Initial soil moisture as a predictor of subsequent summer severe weather in the cropped grassland of the Canadian Prairie Provinces
Bibliographic record
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 to the planetary boundary layer. This study examined whether modeled areal-average root-zone soil moisture (RzSm) in the "wet" and in the "dry" regions of the cropped grassland of the Canadian Prairie Provinces had predictive value in determining whether these areas would subsequently have above or below average number-occurences and event-days of summer severe convective weather (i.e., tornadoes, large hail, heavy rains and/or damaging winds). RzSm, simulated by the Prairie Agro-climate Model, for the 1997 to 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 June 15th, and on July 15th. The areal-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 which were recorded during the remainder of the growing season for each type of summer severe 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 than zero was then tested. A relationship with r2 near or greater than 0.25, and with a regression line slope that was significantly different than zero, was selected as one which could have potential value in the climatological forecasting of summer severe 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 areal-average RzSm was determined, were greater over the "wet" areas than over the "dry" areas. This thesis represent an advancement in the development of our understanding of the linkage between RzSm and severe weather associated with moist deep convection in the cropped grassland of the Canadian Prairies. It demonstrated that modeling RzSm may improve climatological forecasts of severe convective weather.
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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".