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.

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.

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.

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.

We present a method for forecasting the foF2 and hmF2 parameters using modal decompositions from measured ionospheric electron density profiles. Our method is based on Dynamic Mode Decomposition (DMD), which provides a means of determining spatiotemporal modes from measurements alone. Our proposed extensions to DMD use wavelet decompositions that provide separation of a wide range of high-intensity, transient temporal scales in the measured data. This scale separation allows for DMD models to be fit on each scale individually, and we show that together they generate a more accurate forecast of the time-evolution of the F-layer peak. We call this method the Scale-Separated Dynamic Mode Decomposition (SSDMD). The approach is shown to produce stable modes that can be used as a time-stepping model to predict the state of foF2 and hmF2 at a high time resolution. We demonstrate the SSDMD method on data sets covering periods of high and low solar activity as well as low, mid, and high latitude locations.

Over–the–Horizon (OTH) communication is strongly dependent on the state of the ionosphere, which is susceptible to solar flares. Trans–ionospheric high frequency (HF) signals experience a strong attenuation following a solar flare, commonly referred to as Short–Wave Fadeout (SWF). In this study, we examine the role of dispersion relation–collision frequency formulations on the estimation of flare–driven HF absorption seen in Riometer observation using a data assimilation framework. Specifically, the framework first uses modified solar irradiance models (such as EUVAC, FISM), which incorporate high–resolution solar flux data from GOES satellite X-ray sensors, to compute the enhanced ionization produced during the flare events. The framework then uses different dispersion relation–collision frequency formulations to estimate enhanced HF absorption. Finally, the modeled HF absorption is compared against the data to determine which combination of dispersion relation–collision frequency formulation best reproduces the Riometer observations. From the modeling work, we find that the Appleton–Hartree dispersion relation in combination with Schunk–Nagy collision frequency profile produces the best agreement with Riometer data.

The term “ionospheric sluggishness” is used to describe the time delay between maximum radio absorption in the ionosphere following the time of maximum irradiance during a solar flare. Sluggishness is one of the characteristic properties known to be maximized around D-region heights and can be used for studying lower ionospheric (D-region) and mesospheric chemistry. This article is our first attempt to estimate ionospheric sluggishness using high frequency (HF, 3 – 30 MHz) instruments. Specifically, we report on first estimates of sluggishness from riometer and SuperDARN observations following a solar flare and propose two new methods to estimate sluggishness. Sluggishness is shown to be anti-correlated with the peak solar X-ray flux and positively correlated with solar zenith angle and geographic latitude. The choice of instrument, method, and reference solar waveband effects the sluggishness estimation. A simulation study was performed to estimate the effective recombination coefficient, which was found to vary between 4-5 orders of magnitude. We suggest that the effective recombination coefficient is highly sensitive to D-region’s negative and positive ion chemistry.