Flux Footprints Over a Forested Hill Derived From a Lagrangian Particle Model Coupled Into a Large‐Eddy Simulation Model
Bibliographic record
Abstract
Abstract Flux footprints are widely used in the study of turbulent flux measurements. Most of the existing footprint models assume horizontal homogeneity. However, as more and more flux towers are established over complex terrain, it is necessary to advance our understanding of footprints over complex terrain. Here we use a Lagrangian particle model coupled into a large‐eddy simulation model to investigate footprints over an idealized 2‐dimensional forested hill. Coordinate rotation, which is similar to that performed in real eddy‐covariance measurement, is considered in the calculation of footprints. For detectors over the upwind slope, their footprints are generally larger than the footprints of the detectors over the upwind flat ground. For detectors over the separation point, which is slightly downwind of the hill crest, their footprints extend both in the upwind and downwind directions. For detectors over the downwind slope and away from the separation point, their footprints also extend to the downwind direction, provided that the sources are released at the lower half of the canopy. This substantial downwind extension is in contrast to the conventional viewpoint. It is found that the footprints for the whole soil‐canopy system can be calculated by assuming that the canopy source/sink occurs at the single layer with the strongest source/sink. Compared to the footprints calculated with coordinate rotation, footprints calculated without coordinate rotation extend much farther upwind for detectors over the upwind slope, and have opposite signs for detectors over the downwind slope.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".