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.

The International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model outputs were calculated separately for each given geographic location and time stamp. The Consultative Committee on International Radio (CCIR) and International Union of Radio Science (URSI) coefficients provide the skeleton of the IRI model, as they define the global distribution of the maximum usable ionospheric frequency foF2 and the propagation factor M(3000)F2. At the U.S. Naval Research Laboratory (NRL), a novel Python tool was developed that enables global runs of the IRI model with significantly lower computational overhead. This was made possible through the Python rebuild of the core IRI component (which calculates ionospheric critical frequency using the CCIR or URSI coefficients), taking advantage of NumPy matrix multiplication instead of using cyclic addition. This paper explains in detail this new approach and introduces all components of the PyIRI package.

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.

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.