Dustin A. Hickey

Oblique high‐frequency (HF) ionospheric measurements contain valuable information about electron density structure, but remain difficult to assimilate operationally due to the computational cost of ray tracing. PyRayHF is an open‐source, fully Python‐based framework that addresses such difficulties through the implementation of efficient forward operators for HF propagation and parameter retrieval tailored to data‐assimilation (DA) applications. PyRayHF implements magneto‐ionic vertical tracing and a hierarchy of two‐dimensional oblique ray tracing methods, which are verified against one another for internal consistency and validated through comparative analysis with a full three‐dimensional Jones‐Stephenson ray tracer. Validation results show that all solvers reproduce key observables, including group path and apex location, with typical errors below 10% when compared to the reference solution. For group path, the primary observable in oblique ionogram interpretation, simple stratified Snell's‐law formulations perform comparably to gradient‐based solvers while being several orders of magnitude faster, making them suitable for DA workflows. To enable efficient assimilation, PyRayHF combines a fast vertical forward operator with a midpoint‐based oblique‐to‐vertical transformation. For transmitter–receiver separations of 1,000–2,000 km, midpoint‐derived errors remain below 10%. PyRayHF further provides a parameter‐minimization framework that retrieves F2‐layer parameters directly from midpoint‐converted observations, producing anchor‐point measurements suitable for direct assimilation into parametrized systems such as ANCHOR.

Accurate specification of the ionosphere is essential for mitigating the impacts of space weather on communication, navigation, and surveillance systems. ANCHOR is a newly developed ionospheric data assimilation (DA) model from the U.S. Naval Research Laboratory that fuses measurements from ionosondes, radio occultation, and Global Navigation Satellite System (GNSS) receivers into a PyIRI‐based background via a Kalman filter. To support model validation in both reproduction and standardization, PyVALION (Python VALidation for IONosphere) is introduced—a community‐oriented tool built on a forward‐operator framework that projects model output into observation space for statistically robust comparison. Currently, PyVALION directly supports validation of models producing F2, F2, and thickness parameters and , with extension to other parameters for future releases. ANCHOR's performance is assessed using electron density parameters (F2 and F2) from the Global Ionospheric Radio Observatory (GIRO) ionosonde network. Four multi‐day geophysical events are analyzed to evaluate model behavior under varying solar and geomagnetic conditions. Results show that ANCHOR significantly reduces both bias and root mean square error (RMSE) relative to the PyIRI background. Separating day and night conditions reveals that background bias is larger during daytime, and data assimilation proves particularly effective at correcting this bias when it is most significant. A controlled synthetic experiment confirms that forward‐operator errors in PyVALION are negligible compared to observed residuals, validating its use as a precision‐grade evaluation tool. These findings establish PyVALION as a reproducible and scalable framework for ionospheric model assessment and confirm ANCHOR's capability to enhance real‐time ionospheric specification.

ANCHOR is a novel assimilative model developed at the U.S. Naval Research Laboratory, which was designed for rapid assimilative runs. ANCHOR uses recently developed PyIRI model for the background and for the formation of the background covariance matrix. It only takes a few minutes for ANCHOR to complete the data assimilation (DA) for one day, including data pre‐processing and model set up. ANCHOR extracts ionospheric parameters from radio occultation (RO) and ionosonde data using PyIRI formalism and assimilates them as point measurements into maps of the background parameters using a Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment with synthetic data simulated using the SAMI3 model. ANCHOR reduces the root mean square errors in the analysis by more than a half for all of the ionospheric parameters in comparison to the background. Finally, this paper discusses advantages and limitations of the parametrized ionospheric DA, highlighting the avenues for its future improvement.

A novel model called PyIRI was recently developed. It offers a fully Python alternative to the widely used FORTRAN International Reference Ionosphere (IRI) model to construct the ionospheric electron density for the entire day and on the entire global grid in one computation, which has a significantly lower computational overhead. PyIRI introduced a novel approach to the computation of the global and diurnal functions and their matrix multiplication with Consultative Committee on International Radio (CCIR) coefficients, that enabled this global approach for the density specification. Since the IRI-based Real-Time Assimilative Model (IRTAM) produces coefficients in a similar format as CCIR coefficients, the PyIRI software was extended to be able to work with IRTAM coefficients using the same computationally efficient approach. This technical note describes the PyIRTAM software and shows examples of how to use it. PyIRTAM is 21 times more efficient than the classical IRTAM. PyIRTAM tool is made publicly available.

ANCHOR is a novel assimilative model developed at the U.S. Naval Research Laboratory. It extracts ionospheric parameters from RO and ionosonde data and assimilates them as point measurements into the maps of the background parameters using Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment.

Whitepaper #196 in the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033. Main topics: basic research; infrastructure/workforce/other programmatic. Additional topics: research tools and infrastructure; ground-based missions/projects; system science; […]

While low and high‐latitude ionospheric scintillation have been extensively reported, significantly less information is available about the properties of and conditions leading to mid‐latitude scintillations. Here, we report and discuss scintillation observations made in the Southern United States (UT Dallas, 32.99°N, 96.76°W, 43.2°N dip latitude) on June 1st, 2013. The measurements were made by a specialized dual‐frequency GPS‐based scintillation monitor which allowed us to determine main properties of this mid‐latitude scintillation event. Additionally, simultaneous airglow observations and ionospheric total electron content (TEC) maps provided insight on the conditions leading to observed scintillations. Moderate amplitude scintillations (S₄>∼0.4) occurred in both L1 and L2C signals, and severe (S₄ > ∼0.8) events occurred in L2C signals at low (ϕ values exceeding 0.5 radians in L2C. We also show that the observed phase scintillation magnitudes increased with amplitude scintillation severity. Decorrelation times were mostly between 0.25 and 1.25 s, with mean value around 0.65 s for both L1 and L2C. Frequency scaling of S₄ matched fairly well the predictions of weak scattering theory but held for observations of moderate and strong amplitude scintillation as well. Scintillation occurred during the main phase of a modest magnetic storm that, nevertheless, prompted an extreme equatorward movement of the mid‐latitude trough and large background TEC enhancements over the US. Scintillations, however, occurred within TEC and airglow depletions observed over Texas. Finally, scintillation properties including severity and rapidity, and associated TEC signatures are comparable to those associated with equatorial spread F.

The SAMI3/ESF (Sami3 is also a model of the ionosphere/equatorial spread $F$) code is used to simulate the growth of equatorial plasma bubbles in the presence of a background wind field based on measured winds. The measured winds exhibit the well-known ‘midnight temperature maximum’ (MTM) pattern, in which an equatorward wind occurs simultaneously with a cessation in the zonal wind. The MTM is often preceded by strong equatorward winds (about 100 m/s). The circumstance where the MTM winds are symmetric across the equator is considered; here the meridional wind component in the southern hemisphere is the reverse of the northern meridional wind. The timing of the wind pattern relative to the imposition of a seed for the ESF instability is explored. We find that the simultaneous occurrence of a seed wave and a strong converging meridional wind pattern can produce post-midnight ESF. We further find that the seed wave and the sudden cessation of the zonal winds can also produce post-midnight ESF. The MENTAT (Magnetic mEridional NeuTrAl Thermospheric) code verifies the occurrence of converging meridional wind patterns such as those simulated here, based on ionosonde data. Results suggest that regional-scale wind measurements would aid in the prediction signal-disrupting ionospheric bubbles.