Exploring the Influence of Turbulence on Droplet Size Growth and Precipitation in Warm Clouds
Bibliographic record
Abstract
There has been significant progress in comprehending the role of characteristic properties of aerosol in cloud droplet formation over the past decade [1]; however, the growth of cloud droplets into rain droplets, initiating precipitation in warm clouds, is still not well understood [2]. Collision and coalescence among droplets are assumed to be responsible for the rapid growth of cloud droplets to rain droplets. Turbulence is believed to play a significant role in the growth of cloud droplets [2]. The influence of turbulence on droplet dynamics is nominally characterized by the Stokes number, which decides if the droplet either follows the streamlines or decorrelates from it. Due to their deviation from the streamlines, droplets can form clusters and caustics, thereby increasing the chance of collisions [3]. Thus, the size distribution of droplets can determine the influence of turbulence on droplet collisions. The cloud droplet size distribution depends on several parameters, such as the initial aerosol number concentration, aerosol properties, and the in-cloud supersaturation. Thus, investigating the influence of turbulence on a given droplet size distribution can facilitate a better scientific understanding of the onset of precipitation. In the present study, we experimentally investigated the influence of turbulence on different cloud droplet size distributions. We generated homogeneous isotropic turbulence of various intensities in a closed chamber and seeded it with droplets relevant to that observed in clouds originating under different environmental conditions. Using Phase Doppler particle analyzer (PDPA), we measured the droplet size distributions and analyzed the changes with turbulence intensity. Our experiments show significant growth for cloud droplet size distributions with a higher degree of polydispersity than slender droplet size distributions. We attribute this enhancement in collisions to the induced relative velocity between droplets of different Stokes numbers. We observed a positive trend between clustering and droplet size growth, thus indicating the role of clustering in enhancing collisions.Acknowledgments: We thank Dr. Amit Kumar Patra and Dr. T. Narayana Rao for their valuable suggestions. We acknowledge the ISRO-IITM cell (No. SP/21-22/1197/AE/ISRO/002696) and IoE initiative (SP22231222CPETWOCTSHOC) for funding this work. References:[1] Gunthe, S.S., King, S.M., Rose, D., Chen, Q., Roldin, P., Farmer, D.K., Jimenez, J.L., Artaxo, P., Andreae, M.O., Martin, S.T. and Pöschl, U., 2009. Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity. Atmospheric Chemistry and Physics, 9(19), pp.7551-7575.[2] Devenish, B.J., Bartello, P., Brenguier, J.L., Collins, L.R., Grabowski, W.W., IJzermans, R.H.A., Malinowski, S.P., Reeks, M.W., Vassilicos, J.C., Wang, L.P. and Warhaft, Z., 2012. Droplet growth in warm turbulent clouds. Quarterly Journal of the Royal Meteorological Society, 138(667), pp.1401-1429.[3] Ravichandran, S. and Govindarajan, R., 2015. Caustics and clustering in the vicinity of a vortex. Physics of Fluids, 27(3).
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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 | 0.000 | 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) | 0.000 | 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".