Three-Dimensional Modelling of Precipitation Enhancement by Cloud Seeding in Three Different Climate Zones
Bibliographic record
Abstract
This study numerically investigates precipitation enhancement from cumuliform clouds in three different climate regions: (1) Arid climate of the United Arab Emirates (UAE); (2) maritime climate of Thailand; and (3) continental climate of Serbia. Recently developed core/shell sodium chloride (NaCl)/titanium dioxide (TiO2) nanostructure (CSNT) aerosol was tested as a precipitation enhancer in all three climate regions. Previous experimental studies in cloud chambers and idealized numerical simulations demonstrated that CSNT is a significantly more effective precipitation enhancer than the traditional NaCl. Here, CSNT and NaCl seeding agents are incorporated into the WRF (Weather Research and Forecasting) model microphysics with explicate treatment of aerosol. Our results show that CSNT is a profoundly more effective precipitation enhancer in the case of arid climate characterized with low humidity. The accumulated surface precipitation in the arid test was 1.4 times larger if CSNT seeding agent was used instead of NaCl. The smallest difference in the effectiveness between CSNT and NaCl was observed in the maritime case due to their similar activation properties at high values of relative humidity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".