A‐CHAIM: Near‐Real‐Time Data Assimilation of the High Latitude Ionosphere With a Particle Filter
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Bibliographic record
Abstract
Abstract The Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is an operational ionospheric data assimilation model that provides a 3D representation of the high latitude ionosphere in Near‐Real‐Time (NRT). A‐CHAIM uses low‐latency observations of slant Total Electron Content (sTEC) from ground‐based Global Navigation Satellite System (GNSS) receivers, ionosondes, and vertical TEC from the JASON‐3 altimeter satellite to produce an updated electron density model above 45 ° geomagnetic latitude. A‐CHAIM is the first operational use of a particle filter data assimilation for space environment modeling, to account for the nonlinear nature of sTEC observations. The large number (>10 4 ) of simultaneous observations creates significant problems with particle weight degeneracy, which is addressed by combining measurements to form new composite observables. The performance of A‐CHAIM is assessed by comparing the model outputs to unassimilated ionosonde observations, as well as to in‐situ electron density observations from the SWARM and DMSP satellites. During moderately disturbed conditions from 21 September 2021 through 29 September 2021, A‐CHAIM demonstrates a 40%–50% reduction in error relative to the background model in the F2‐layer critical frequency (foF2) at midlatitude and auroral reference stations, and little change at higher latitudes. The height of the F2‐layer (hmF2) shows a small 5%–15% improvement at all latitudes. In the topside, A‐CHAIM demonstrates a 15%–20% reduction in error for the Swarm satellites, and a 23%–28% reduction in error for the DMSP satellites. The reduction in error is distributed evenly over the assimilation region, including in data‐sparse regions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it