The impact of particle precipitation on the ion-neutral collision frequency analyzed with EISCAT measurements
Bibliographic record
Abstract
Abstract. The ion-neutral collision frequency is a key parameter for the coupling of the neutral atmosphere and the ionosphere. Especially in the mesosphere lower-thermosphere (MLT), the collision frequency is crucial for multiple processes e.g. Joule heating, neutral dynamo effects, and momentum transport due to ion drag. Very few approaches exist to directly infer ion-neutral collision frequency measurements in that altitude range. We apply the recently demonstrated difference spectrum fitting method to obtain the ion-neutral collision frequency from dual-frequency measurements with the EISCAT incoherent scatter radars in Tromsø. A 60-hour-long EISCAT campaign was conducted in December 2022. Strong variations of nighttime ionization rates were observed with electron densities at 95 km altitude varying from Ne,95 ∼ 109 − 1011 m−3 which indicates varying levels of particle precipitation. A second EISCAT campaign was conducted on 16 May 2024 capturing a Solar Energetic Particle (SEP) event, exhibiting constantly increased ionization due to particle precipitation in the lower E region Ne,95 ≳ 5 · 1010 m−3. We demonstrate that the particle precipitation significantly impacts the ion-neutral collision frequency profile. Assuming a rigid-sphere particle model, we derive neutral density profiles and show that the particle precipitation heating causes a significant uplift of neutral gas between about 90–110 km altitude. We additionally test the sensitivity of the difference spectrum method to different a priori collision frequency profiles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".