Development and Validation of Precipitation Enhanced Densities for the Empirical Canadian High Arctic Ionospheric Model
Bibliographic record
Abstract
Abstract The Empirical Canadian High Artic Ionospheric Model (E‐CHAIM) provides the four‐dimensional ionosphere electron density at northern high latitudes (>50° geomagnetic latitude). Despite its emergence as the most reliable model for high‐latitude ionosphere density, there remain significant deficiencies in E‐CHAIM's representation of the lower ionosphere (below ∼200 km) due to a sparsity of reliable measurements at these altitudes, particularly during energetic particle precipitation events. To address this deficiency, we have developed a precipitation component for E‐CHAIM to be driven by satellite‐based far‐ultraviolet (FUV) imager data. Satellite observations of FUV emissions may be used to infer the characteristics of energetic particle precipitation and subsequently calculate the precipitation‐enhanced ionization rates and ionosphere densities. In order to demonstrate the improvement of E‐CHAIM's ionosphere density representation with the addition of a precipitation component, this paper presents comparisons of E‐CHAIM precipitation‐enhanced densities with ionosphere density measurements of three auroral region incoherent scatter radars (ISRs) and one polar cap ISR. Calculations for 29,038 satellite imager and ISR conjunctions during the years 2005–2019 revealed that the root‐mean‐square difference between E‐CHAIM and ISR measurements decreased by up to 2.9 × 1010 ele/m3 (altitude dependent) after inclusion of the precipitation component at auroral sites, and by 2.6 × 109 ele/m3 in the polar cap. Improvements were most substantial in the winter season and during active auroral conditions. The sensitivity of precipitation‐enhanced densities to uncertainties inherent to the calculation method was also examined, with the bulk of the errors due to uncertainties in FUV imager data and choice of distribution function for precipitation energy spectra.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".