Seasonal and solar wind sector duration influences on the correlations of high latitude clouds with ionospheric potential.
Bibliographic record
Abstract
Irradiances from long-lived stratus-type clouds at Alert (Canada), Summit (Greenland), and South Pole, previously measured, show correlations with the day-to-day input to the global atmospheric electric circuit from the solar wind, as well as with the inputs of low- and mid-latitude thunderstorms and shower clouds. We analyze the measured Alert cloud irradiances, and find differences in the responses to 2, 4, or more solar wind sectors per 27-day solar rotation. We find seasonal variations in the correlations, with sign reversal in the summer. The correlation coefficients that were found previously for all-year, all sector types show further increases for just winter months and in addition, for just 2-sector intervals. At high magnetic latitudes the ionospheric potential correlates strongly with the solar wind sector structure, and determines the flow of current density (Jz) to the Earth;s surface that passes through clouds and modifies space charge in them. Parameterizations of the potential distribution near the magnetic pole are used in the correlations. The daily average values depend mainly on the solar wind (interplanetary) magnetic field (IMF) By component, with lesser influence of the solar wind speed and IMF Bz. Mechanisms by which space charge in clouds can affect cloud microphysics and cloud opacity are described and are qualitatively consistent with the correlations, but need quantitative testing.
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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 | 0.000 |
| 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".