Data Assimilation Retrieval of Electron Density Profiles From Ionosonde Virtual Height Data

Abstract

In the ionosphere the electromagnetic wave has to pass through the ionized medium which decreases the group velocity of the radio pulses, making the propagation speed less than the free-space speed of light c. This makes the VH h′ greater than the true height h. A certain procedure, known as true height analysis, needs to be applied in order to recreate the true height electron density profile N e (h) from the VH data.Two of the most commonly used true height inversion algorithms are POLynomial ANalysis (POLAN) (Titheridge, 1988) and NhPC (Reinisch et al., 2005;Reinisch & Xueqin, 1983), with the latter being a component of the Automatic Real-Time Ionogram Scaler with True height (ARTIST) software that is widely used in the ionospheric research community. Both algorithms determine the true height using polynomial analysis. POLAN expresses each ionospheric layer in terms of sixth-order overlapping polynomials, whereas the NhPC algorithm describes the layers in terms of shifted Chebyshev polynomials. Both algorithms model the transition in the valley region between E and F layers. Additionally, some ionosonde operators employ the Autoscala algorithm (Pezzopane et al., 2010) which is based on image recognition and parametrization of empirical curves. This paper describes a new method for the electron density profile retrieval from VH measurements using data assimilation. Data assimilation methods incorporate observations into models, providing a description of the system that is optimally consistent with both the model and data. The components that are necessary to build a data assimilation model include a background model, the observations, the observation operator that maps the observations to the model state, and an optimization method that gives an optimal analysis by incorporating the observations into the model. Numerous ionospheric data assimilation methods 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. FORSYTHE ET AL.

Assimilating Space‐Based Thermospheric Neutral Density (TND) Data Into the TIE‐GCM Coupled Model During Periods With Low and High Solar Activity

“…There have been other studies that indirectly specified thermosphere properties (such as neutral winds, mass density, and temperature) by assimilating the ionospheric measurements into coupled models (e.g., Aa et al, 2016;G. Bust & Immel, 2020; G. S. Bust et al, 2004;Chen et al, 2016;Datta-Barua et al, 2013;Forsythe, Azeem, Blay, Crowley, Gasperini, et al, 2021;Forsythe, Azeem, Blay, Crowley, Makarevich, & Wu, 2021;He et al, 2020;Kodikara et al, 2021;Lee et al, 2012;Lin et al, 2015;N. M. Pedatella et al, 2020).…”

“…Considerable efforts have already been taken to improve the accuracy of physics‐based models by implementing DA for the thermospheric temperature (e.g., Cantrall et al., 2019; Ruan et al., 2018), Thermospheric Neutral Density (TND, e.g., Forootan et al., 2020, 2022; Sutton, 2018; Weimer et al., 2020), neutral compositions (e.g., M. V. Codrescu et al., 2004; P. M. Mehta et al., 2019; J. Yue et al., 2019), and wind patterns (e.g., Cierpik et al., 2003; Lomidze et al., 2015). There have been other studies that indirectly specified thermosphere properties (such as neutral winds, mass density, and temperature) by assimilating the ionospheric measurements into coupled models (e.g., Aa et al., 2016; G. Bust & Immel, 2020; G. S. Bust et al., 2004; Chen et al., 2016; Datta‐Barua et al., 2013; Forsythe, Azeem, Blay, Crowley, Gasperini, et al., 2021; Forsythe, Azeem, Blay, Crowley, Makarevich, & Wu, 2021; He et al., 2020; Kodikara et al., 2021; Lee et al., 2012; Lin et al., 2015; N. M. Pedatella et al., 2020). In most of these studies, the physics‐based general circulation models are used because they are suitable for understanding the complex behavior of terrestrial atmosphere in different spatial and temporal scales (e.g., Emmert, 2015; Fesen et al., 2002; Huba & Sazykin, 2014; P. Mehta & Linares, 2017; Qian & Solomon, 2012; Sutton, 2018).…”

Ionosound Data Analysis for Precise Study of Ionospheric Electron Density

“…The vertical-incidence ionosonde stands as a pinnacle of advanced technology, offering a sophisticated radar system tailored to penetrate and explore the ionosphere directly above it (Forsythe et al, 2021). Emitting concise bursts of radio energy vertically, this innovative apparatus meticulously analyzes the reflected signals to decipher the ionosphere's intricate characteristics.…”

Improving the Thermosphere Ionosphere in a Whole Atmosphere Model by Assimilating GOLD Disk Temperatures

“…There is a long‐history of lower atmosphere data assimilation (Gelaro et al., 2017; Hersbach et al., 2020; Rienecker et al., 2011), but the whole atmosphere system data assimilation is relatively new. There have been significant developments in the assimilation of thermosphere‐ionosphere observations such as, neutral density (Matsuo et al., 2013; Mehta et al., 2018; M. V. Codrescu et al., 2004; Ren & Lei, 2020; S. M. Codrescu et al., 2018; Sutton, 2018), thermospheric temperature (Laskar, Pedatella, et al., 2021), thermospheric airglow radiance (Cantrall et al., 2019), and electron content (Aa et al., 2016; Bust et al., 2004; Bust & Immel, 2020; Chen et al., 2016; Datta‐Barua et al., 2013; Forsythe et al., 2021; He et al., 2020; Kodikara et al., 2021; Lee et al., 2012; Lin et al., 2015; Matsuo et al., 2013; Pedatella et al., 2020; Song et al., 2021). While these results were promising and showed that the assimilation of TI observations improves the model states, most were limited to using upper atmosphere only models or used limited thermospheric datasets from low‐earth‐orbit satellites or ionospheric only measurements or observing system simulation experiments.…”