Initial soil moisture as a predictor of subsequent severe summer weather in the cropped grassland of the Canadian Prairie provinces
Bibliographic record
Abstract
Abstract Soil moisture, along with the type and stage of the vegetation, influences the thermodynamic structure of the atmosphere by regulating heat and moisture fluxes in the planetary boundary layer (PBL). This study examined whether the modelled aerial‐average root‐zone soil moisture (RzSm) in ‘wet’ and ‘dry’ areas of the cropped grassland of the Canadian Prairie provinces had predictive value in determining if these areas would subsequently have above‐ or below‐normal occurrences and event days of severe summer convective weather (i.e. tornadoes, hail, heavy rains, and/or strong winds). RzSm, simulated by the Prairie Agro‐climate Model, for the 1997–2003 growing seasons was analyzed three times per season. Dry areas with RzSm ⩽50% of available water holding capacity (AWHC) and wet areas with RzSm > 50% of AWHC were delineated post‐snowmelt, on 15th June, and on 15th July. The aerial‐average RzSm levels in the dry and in the wet areas were calculated, and plotted against the relative number of occurrences and number of event days that were recorded during the remainder of the growing season for the various types of severe summer convective weather. In each case, the best‐fit linear regression line and the variance that it explained (r2 value) were computed. The hypothesis that the slope of each regression line was significantly different from zero was then tested. A value of r2 close to or greater than 0.25 was arbitrarily used as a cut‐off point—a relationship with an r2 close to or greater than this value, and with a regression line slope that was significantly different from zero, was selected as one which could have potential value in the climatological forecasting of severe summer convective weather. For most of the severe weather types, the relative number of occurrences and the relative number of event days, which were recorded subsequent to the three dates on which the aerial‐average RzSm was determined were greater in the wet areas than in the dry areas. Copyright © 2008 Royal Meteorological Society
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".