Meghan Burleigh

This paper introduces the integration of Jason total electron content (TEC) data into PyVALION (Python VALidation for IONosphere), an open‐source community tool for ionospheric model validation. PyVALION applies a novel data assimilation (DA)‐based geometry matrix to improve validation adaptability, computational efficiency, and residual analysis. The updated version incorporates TEC measurements from the Jason‐2 and Jason‐3 satellite altimeter missions (2008–2026). It also automates data downloading, quality control, noise filtering, and bias correction, ensuring high‐accuracy TEC for validation purposes. Jason missions provide vertical TEC (vTEC) measurements over the open ocean, where there are limited data from other ionospheric data sources, such as ground‐based global navigation satellite system (GNSS) receivers, ionosondes, and incoherent scatter radars (ISRs). This addition delivers a valuable open‐access validation source with consistent coverage in areas where many ionospheric models lack ionospheric observation data. New capabilities are demonstrated through validation of ANCHOR, an advanced parameterized ionospheric DA model developed at the U.S. Naval Research Laboratory. Using Jason TEC, the performance of ANCHOR in oceanic regions is assessed and compared with that of its climatological background model, the Python International Reference Ionosphere (PyIRI). Results show ANCHOR reduces residual bias, spread, and skewness relative to PyIRI during moderate and high solar activity, with RMSE reductions in three of four events and improvements exceeding 50% in the high‐activity case, primarily during daytime conditions. Overall, this work extends PyVALION's validation reach to the open ocean and offers the research community a standardized, flexible platform for model assessment.

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.

ANCHOR is a novel data assimilation (DA) algorithm developed at the U.S. Naval Research Laboratory to improve ionospheric nowcasting by increasing accuracy and decreasing computational cost. As a parameterized DA model, ANCHOR represents the ionosphere using physical parameters such as the F2 layer peak (F2), which characterizes a region's maximum electron density. These parameters are derived from various data sources and assimilated to generate two‐dimensional ionospheric state estimates. Originally, ANCHOR utilized data from ionosondes, radio occultation (RO) electron density profiles, and slant Total Electron Content (sTEC) from ground‐based Global Navigation Satellite System (GNSS) stations. This work extends the ground‐based approach by incorporating GNSS reflectometry (GNSS‐R) data; GNSS signals reflected off Earth's surface, captured by low Earth orbit satellites, to estimate sTEC in data‐sparse regions. GNSS‐R sTEC measurements along combined incident and reflected raypaths are used to extract ionospheric parameters, refining model F2 estimates to better match the true ionospheric state. F2 is extracted analytically along each ray path using a background model (PyIRI) and total sTEC. Modeled sTEC is used to estimate a weighting factor that apportions the total sTEC between the incident and reflected paths. An observational system simulation experiment (OSSE) demonstrates the reduction of global F2 root mean square error following assimilation. Results show a 36.8% linear reduction with a single day of GNSS‐R data (140 individual reflection events) with up to 47.3% reduction when a full week (1,018 events) is assimilated. These results highlight the utility of GNSS‐R data for enhancing ionospheric state estimation within a DA framework.

Observations of neutral structuring in the cusp region have been obtained in several satellite and rocket missions. Observations include altitude‐dependent neutral density enhancements (CHAllenging Minisatellite Payload and Gravity Recovery And Climate Experiment satellites) and slight neutral density depletion at lower altitudes (Streak satellite and Rocket Experiment for Neutral Upwelling 2 (RENU2) mission). Soft electron precipitation (< a few hundred eV), ion upwelling, and ion‐neutral momentum exchange are among the processes theorized to drive neutral enhancement. We investigate these processes as they relate to neutral structuring using a high‐resolution ionosphere‐thermosphere numerical model (Alfvén wave scale) that includes inertial ion and neutral terms. A detailed study of a soft electron precipitation event is performed, and the resulting thermospheric response is assessed. Ion‐neutral coupling is found to span vertically for hundreds of kilometers for 15–20 min, enhancing the neutral density at higher altitudes. A slight depletion is found near the F region, driven by divergent ion flow near the region of the most intense electron heating. Temporal effects include an upward traveling compression wave and high‐altitude gravity waves. Thermospheric structuring occurs on a 10‐min time scale.

