SPATIOTEMPORAL EVALUATION OF NOCTURNAL COLD AIR DRAINAGE OVER A SIMPLE SLOPE USING THERMAL INFRARED IMAGERY
Bibliographic record
Abstract
Abstract. Measurements of climatic processes such as cold air drainage flows are problematic over mountainous areas. Observation of cold air drainage is not available in the existing observation network and it requires a special methodology. The main objective of this study was to characterize the cold air drainage over regions with a slope. A high resolution infrared camera, a meteorological station and Digital Elevation Model (DEM) were used. The specific objective was to derive nocturnal cold air drainage velocity over the slope. To address these objectives, a number of infrared measurement campaigns were conducted during calm and clear sky conditions over an agricultural zone (blackcurrant farm) in Canada. Using thermal infrared images, the nocturnal surface temperature gradient were computed in hourly basis. The largest gradient magnitudes were found between 17h -20h. The cooling rates at basin area were two times higher in comparison to the magnitudes observed within slope area. The image analysis illustrated this considerable temperature gradient of the basin may be partly due to transport of cold air drainage into the basin from the slope. The results show that thermal imagery can be used to characterize and understand the microclimate related to the occurrence of radiation frost in the agricultural field. This study provided the opportunity to track the cold air drainage flow and pooling of cold air in low lying areas. The infrared analysis demonstrated that nocturnal drainage flow displayed continuous variation in terms of space and time in response to microscale slope heterogeneities. In addition, the analysis highlighted the periodic aspect for cold air drainage flow.
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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 | 0.001 | 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".