A 0～6 h Quantitative Snow(Rain) Forecast Technique and Its Application in Vancouver Winter Olympics
Bibliographic record
Abstract
Aiming to reach the scientific goal of SNOWV10 project,a short-range(0～6 h) quantitative snow(including rain) forecast technique was developed by blending mesoscale numerical prediction with radar-based extrapolative forecast,in conjunction with radar estimated snowfall(and rainfall) approaches and automatic identification of precipitation phase(rain,snow or their mix).This technique was then integrated into the GRAPES-SWIFT nowcasting system so that venue forecast could be issued during the Vancouver Winter Olympics on a real-time basis.Results from Vancouver Olympics application in February 2010 show that this quantitative snow(rain) forecast technique,to a certain extent,could meet the requirement of winter snow(or rain) nowcast or warning,whereas the magnitude of rainfall forecast was much less than that of observation and the forecast timing for snow(or rain) initiation and ending was quite inconsistent with observations,which call for future improvement for both NWP models and extrapolation,as well as their blending technique.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".