Impacts of Land–Atmosphere Feedbacks on Deep, Moist Convection on the Canadian Prairies
Bibliographic record
Abstract
Abstract The purpose of this study was to focus on how anomalies in the normalized difference vegetation index (NDVI; a proxy for soil moisture) over the Canadian Prairies can condition the convective boundary layer (CBL) so as to inhibit or facilitate thunderstorm activity while also considering the role of synoptic-scale forcing. This study focused on a census agricultural region (CAR) over central Alberta for which we had observed lightning data (proxy for thunderstorms), remotely sensed NDVI data, and in situ rawinsonde data (to quantify impacts of vegetation vigor on the CBL characteristics) for 11 summers from 1999 to 2009. The authors’ data suggest that the occurrence of lightning over the study area is more likely (and is of longer duration) when storms develop in an environment in which the surface and upper-air synoptic-scale forcing are synchronized. On days when surface forcing and midtropospheric ascent are present, storms are more likely to be triggered when NDVI is much above average, compared to when NDVI is much below average. Additionally, the authors found the response of thunderstorm duration to NDVI anomalies to be asymmetric. That is, the response of lightning duration to anomalies in NDVI is marked when NDVI is below average but is not necessarily discernible when NDVI is above average. The authors propose a conceptual model, based largely on observations, that integrates all of the above findings to describe how a reduction in vegetation vigor—in response to soil moisture deficits—modulates the partitioning of available energy into sensible and latent heat fluxes at the surface, thereby modulating lifting condensation level heights, which in turn affect lightning activity.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".