density (Ne) Assimilation System (AENeAS) to enhance its ionospheric state estimation and forecast capabilities. The total electron content (TEC) estimates of the developed model are statistically benchmarked against its baseline diagonal R counterpart, GPS TEC data, TIE-GCM, and NeQuick models. The results from the analysis show that taking into account the impact of correlated observation errors helps improve the estimation of the TEC and overall ionosphere/thermosphere structure.
Original language | English |
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Publication status | Published - 29 Oct 2021 |
Event | 17th European Space Weather Week - Technology Innovation Centre, Glasgow, United Kingdom Duration: 25 Oct 2021 → 29 Oct 2021 http://esww17.iopconfs.org/home |
Conference
Conference | 17th European Space Weather Week |
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Abbreviated title | ESWW17 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 25/10/21 → 29/10/21 |
Internet address |