Ionospheric data assimilation is a technique to evaluate the 3-D time varying distribution of electron density using a combination of a physics-based model and observations. A new ionospheric data assimilation method is introduced that has the capability to resolve traveling ionospheric disturbances (TIDs). TIDs are important because they cause strong delay and refraction to radio signals that are detrimental to the accuracy of high-frequency (HF) geolocation systems. The capability to accurately specify the ionosphere through data assimilation can correct systems for the error caused by the unknown ionospheric refraction. The new data assimilation method introduced here uses ionospheric models in combination with observations of HF signals from known transmitters. The assimilation methodology was tested by the ability to predict the incoming angles of HF signals from transmitters at a set of nonassimilated test locations. The technique is demonstrated and validated using observations collected during 2 days of a dedicated campaign of ionospheric measurements at White Sands Missile Range in New Mexico in January 2014. This is the first time that full HF ionospheric data assimilation using an ensemble run of a physics-based model of ionospheric TIDs has been demonstrated. The results show a significant improvement over HF angle-of-arrival prediction using an empirical model and also over the classic method of single-site location using an ionosonde close to the midpoint of the path. The assimilative approach is extendable to include other types of ionospheric measurements.
ASJC Scopus subject areas
- Condensed Matter Physics
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)
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- Department of Electronic & Electrical Engineering - Professor
- Centre for Space, Atmospheric and Oceanic Science
- EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Autonomous Robotics (CENTAUR)
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