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Evaluation of the New Background Covariance Model for the Ionospheric Data Assimilation
Author(s) -
Forsythe Victoriya V.,
Azeem Irfan,
Blay Ryan,
Crowley Geoff,
Gasperini Federico,
Hughes Joe,
Makarevich Roman A.,
Wu Wanli
Publication year - 2021
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2021rs007286
Subject(s) - data assimilation , covariance , thermosphere , ionosphere , meteorology , atmospheric sciences , environmental science , remote sensing , computer science , algorithm , geophysics , mathematics , physics , geology , statistics
This paper presents the evaluation of the recently developed covariance model for the Ionospheric Data Assimilation Four‐Dimensional (IDA4D) technique. The ionospheric data are generated using the Observation System Simulation Experiment Tool from the known ionospheric state produced by the physics‐based Thermosphere‐Ionosphere‐Mesosphere Electrodynamics General Circulation Model. Several experiments are conducted to assess performance of IDA4D with data‐driven vertical and horizontal covariance matrices. We show that the vertical part of the covariance model plays the most important role because it preserves the vertical structure of the F‐region density layer and helps to correct a tomographic issue that arises when the slant total electron content is assimilated along the intersecting rays. The results show that the new covariance model improves the fidelity of IDA4D algorithm, making it more suitable for the regional assimilation with dense ground‐based Global Positioning System data coverage.