Overview of Ice Nucleating Particles
Bibliographic record
Abstract
Abstract Ice particle formation in tropospheric clouds significantly changes cloud radiative and microphysical properties. Ice nucleation in the troposphere via homogeneous freezing occurs at temperatures lower than −38°C and relative humidity with respect to ice above 140%. In the absence of these conditions, ice formation can proceed via heterogeneous nucleation aided by aerosol particles known as ice nucleating particles (INPs). In this chapter, new developments in identifying the heterogeneous freezing mechanisms, atmospheric relevance, uncertainties, and unknowns about INPs are described. The change in conventional wisdom regarding the requirements of INPs as new studies discover physical and chemical properties of these particles is explained. INP sources and known reasons for their ice nucleating properties are presented. The need for more studies to systematically identify particle properties that facilitate ice nucleation is highlighted. The atmospheric relevance of long-range transport, aerosol aging, and coating studies (in the laboratory) of INPs are also presented. Possible mechanisms for processes that change the ice nucleating potential of INPs and the corresponding challenges in understanding and applying these in models are discussed. How primary ice nucleation affects total ice crystal number concentrations in clouds and the discrepancy between INP concentrations and ice crystal number concentrations are presented. Finally, limitations of parameterizing INPs and of models in representing known and unknown processes related to heterogeneous ice nucleation processes are discussed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".