Douglas P. Drob

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.

This paper presents a major update to the PyIRI model, a fully vectorized Python implementation of the International Reference Ionosphere (IRI). The update reformulates the IRI core coefficients using spherical harmonics in Quasi‐Dipole (QD) latitude and Magnetic Local Time coordinates, replacing the legacy geographic‐coordinate‐based basis functions of the CCIR (Consultative Committee on International Radio) and URSI (International Union of Radio Science) models of the fo F2 critical plasma frequency parameter. The resulting coefficients remain valid over decadal timescales, with re‐derivation recommended every 5 years to account for magnetic pole drift. This new formulation removes the dependence on the modified dip angle, enabling a more transparent and physically interpretable representation of ionospheric climatology. Diurnal variability is expressed through real Fourier series, with the optimal number of spatial and temporal coefficients determined via reconstruction error analysis. The coefficients are stored in standardized NetCDF format and validated against IRI‐2020. Artificial small‐scale oscillations present in the original IRI climatology are substantially reduced in the new formulation, leading to an ionosphere more consistent with the SAMI physics‐based model. The updated PyIRI model also includes previously unavailable IRI parameters, such as the SHU‐2015 and AMTB2013 options for hm F2, and the B0 and B1 F2‐layer thickness and shape parameters. Spherical harmonic coefficients are extracted identically across the fo F2 CCIR, fo F2 URSI, hm F2 SHU‐2015, hm F2 AMTB2013, M(3000)F2, and foEs parameters.

We present a novel method for forecasting key ionospheric parameters using transformer‐based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2‐layer peak plasma frequency (foF2), the F2‐layer peak density height (hmF2), and total electron content for a given geographic location. It supports a number of exogenous variables, including F10.7 cm solar flux and disturbance storm time (Dst). We demonstrate how transformers can be trained in a data assimilation‐like fashion that uses these exogenous variables along with naïve predictions from climatology to generate 24‐hr forecasts with nonparametric uncertainty bounds. We call this method the Local Ionospheric Forecast Transformer. We demonstrate that the trained model can generalize to new geographic locations and time periods not seen during training, and we compare its performance to that of the International Reference Ionosphere using CCIR coefficients.

The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC‐1 and COSMIC‐2) missions provide global observations of atmospheric and ionospheric conditions using Global Navigation Satellite System (GNSS) radio occultation (RO). While distributing satellites across different orbital inclinations ensures broad spatial coverage, it does not inherently guarantee uniform improvements in electron density estimation across all data assimilation (DA) methods. This study evaluates the impact of various RO constellation configurations on ionospheric space weather nowcasting using ANCHOR, a DA model developed at the Naval Research Laboratory. Observing system simulation experiments (OSSEs) quantify global enhancements in ionospheric state estimation and identify the optimal number of satellites before diminishing returns are observed. These findings are critical for agencies such as National Oceanic and Atmospheric Administration and the Department of Defense, informing the design of future RO missions to improve space weather monitoring and operational forecasting capabilities.

A novel model called PyIRI was recently developed. It constructs the ionospheric electron density for the entire day and on the entire global grid in one computation, which has a very low 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 or the International Union of Radio Science (URSI) coefficients, that enabled this global approach for the density specification. Since the International Reference Ionosphere‐based Real‐Time Assimilative Model (IRTAM) produces coefficients in a similar format as CCIR/URSI coefficients, the PyIRI computational approach was extended to work with IRTAM coefficients. This technical note describes the PyIRTAM software and provides usage examples. The PyIRTAM tool is made publicly available through PyPI and GitHub.

Upward propagating waves of lower atmospheric origin play an important role in coupling terrestrial weather with space weather on daily to inter‐annual timescales. Quantifying their short‐term (<30 days) variability is a difficult challenge because simultaneous observations at multiple local times are needed to sample diurnal cycles. This study demonstrates and validates a short‐term estimation method of the DE3 non‐migrating tide at the equator and then applies the technique to three independent data sets: MIGHTI, SABER, and TIDI. We find that daily DE3 estimates from SABER, MIGHTI, and TIDI at equator agree well with correlation coefficients ranging between 0.76 and 0.85. The daily DE3 amplitude variability is typically ∼7 m/s in zonal winds and ∼3 K in temperature. We also find that daily MLT variations and F‐region ionospheric DE3 from COSMIC‐2 Global Ionospheric Specification (GIS) show a correlation of 0.55–0.65, suggesting that not all ionospheric variability can be attributed to the E‐region dynamo; however, increasing correlation with increasing time‐scale suggests that lower atmospheric variability has pronounced impact on the ionosphere on intra‐seasonal scales. We find that the MLT and the F‐region ionosphere exhibit strong coherent intra‐seasonal oscillations (residual amplitudes upto 50%–60%); their coherency with the MJO in 2020 suggests a possible modulation of the upward propagating DE3 tide related to this major tropical tropospheric weather pattern. In addition, we find stratospheric QBO signatures in the MLT DE3 on inter‐annual scales. This study offers fresh observational insights into the pivotal role of tropospheric weather in shaping variability in the coupled thermosphere‐ionosphere system.

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 Python module called PyIRTAM was developed in addition to the previously released PyIRI model. The PyIRI computational approach was extended to work with Real-Time Assimilative Model (IRTAM) coefficients. This enables global approach to the density specification, utilizing computation of the global and diurnal functions and their multiplication with coefficients in the matrix form. PyIRTAM tool is made publicly available through PyPI and GitHub.

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.