Data Assimilation Retrieval of Electron Density Profiles From Ionosonde Virtual Height Data. Issue 5 (29th April 2021)

• Record Type: Journal Article
• Authors: Forsythe, Victoriya V.; Azeem, Irfan; Blay, Ryan; Crowley, Geoff; Makarevich, Roman A.; Wu, Wanli
• Is Part Of: Radio science. Volume 56:Issue 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-04-29
• Subjects: assimilative inversion -- electron density retrieval -- Kalman filter -- new virtual height inversion technique -- sequential and simultaneous data ingestion -- virtual height Jacobian matrix; Radio meteorology -- Periodicals; Radio wave propagation -- Periodicals; 621.38405
• DOI: 10.1029/2021RS007264
• Languages: English
• ISSNs: 0048-6604

Abstract

Abstract: A new method is developed to retrieve electron density profiles from a raw virtual height ionosonde traces. A Kalman filter is used for the assimilative inversion scheme together with the newly developed data‐driven vertical covariance model. The detailed mathematical formalism for the derivation of the Jacobian that takes into account the effect of the magnetic field is presented. The incoherent scatter radar measurements from Arecibo observatory are employed as the known truth to simulate the virtual height data. The results show that the data assimilative inversion technique accurately retrieves the vertical structure of the ionospheric density at the bottom side of the profile and reconstructs the vertical and temporal small‐scale density variations. A comparison with the results obtained by the POLynomial ANalysis (POLAN) inversion algorithm is presented. The assimilative inversion systematically outperforms the accuracy of the POLAN algorithm, on average reducing the percent errors in the electron density by half. Additionally, the simultaneous data ingestion is compared to the sequential assimilation of the virtual height data. Key Points: New method to retrieve the bottom side electron density from virtual height data is developed based on Kalman filter equations The derivation of the Jacobian for virtual height data assimilation is presented The assimilative inversion of virtual height data outperforms the polynomial analysis inversion algorithm