Background Free‐Tropospheric Ice Nucleating Particle Concentrations at Mixed‐Phase Cloud Conditions
Bibliographic record
Abstract
Abstract Clouds containing ice are vital for precipitation formation and are important in determining the Earth's radiative budget. However, primary formation of ice in clouds is not fully understood. In the presence of ice nucleating particles (INPs), the phase change to ice is promoted, but identification and quantification of INPs in a natural environment remains challenging because of their low numbers. In this paper, we quantify INP number concentrations in the free troposphere (FT) as measured at the High Altitude Research Station Jungfraujoch (JFJ), during the winter, spring, and summer of the years 2014–2017. INPs were measured at conditions relevant for mixed‐phase cloud formation at T = 241/242 K. To date, this is the longest timeline of semiregular measurements akin to online INP monitoring at this site and sampling conditions. We find that INP concentrations in the background FT are on average capped at 10/stdL (liter of air at standard conditions [T = 273 K and p = 1013 hPa]) with an interquartile range of 0.4–9.6/stdL, as compared to measurements during times when other air mass origins (e.g., Sahara or marine boundary layer) prevailed. Elevated concentrations were measured in the field campaigns of 2016, which might be due to enhanced influence from Saharan dust and marine boundary layer air arriving at the JFJ. The upper limit of INP concentrations in the background FT is supported by measurements performed at similar conditions, but at different locations in the FT, where we find INP concentrations to be below 13/stdL most of the time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".