Validation of Ionospheric Models at Mid- and High-Latitudes: Climatological Performance of WACCM-X (SD) and TIE-GCM in foF2
Bibliographic record
Abstract
Modelling the high-latitude ionosphere-thermosphere system is imperative to understanding the impacts of space weather on modern technology, such as communications and navigation systems. At low- and mid- latitudes, physics-based and empirical models are well-developed and capture the variability of the ionosphere to a good degree of accuracy. At high-latitudes, however, the complex chemistry and dynamics due to interactions with the solar wind and magnetosphere, added to a lack of observations, presents challenges to such models. In this study we evaluate the climatological performance of the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM), the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extensions (WACCM-X), and the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) for the period 1950 – 2022. Model outputs are compared to observational foF2 data from the high-latitude Resolute Bay ionosonde and the mid-latitude Chilton ionosonde to assess the limitations of models in reproducing the variability of the high-latitude ionosphere. We find that TIE-GCM and WACCM-X exhibit strong winter anomaly behaviour at all times of day at high-latitude, which is not seen in observational data, making overestimates in foF2 of up to 6.15 MHz and 8.65 MHz respectively. An equinoctial asymmetry is also present at both mid- and high- latitudes, with differences in observational foF2 of up to 2.92 MHz and 2.38 MHz between March and September, respectively. We further find that including lower atmospheric forcing within physics-based models improves representation of ionospheric variability during the summertime but can cause further overestimations and winter anomaly behaviour during the winter.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".