A precipitation parameterization for the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) and other empirical models
Bibliographic record
Abstract
The Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) [1,2,3] was developed as an alternative to the use of traditional global empirical ionospheric models at high latitudes, namely the International Reference Ionosphere (IRI) [4] and NeQuick [5]. While E-CHAIM has been demonstrated significantly improvements over those models at high latitudes [1,2], it lacks the implementation of an auroral precipitation scheme and as such does not account for significantly enhanced E-Region densities in that region [3]. In this study, we will present the new auroral precipitation module that has been developed for implementation with ECHAIM. Assuming a Maxwellian energy distribution, the scheme uses a Fang et. al. (2010) [6] parameterization with an NRLMSIS background neutral atmosphere to represent the vertical structure of precipitation-induced ionization for an input precipitation flux and mean energy. Precipitation flux and mean energy are then modeled based on TIMED GUVI- and DMSP SSUSI-inferred precipitation characteristics. Beginning with an overview of how the parameterization was implemented, we will further validate the model against Incoherent Scatter Radar (ISR) measurements of auroral electron density and compare the performance of the model with what can be achieved with the parameterization when using the Zhang and Paxton (2008) [7] mean precipitation energy and flux model. We will further examine the possibility of implementing such a scheme in the IRI and examine whether hemispheric differences in mean energy and flux must be accommodated in such a system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".