Understanding the formation, progression, and global impact of Large Scale (LS) Traveling Ionospheric Disturbances (TIDs) is a long‐standing challenge in global space weather research. At high latitudes, the magnetosphere injects significant energy into the Ionosphere‐Thermosphere (I‐T) system through Joule heating, auroral particle heating, and ion drag. Traveling Atmospheric Disturbances (TADs) are a commonly observed thermospheric response to magnetospheric energy entering the I‐T system. These atmospheric waves are believed to drive a similar wave response in the ionosphere, known as TIDs. Large‐Scale Traveling Ionospheric Disturbances (LSTIDs) commonly travel from the equatorward edge of the auroral oval into the opposite hemisphere. This study examines the impact of LSTIDs on the topside equatorial ionosphere using a combination of observational and modeling methods. The geomagnetic storm that occurred on 25–26 March 2014 is used as a case study, because it contains a conjunction between two satellites that permits an analysis of the altitudinal differences in LSTIDs. The variations seen in the observations are then explored using I‐T model runs, which allow for a detailed analysis of the LSTID variations and their drivers. The model results qualitatively agree with the satellite observations, and demonstrate that it is possible for LSTIDs to reach the topside ionosphere at low latitudes through variations in the field‐aligned plasma drifts that are caused by the field‐aligned component of the neutral wind modifying the plasma density peak heights, resulting in conjugate differences that affect the field‐aligned plasma pressure gradient.

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.

Ionospheric outflow supplies nearly all of the heavy ions observed within the magnetosphere, as well as a significant fraction of the proton density. While much is known about upflow and outflow energization processes, the full global pattern of outflow and its evolution is only known statistically or through numerical modeling. Because of the dominant role of heavy ions in several key physical processes, this unknown nature of the full outflow pattern leads to significant uncertainty in understanding geospace dynamics, especially surrounding storm intervals. That is, global models risk not accurately reproducing the main features of intense space storms because the amount of ionospheric outflow is poorly specified and thus magnetospheric composition and mass loading could be ill‐defined. This study defines a potential mission to observe ionospheric outflow from several platforms, allowing for a reasonable and sufficient reconstruction of the full outflow pattern on an orbital cadence. An observing system simulation experiment is conducted, revealing that four well‐placed satellites are sufficient for reasonably accurate outflow reconstructions. The science scope of this mission could include the following: reveal the global structure of ionospheric outflow; relate outflow patterns to geomagnetic activity level; and determine the spatial and temporal nature of outflow composition. The science objectives could be focused to be achieved with minimal instrumentation (only a low‐energy ion spectrometer to obtain outflow reconstructions) or with a larger scientific scope by including contextual instrumentation.

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.

Ionospheric outflow supplies nearly all of the heavy ions observed within the magnetosphere, as well as a significant fraction of the proton density. While much is known about upflow and outflow energization processes, the full global pattern of outflow and its evolution is only known statistically or through numerical modeling. Because of the dominant role of heavy ions in several key physical processes, this unknown nature of the full outflow pattern leads to significant uncertainty in understanding geospace dynamics, especially surrounding storm intervals. That is, global models risk not accurately reproducing the main features of intense space storms because the amount of ionospheric outflow is poorly specified and thus magnetospheric composition and mass loading could be ill-defined. This study defines a potential mission to observe ionospheric outflow from several platforms, allowing for a reasonable and sufficient reconstruction of the full outflow pattern on an orbital cadence. An observing system simulation experiment is conducted, revealing that four well-placed satellites are sufficient for reasonably accurate outflow reconstructions. The science scope of this mission could include the following: reveal the global structure of ionospheric outflow; relate outflow patterns to geomagnetic activity level; and determine the spatial and temporal nature of outflow composition. The science objectives could be focused to be achieved with minimal instrumentation (only a low-energy ion spectrometer to obtain outflow reconstructions) or with a larger scientific scope by including contextual instrumentation. Note that the upcoming Geospace Dynamics Constellation mission will observe upwelling but not ionospheric outflow.