Drivers of Droplet Formation in East Mediterranean Orographic Clouds
Bibliographic record
Abstract
The purpose of this study is to understand the drivers of cloud droplet formation in orographic clouds. We used a combination of modeling, in situ, and remote sensing measurements at the high-altitude Helmos Hellenic Atmospheric Aerosol and Climate Change ((HAC)2) station, which is located at the top of Mt. Helmos (1314 m above sea level), Greece, during the Cloud–AerosoL InteractionS in the Helmos Background TropOsphere (CALISHTO) campaign in fall 2021 (https://calishto.panacea-ri.gr/, last access: 1 August 2024) to examine the origins of the aerosols (i.e., local aerosol from the planetary boundary layer (PBL) or long-range-transported aerosol from the free-tropospheric layer (FTL) contributing to the cloud condensation nuclei (CCN)), their characteristics (hygroscopicity, size distribution, and mixing state), and the vertical velocity distributions and resulting supersaturations. We found that the characteristics of the PBL aerosol were considerably different from FTL aerosol and use the aerosol particle number and equivalent mass concentration of the black carbon (eBC) in order to determine when (HAC)2 was within the FTL or PBL based on time series of the height of the PBL. During the (HAC)2 cloud events we sample a mixture of interstitial aerosol and droplet residues, which we characterize using a new approach that utilizes the in situ droplet measurements to determine time periods when the aerosol sample is purely interstitial. From the dataset we determine the properties (size distribution and hygroscopicity) of the pre-cloud, activated, and interstitial aerosol. The hygroscopicity of activated aerosol is found to be higher than that of the interstitial or pre-cloud aerosol. A series of closure studies with the droplet parameterization shows that cloud droplet concentration (Nd) and supersaturation can be predicted to within 25 % of observations when the aerosol size distributions correspond to pre-cloud conditions. The analysis of the characteristic supersaturation of each aerosol population indicates that droplet formation in clouds is aerosol-limited when formed in FTL air masses – hence droplet formation is driven by aerosol variations, while clouds formed in the PBL tend to be velocity-limited and droplet variations are driven by fluctuations in vertical velocity. Given that the cloud dynamics do not vary significantly between air masses, the variation in aerosol concentration and type is mostly responsible for these shifts in cloud microphysical state and sensitivity to aerosol. With these insights, the remote sensing of cloud droplets in such clouds can be used to infer either CCN spectra (when in the FTL) or vertical velocity (when in the PBL). In conclusion, we show that a coordinated measurement of aerosol and cloud properties, together with the novel analysis approaches presented here, allows for the determination of the drivers of droplet formation in orographic clouds and their sensitivity to aerosol and vertical velocity variations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".