A-CHAIM: Near-Real-Time Data Assimilation of the High Latitude Ionosphere with a Particle Filter
Bibliographic record
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 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 $45^o$ 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 September 21st, 2021 through September 29th, 2021, A-CHAIM demonstrates a 40% to 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% to 15% improvement at all latitudes. In the topside, A-CHAIM demonstrates a 15% to 20% reduction in error for the Swarm satellites, and a 23% to 28% reduction in error for the DMSP satellites. The reduction in error is distributed evenly over the assimilation region, including in data-sparse regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".