Validation of ANCHOR Ionospheric Data Assimilation Model Using GIRO Ionosondes Within PyVALION Software

Abstract Accurate specification of the ionosphere is essential for mitigating the impacts of space weather on communication, navigation, and surveillance systems. ANCHOR is a newly developed ionospheric data assimilation (DA) model from the U.S. Naval Research Laboratory that fuses measurements from ionosondes, radio occultation, and Global Navigation Satellite System (GNSS) receivers into a PyIRI‐based background via a Kalman filter. To support model validation in both reproduction and standardization, PyVALION (Python VALidation for IONosphere) is introduced—a community‐oriented tool built on a forward‐operator framework that projects model output into observation space for statistically robust comparison. Currently, PyVALION directly supports validation of models producing F2, F2, and thickness parameters and , with extension to other parameters for future releases. ANCHOR's performance is assessed using electron density parameters (F2 and F2) from the Global Ionospheric Radio Observatory (GIRO) ionosonde network. Four multi‐day geophysical events are analyzed to evaluate model behavior under varying solar and geomagnetic conditions. Results show that ANCHOR significantly reduces both bias and root mean square error (RMSE) relative to the PyIRI background. Separating day and night conditions reveals that background bias is larger during daytime, and data assimilation proves particularly effective at correcting this bias when it is most significant. A controlled synthetic experiment confirms that forward‐operator errors in PyVALION are negligible compared to observed residuals, validating its use as a precision‐grade evaluation tool. These findings establish PyVALION as a reproducible and scalable framework for ionospheric model assessment and confirm ANCHOR's capability to enhance real‐time ionospheric specification.