Sarah E. McDonald

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.

ANCHOR is a novel data assimilation model developed at the U.S. Naval Research Laboratory for nowcasting ionospheric parameters relevant to space weather applications. ANCHOR incorporates electron density observations from ionosondes, Abel inverted radio occultation (RO) data, and ground‐based GNSS receiver data into a PyIRI‐driven model background using the Kalman filter technique. The purpose of this study is to validate the estimated model parameters with parameters derived from electron density observations from incoherent scatter radars (ISR) at various levels of solar activity. Four distinct events were identified from a 6‐year data set spanning from 2018 to 2024 collected from four operating ISRs located at varying latitudes west of the prime meridian: Arecibo, Jicamarca, Millstone Hill, and Poker Flat. These events span a range of solar activity levels, with two events at low solar activity, one at moderate and one at high solar activity, each with data coverage from at least two radars. Parameter extraction is achieved by fitting Epstein functions to the electron density profiles, where the peak density ( NmF2 ), peak altitude ( hmF2 ), and the bottomside and topside thickness parameters are simultaneously optimized to characterize the F2 layer. The ISR‐extracted parameters are used to directly compare with the model outputs using the root mean square error (RMSE) analysis method. Up to 75% improvement relative to the background model for F2, F2, and thickness parameters with consistency across all latitudes is found. Additionally, the ANCHOR assimilative model was compared to PyIRTAM model, showing a good agreement between the performances of both systems.

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.

This study investigates the relative significance of gravity wave and gravity dynamo effects in large‐scale wave structure (LSWS) development using the coupled Sami3 is Also a Model of the Ionosphere (SAMI3) and Specified Dynamics Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (SD‐WACCM‐X). Simulations show significant vertical E × B drift perturbations associated with gravity waves in the F region after ∼1700 LT, leading to LSWS near midnight. Notably, LSWS can occur independently of gravity‐driven dynamo current, emphasizing the significance of the gravity wave wind dynamo mechanism. However, LSWS exhibits more pronounced vertical E × B drift perturbations, indicating the involvement of background wind fields. Both gravity wave and background wind dynamo effects cause LSWS to grow vertically by ∼20 km and extend to ±10° in latitude. Gravity‐driven Pedersen current, therefore, plays a role in amplifying the upwelling growth and equatorial plasma bubble development. Furthermore, simulations demonstrate the emergence of predawn ionospheric irregularities in the bottomside F layer, even without gravity‐driven currents, attributed to concentric gravity waves over the magnetic equator. A comparison between FORMOSAT‐7/COSMIC2 and SAMI3 ion density is also conducted. These findings emphasize the significant influence of gravity waves and background wind fields on the formation of LSWS and irregularities.

ANCHOR is a novel data assimilation model developed at the U.S. Naval Research Laboratory for nowcasting ionospheric parameters relevant to space weather applications. ANCHOR incorporates electron density observations from ionosondes, Abel inverted radio occultation data, and ground-based GNSS receivers data into a PyIRI driven model background using the Kalman filter technique. The purpose of this study is to validate the estimated model parameters with direct electron density observations from incoherent scatter radars (ISR) at various levels of solar activity. A six year dataset spanning from 2018 to 2024, has been collected from four operating ISRs located at varying latitudes left of the prime meridian: Arecibo, Jicamarca, Millstone Hill, and Poker Flat. The validation includes four distinct events, with two events at low solar activity, one at moderate, and one at high solar activity, each with data coverage from at least two radars. Parameter extraction is achieved using Epstein functions to derive the bottom and topside of the F2 layer after the peak density (NmF2) and altitude (hmF2) have been found. The ISR-extracted parameters are used to directly compare with the model outputs using the root mean square error (RMSE) analysis method. Up to 75% improvement relative to the background model for NmF2 and hmF2 parameters with consistency across all latitudes is found. Additionally, ANCHOR assimilative model was compared to PyIRTAM model, showing a good agreement between the performances of both systems.

