Extreme precipitation in Central Norway. A case and climate study
Bibliographic record
Abstract
An extreme precipitation event occurred over Central Norway at the end of January to the beginning of February 2006. The heavy precipitation in addition to high temperatures lead to snow melt and increased run-off, which produced flooding and landslides that caused considerable damage to infrastructure and loss of human life. A numerical weather prediction tool is used to model the flow pattern on synoptic and mesoscale to find the cause for the generation of the high precipitation rates. Forced lifting of warm moist air due to strong perpendicular winds over the mountains in Central Norway is found to be the main cause. A second topographical effect is the blocking of the flow by the mountain ridge in Southern Norway. The blocking causes a deflection and enhancement of the forcing over Central Norway, and leads to more precipitation. Vertical motion described by the quasi-geastrophic theory is found to be of limited importance. The warm moist air over Trøndelag during the event is calculated backwards to the subtropics. An investigation of the predictability of the event reveals a sensitivity in a baroclinic zone in an area south of Newfoundland upstream of the event. A likely effect of the global climate change is a shift in the frequency of extreme events, and an increase in combined extreme events like the one described in this thesis. Results from a global climate model are downscaled with a higher resolution regional climate model in order to acquire a description of the frequency of similar events in a future greenhouse gas scenario. There is found an increase in high temperature events during winter, and an increase in frequency for similar extreme precipitation events during the whole year and winter.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".