Towards the development of a high resolution extreme wind climatology for Switzerland
Bibliographic record
Abstract
This study aims at establishing a high-resolution climatology of extreme winds over Switzerland using a numerical modelling approach. The basic model is the Canadian Regional Climate Model (CRCM; Caya and Laprise, 1999) to which is applied a new windgust parameterisation (Brasseur, 2001; Goyette et al., 2003). This model has previously shown genuine skill in downscaling a number of observed windstorms over Western Europe using the NCEP-NCAR reanalysis as driving data (e.g. Goyette et al., 2001). As input data, this study uses the simulated outputs of the UK HADLEY Center's HADCM3 coupled ocean-atmosphere global model - available in the EU 5th Framework Program “PRUDENCE” project (Christensen et al., 2002)- for the 1961-1990 period. Using the multiple self-nesting procedure of the CRCM, a number of storms are downscaled at 2 km over Switzerland. The preliminary analysis of ten windstorms is very encouraging: the potential areas which experienced severe winds agree well with observations. Most of the severe winds episodes are embedded in westerly flows; however observations show that strong winds may also be generated by southerly and northerly type of flows. These results thus prompt the need for further storm investigation to encompass the largest portion of potential extreme wind cases since the ultimate goal is to assess the change in the behaviour of extreme winds under climate changes based on the IPCC A2 greenhouse-gas emission scenario.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".