During geomagnetic storms, the capabilities of current climate models in predicting ionospheric behavior are notably limited. A data assimilation tool, Estimating Model Parameters Reverse Engineering (EMPIRE), implements a Kalman filter to ingest electric density rate correcting the background electric potential and neutral wind. For the baseline setup, or case (1), EMPIRE ingests electron density global map output from the Ionospheric Data Assimilation 4‐Dimensional (IDA4D) algorithm. In this work, a new augmentation method is evaluated in which ion drift measurements are also assimilated into EMPIRE. The ion drift measurements used in the new augmentation method are obtained from Super Dual Auroral Radar Network (SuperDARN) sites in the mid‐to‐high latitude region of the northern hemisphere. Cases (2) and (3) are set up for evaluating the impacts from ingesting different types of observations: SuperDARN fit and grid data, respectively. Six independent data sources are used as validation data sets to compare outcomes with or without ingesting ion drifts. One is the vector ion velocities derived from the Millstone Hill Incoherent Scatter Radar (MHISR) and a second is the vertical drift from Arecibo site. The other four are SuperDARN ion velocity grid data from Saskatoon, Kapuskasing, Christmas Valley West, and Hokkaido East. Results show improvements in performance at mid‐latitudes by augmenting electron density rates with 3D spatially distributed line‐of‐sight ion drift measurements, with negligible improvements to low and high latitude estimations. The lack of improvement at high‐latitudes is attributed to the increase in EMPIRE ion drift error poleward of 60° magnetic.

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.

In this work we quantify the representation error of the algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), which estimates global neutral winds and ion drifts given time‐varying plasma densities. SAMI3 (Sami3 is A Model of the Ionosphere) serves as the background climate model and pseudo‐measurements for the EMPIRE observation system. This configuration allows the data assimilation inputs to be self‐consistent between each other and with the validation data. The estimated neutral winds and ion drifts are compared to the Horizontal Wind Model (HWM14) and SAMI3 “truth.” For both the quiet period on 25 August 2018 and subs7equent storm on 26 August, the EMPIRE estimation of ion drifts is better at low‐to‐mid geomagnetic latitudes with mean error up to 20 m/s. For the high latitudes (poleward of ±60° magnetic), the mean errors exceed 50 m/s with variances up to 200 m/s, and the relative errors are higher than the “truth.” At latitudes of ±87°, the large errors are attributed to a boundary effect. However, the neutral wind mean errors peak at 20 m/s at mid‐latitudes (40°–60° magnetic), with larger uncertainties, then converge to 0 approaching higher latitudes. By conducting this study, we define a method for obtaining the representation error covariance for future use of EMPIRE with SAMI3 as background.

Realistic modeling of the dynamics and variability in the upper mesosphere and lower thermosphere (UMLT) is critical to understand the coupling between different layers of the whole atmosphere system. Here we present simulations of the UMLT temperatures at ∼100 km altitude for one year during 2014 by the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere extension (WACCM‐X) constrained below ∼90 km using meteorological analysis products of the high‐altitude version of Navy Global Environmental Model (NAVGEM‐HA). The model results are sampled at the same times and latitudes and longitudes as the satellite observations from Thermosphere Ionosphere and Mesosphere Electric Dynamics/Sounding of the Atmosphere using Broadband Emission Radiometry (TIMED/SABER). Comparisons show that the observed and modeled daily zonal mean temperatures are correlated (r ∼0.5–0.7) at most latitudes between ±50°. Both the observations and simulations show an annual variation at mid‐latitudes in two hemispheres with the temperature maximum in summer and the minimum in winter, and at lower latitudes the semiannual variation becomes stronger having the temperature maximums at equinoxes and the minimums during solstices. However, the temperatures observed are on average ∼5–10 K (3%–5%) smaller than the model and the observations show a larger variability. Moreover, migrating tidal amplitudes are mostly overestimated by the model. Though differences are noticed, the WACCM‐X simulations with NAVGEM‐HA meteorological analyses are overall consistent with the SABER observations. These results support that whole atmosphere models informed by high altitude observations would help to simulate the UMLT variability and the atmosphere and ionosphere coupling.