Application of the Factor Separation Methodology to quantify the effect of waste heat, vapor and pollution on cumulus convection
Bibliographic record
Abstract
Large oil refineries emit heat, vapor, and cloud condensation nuclei (CCN), all of which can affect the formation of cloud and precipitation. The Factor Separation (FS) technique is applied to isolate the net contributions of waste heat, vapor, and CCN to the rainfall of a cumulus developing in the industrial plume. The mutualinteractive contributions of two or three of the factors are also computed. The model simulations indicate that the sensible heat provides the major stimulus for cloud development and rain formation. The pure contribution of the industrial CCN is to enhance the condensation causing an increase in the mass of total cloud water. The contributions arising from mutual interactions among two or three factors are quite large and should not be neglected. Particularly, the synergistic interaction of the sensible heat and pollution effects contribute towards the accumulated rainfall. Introduction There is considerable interest in the effects of large electrical power plants and oil refineries on meteorological phenomena. Preferential cumulus formation has been observed above electrical power plants and oil refineries (Auer, 1976). Hobbs et al . (1970) reported that in regions adjacent to or downwind of the Port Townsend paper mill (Washington State, USA) the annual rainfall recorded was 30% greater than the rainfall from nearby stations. This dramatic increase in annual precipitation is likely caused by the presence of the paper mill. Hobbs et al . speculated that the enhanced rainfall might be attributed to the large and giant CCN emitted from the paper mill into the pollution plume. Support for this hypothesis came from Eagan et al .'s (1974) study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".