Assimilative Real-time Models of HF Absorption at High Latitudes
Bibliographic record
Abstract
Improved real-time HF communications frequency management is required for aircraft on trans-polar routes. Polar cap absorption (PCA) models have therefore been adapted to assimilate real-time measurements of zenithal cosmic radio noise absorption (~ 30 MHz) from a large network of online riometers in Canada and Finland. Two types of PCA model have been developed and improvements to model accuracy following optimisation are quantified. Real-time optimisation is performed by age-weighting riometer measurements in a non-linear regression. This reduces root-mean-square errors (RMSE) from 2-3 dB to less than 1 dB and mean errors to within ±0.2 dB over a wide latitude range. This paper extends previous work by further optimising the models’ dependences on solar-zenith angle to account for differences in the ionospheric response at sunrise and sunset (the Twilight Anomaly). Two models of the rigidity cutoff latitudes are compared and one is optimised in real time by regression to riometer measurements. Whilst measurements from the NASA POES satellites may provide a direct measurement of the rigidity cut-off, it is observed that proton flux measurements from POES often need correcting for relativistic electron contamination for several hours at the start of a PCA event. An optimised real-time absorption model will be integrated into HF ray-tracing propagation predictions relating to measurements of HF signal strengths on a network of HF transmitters and receivers in the high northern latitudes.
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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 | 0.000 |
| 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) | 0.002 | 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".