Evaluation of the New Background Covariance Model for the Ionospheric Data Assimilation. Issue 8 (17th August 2021)

• Record Type: Journal Article
• Authors: Forsythe, Victoriya V.; Azeem, Irfan; Blay, Ryan; Crowley, Geoff; Gasperini, Federico; Hughes, Joe; Makarevich, Roman A.; Wu, Wanli
• Is Part Of: Radio science. Volume 56:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-17
• Subjects: ionospheric data assimilation -- horizontal covariance model -- background error covariance -- vertical covariance model -- relaxation time -- assimilation of GPS TEC; Radio meteorology -- Periodicals; Radio wave propagation -- Periodicals; 621.38405
• DOI: 10.1029/2021RS007286
• Languages: English
• ISSNs: 0048-6604

Abstract

Abstract: 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. Key Points: The influence of the recently developed vertical and horizontal correlation model on the performance of Ionospheric Data Assimilation Four‐Dimensional is investigated The tomographic issue occurs at the grid cells where the rays with slant total electron content measurements intersect The vertical component of the background error covariance matrix plays the most important role in solving the above tomographic issue