The Global Analysis of the Ionospheric Correlation Time and Its Implications for Ionospheric Data Assimilation

Forsythe, Victoriya V.; Azeem, Irfan; Crowley, G.; Makarevich, R. A.; Wang, Cheng

doi:10.1029/2020rs007181

Cited by 10 publications

(17 citation statements)

References 27 publications

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“…However, the complexity of defining, understanding and balancing the errors to spread the correct information in space makes the process intricate. For instance, Forsythe and McDonald (2022) show that if horizontal gradients exist in the ionosphere, the ingestion of radio occultation STEC can introduce a fictitious F3‐layer in the analysis. Thus, sequentially assessing the impact of the different types of observations can highlight the areas to improve; here, ground GNSS data are the base starting point to improve as the scheme(s) mature.…”

Section: Experimental Runmentioning

confidence: 99%

Performance Analysis of a Strong Constraint 4DVar and 4DEnVar on Regional Ionosphere Imaging

Ssessanga,

Miloch,

Clausen

et al. 2023

Space Weather

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Data assimilation (DA) techniques have recently gained traction in the ionospheric community, particularly at regional operational centers where more precise data are becoming prevalent. At center stage is the argument over which technique or scheme merits realization. At 4DSpace, we have in‐house developed and assessed the performance of two regional flavors of short‐term forecast strong constraint four‐dimensional (4D, space and time) variational (SC4DVar) DA schemes; the orthodox incremental (SC4DVar‐Inc) and ensemble‐based (SC4DEnVar) approach. SC4DVar‐Inc is bottled‐necked by expensive Tangent Linear Models (TLMs) and model Ad‐joints (MAs), while SC4DEnVar design mitigates these limitations. Both schemes initialize from the same background (IRI‐2016), and electron densities forward propagated (30‐min) by a Gauss Markov filter‐ the densities take on a log‐normal distribution to assert the mandatory ionosphere density positive definiteness. Preliminary assimilation is performed only with ubiquitous Global Navigation Satellite System observables from ground‐based receivers, with a focus on moderately stable mid‐latitudes, specifically the Japanese archipelago and neighboring areas. Using a simulation analysis, we find that under model space localization, 30 member Ensembles are sufficient for regional SC4DEnVar. Verification of reconstructions is with independent observations from ground‐based ionosonde and satellite radio occultations: the performance of both schemes is fairly adequate during the quiet period when the background has a better estimation of the hmF2. SC4DVar‐Inc is slightly better over areas densely populated with measurements, but SC4DEnVar estimates the overall 3D ionosphere picture better, particularly in remote areas and during severe conditions. These results warrant SC4DEnVar as a better candidate for precise short‐time regional forecasts.

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Section: Experimental Runmentioning

confidence: 99%

Performance Analysis of a Strong Constraint 4DVar and 4DEnVar on Regional Ionosphere Imaging

Ssessanga,

Miloch,

Clausen

et al. 2023

Space Weather

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“…The one comparable study of ionospheric correlation time (Forsythe, Azeem, Crowley, Makarevich, & Wang, 2020) used TEC data rather than ionosonde‐provided electron density. They also defined the correlation time by when the correlation coefficient dropped below 0.8, whereas we use the time at which is drops below e −1 ≈ 0.368.…”

Section: Characteristics Of the Unresolved Variancementioning

confidence: 99%

“…They also defined the correlation time by when the correlation coefficient dropped below 0.8, whereas we use the time at which is drops below e −1 ≈ 0.368. Therefore, to translate the results from (Forsythe, Azeem, Crowley, Makarevich, & Wang, 2020) to ours, their results must be multiplied by −1/ln(0.8) ≈ 4.48. For mid‐latitudes they find times from 2 to 2.5 hr, which translate to between 9 and 11 hr in our system.…”

Section: Characteristics Of the Unresolved Variancementioning

confidence: 99%

“…The ISR profiles are fit with a Chapman profile in (Yue et al., 2007), so there is no information on small variations. The temporal correlation time is only studied in one prior published study (Forsythe, Azeem, Crowley, Makarevich, & Wang, 2020) to our knowledge. Lastly, the 2017 conference abstract (Holmes et al., 2017) presents the first simultaneous and consistent study of horizontal and temporal variations for the purpose of making a higher‐fidelity truth model for use in an OSSE.…”

Section: Introductionmentioning

confidence: 99%

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On Constructing a Realistic Truth Model Using Ionosonde Data for Observation System Simulation Experiments

Hughes¹,

Forsythe

Blay³

et al. 2022

Radio Science

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The ionosphere contains many small‐scale electron density variations that are under represented in smooth physics‐based or climatological models. This can negatively impact the results of Observation System Simulation Experiments, which use a truth model to simulate data. This paper addresses this problem by using ionosonde data to study ionospheric variability and build a new truth model with empirically driven variations. The variations are studied for their amplitude, horizontal and vertical size, and temporal extent. Results are presented for different local times, seasons, and at solar minimum and solar maximum. We find that these departures from a smooth background are often as large as 25% and are most prevalent near 250 km in altitude. They have horizontal spatial extents that vary from a few hundred to a few thousand kilometers, and typically have the largest horizontal extent at high altitudes. Their vertical extents follow the same pattern of being larger at high altitudes, but they only vary from 10s of km up to 200 km in vertical size. Temporally, these variations can last for a few hours. A procedure for using these spatial and temporal distributions to add empirically driven variance to a smooth truth model is outlined. This process is used to make a truth model with representative variations, which is compared to ionosonde data as well as Global Positioning System Total Electron Content data that was not used to inform the model. The new model resembles the data much better than the smooth models traditionally used.

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“…where Δt is the time difference between t n+1 and t n , and τ is the correlation time (Forsythe, Azeem, Crowley, Makarevich, et al, 2020). The correlation time controls to what degree the state projected forward in time depends on the background model and analysis density.…”

Section: Data Assimilationmentioning

confidence: 99%

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

et al. 2021

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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.

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The Global Analysis of the Ionospheric Correlation Time and Its Implications for Ionospheric Data Assimilation

Cited by 10 publications

References 27 publications

Performance Analysis of a Strong Constraint 4DVar and 4DEnVar on Regional Ionosphere Imaging

Performance Analysis of a Strong Constraint 4DVar and 4DEnVar on Regional Ionosphere Imaging

On Constructing a Realistic Truth Model Using Ionosonde Data for Observation System Simulation Experiments

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

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