Impact of mesoscale vegetation heterogeneities on the dynamical and thermodynamic properties of the planetary boundary layer
Bibliographic record
Abstract
This study uses aircraft observations over the Republic of Benin from the African Monsoon Multidisciplinary Analyses (AMMA) campaign to investigate the impact of vegetation heterogeneities on the dynamics within the planetary boundary layer, such as convection, transport, and mixing. Isoprene, a biogenic volatile organic compound emitted primarily by woody vegetation, was used as a tracer for transport to link the land surface to the boundary layer properties. Associated to planetary boundary layer (PBL) temperature gradients at vegetation boundaries, a persistent mesoscale organization of the winds which controlled patterns in the formation of cumulus congestus clouds was observed. A strong relationship among PBL temperatures, meridional wind velocity, isoprene concentrations, and fraction of forest or shrub cover was found, corroborating the land surface forcing of the observed dynamics. The observations show that the convergence zones tended to occur on the southern edge of warm surface and atmospheric anomalies. The northerly synoptic wind appears to have increased the coherency of the southerly part of the mesoscale flow and displaced the convergence zones southward. The relationships between the PBL potential temperatures and the meridional wind and isoprene concentrations were spatially coherent down to wavelengths of 10 and 8 km, respectively. A seasonal climatology of visible satellite data shows enhanced cloud cover in the afternoon over cropland, suggesting that the results presented are not limited to this case study but are of climatological significance in the region.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".