Eine Methode zur Variation sturmflutrelevanter Wetterlagen über der Ostsee
Bibliographic record
Abstract
Z u s a m m e n f a s s u n g Ziel dieser Studie ist es, Extremwetterlagen so zu modifizieren, dass an der südwestlichen Ostseeküste sehr hohe Sturmflutscheitelwasserstände entstehen können.Für diese Modifikationen wird das regionale Atmosphärenmodell COSMO-CLM eingesetzt.Als Antrieb dienen sowohl Originalantriebsdaten des Globalmodells als auch Analysen mit explizit verändertem zeitlichen Ablauf der Wetterlage.Es zeigt sich, dass dem Modell explizit die modifizierten dreidimensionalen Luftdruckverteilungen aus dem Antriebsdatensatz aufgezwungen werden müssen, um das Muster und die Verlagerungsgeschwindigkeit nennenswert ändern zu können.Die Modifikation der Sturmtiefzuggeschwindigkeit im regionalen Atmosphärenmodell COSMO-CLM hat zu einer Erhöhung der berechneten Scheitelwasserstände von 5-10 cm geführt.S c h l a g w ör t e r Ostsee, Sturmflut, Meteorologie, regionales Atmosphärenmodell, spectral nudging S u m m a r yThe aim of this study is to modify extreme weather situations in order to generate storm surges with very high water levels in the south-western Baltic Sea.For these modifications the regional atmospheric model COSMO-CLM is used.As forcing extreme weather situations were used, which caused storm surges in the south western Baltic Sea in the past.These weather situations were simulated by a global atmospheric model.This study shows that storm surge generating wind fields are not sensitive to short-term perturbations.A modification of the weather situation simulated by the regional atmospheric model was achieved only by deriving the complete three dimensional field of the global model.These modifications raised the water level in the south western Baltic Sea by 5-10 cm. K e y w o r d sBaltic Sea, storm surge, meteorology, regional atmospheric model, spectral Nudging I n h a l t Die Küste, 75 MUSTOK (2009), 21-36
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".