Dependence of Convective Cloud Microphysical Properties on Environmental Conditions during the TRACER and ESCAPE Field Campaigns: A Synergistic Approach of Observations, Machine Learning, and Parcel Models
Bibliographic record
Abstract
Abstract The sensitivity of convective clouds to aerosols and their interactions with environment, combined with limited observational constraints in parameterizations, introduces significant uncertainties in atmospheric models. This study investigates the dependence of convective cloud microphysical properties on environmental conditions using a synergistic approach that combines unique observations from the Tracking Aerosol Convection Interactions Experiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) field campaigns, machine learning techniques, and parcel model simulations with a superdroplet microphysics scheme. A random forest algorithm identifies in situ vertical velocity w , temperature T , and surface fine-mode aerosol mass concentration as the three most important environmental conditions influencing cloud properties including liquid water content (LWC), number concentration for particles with D max < 50 μ m ( N c ,<50 ), 50 μ m ≤ D max ≤ 3000 μ m ( N c ,50–3000 ), and droplet effective diameter D e . The results show that LWC, N c ,<50 , and N c ,50–3000 significantly increase with w in updrafts. Across w bins, as T decreases, LWC, D e , and N c ,50–3000 increase, while N c ,<50 decreases, which are closely linked to the distance above cloud bases. Warmer cloud bases yield higher LWC, greater N c ,50–3000 , and smaller N c ,<50 , while polluted environments produce greater N c ,<50 . Parcel model simulations successfully replicate these observed dependencies. The simulation results indicate that warmer cloud bases enhance condensation generating larger droplets, and differences in droplet sizes are then amplified through collision–coalescence, resulting in a greater N c ,50–3000 . Polluted conditions result in a greater N c ,<50 primarily due to enhanced cloud condensation nuclei activation despite increased collision–coalescence rates compared to pristine conditions. This study provides observed quantitative patterns characterizing cloud microphysical properties as a function of key environmental parameters, offering valuable constraints for improving physics parameterizations and numerical models. Significance Statement This study explores how microscale characteristics of convective clouds change under varying meteorological and aerosol conditions. By analyzing data from two major field campaigns, combined with advanced machine learning and physics-based models, we obtained the quantitative patterns linking cloud microscale characteristics with key environmental factors. We also revealed the mechanisms by which warmer cloud bases produce larger droplets, while polluted environments lead to an increase in smaller droplets. These insights provide critical guidance for improving weather and climate models, helping to reduce forecast uncertainties. This study also highlights the value of integrating observations, machine learning, and numerical modeling to advance our understanding of cloud physics.
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 | 0.001 | 0.001 |
| 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".