The Assimilative Canadian High Arctic Ionospheric Model (A-CHAIM)
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Date
2024-06
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University of New Brunswick
Abstract
The high latitude ionosphere is coupled with the magnetosphere and solar wind, and exhibits a high degree of variability which cannot be captured by existing models. The objective of the research presented in this dissertation is to improve the specification of ionospheric weather, leading to the development of the Assimilative Canadian High Arctic Ionospheric Model, or A-CHAIM. A-CHAIM is a near-real-time data assimilation model of the high latitude ionospheric electron density, combining ionospheric models with low-latency measurements to produce the best possible representation of the current ionospheric state. Beginning in 2021, A-CHAIM is the first operational space weather model to use a particle filter; a nonlinear data assimilation technique. This required the development of several novel methods to avoid sample degeneracy, which has previously prevented the use of particle filters in large-scale geophysical models. A-CHAIM is shown to produce a significantly improved representation of ionospheric electron density when compared to the empirical background model. The flexibility of particle filters is also exploited to include separate models for the electron density enhancements of auroral electron and solar energetic particle precipitation. The use of Rao-Blackwellized particle filtering to solve for instrumental biases allows A-CHAIM to efficiently estimate the hardware-specific differential code biases (DCBs) of the hundreds of global navigation satellite system (GNSS) receivers in the measurement dataset. This bias estimation technique requires fewer assumptions than previous methods, and is able to include independent measurement types. It is shown that there are systematic errors in existing techniques for DCB estimation which use an external ionospheric reference.