Size-resolved measurements of ice-nucleating particles at six locations in North America and one in Europe
Bibliographic record
Abstract
Abstract. Detailed information on the size of ice-nucleating particles (INPs) may be useful in source identification, modeling their transport in the atmosphere to improve climate predictions, and determining how effectively or ineffectively instrumentation used for quantifying INPs in the atmosphere captures the full INP population. In this study we report immersion-mode INP number concentrations as a function of size at six ground sites in North America and one in Europe using the micro-orifice uniform-deposit impactor droplet freezing technique (MOUDI-DFT), which combines particle size-segregation by inertial impaction and a microscope-based immersion freezing apparatus. The lowest INP number concentrations were observed at Arctic and alpine locations and the highest at suburban and agricultural locations, consistent with previous studies of INP concentrations in similar environments. We found that 91 ± 9, 79 ± 17, and 63 ± 21 % of INPs had an aerodynamic diameter > 1 µm at ice activation temperatures of −15, −20, and −25 °C, respectively, when averaging over all sampling locations. In addition, 62 ± 20, 55 ± 18, and 42 ± 17 % of INPs were in the coarse mode (> 2.5 µm) at ice activation temperatures of −15, −20, and −25 °C, respectively, when averaging over all sampling locations. These results are consistent with six out of the nine studies in the literature that have focused on the size distribution of INPs in the atmosphere. Taken together, these findings strongly suggest that supermicron and coarse-mode aerosol particles are a significant component of the INP population in many different ground-level environments. Further size-resolved studies of INPs as a function of altitude are required since the size distribution of INPs may be different at high altitudes due to size-dependent removal processes of atmospheric particles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".