A Modeling Framework for Estimating Ionospheric HF Absorption Produced by Solar Flares
Bibliographic record
Abstract
Abstract Over‐the‐Horizon communication is strongly dependent on the state of the ionosphere, which is susceptible to solar flares. Trans‐ionospheric high frequency (HF, 3–30 MHz) signals can experience strong attenuation following a solar flare that lasts typically for an hour, commonly referred to as shortwave fadeout (SWF). In this study, we examine the role of dispersion relation and collision frequency formulations on the estimation of SWF in riometer observations using a new physics‐based model framework. The new framework first uses modified solar irradiance models incorporating high‐resolution solar flux data from the GOES satellite X‐ray sensors as input to compute the enhanced ionization produced during a flare event. The framework then uses different dispersion relation and collision frequency formulations to estimate the enhanced HF absorption. The modeled HF absorption is compared with riometer data to determine which formulation best reproduces the observations. We find the Appleton‐Hartree dispersion relation in combination with the averaged collision frequency profile reproduces riometer observations with an average skill score of 0.4, representing 40% better forecast ability than the existing D‐region Absorption Prediction model. Our modeling results also indicate that electron temperature plays an important role in controlling HF absorption. We suggest that adoption of the Appleton‐Hartree dispersion relation in combination with the averaged collision frequency be considered for improved forecasting of ionospheric absorption following solar flares.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".