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Record W1993227925 · doi:10.1175/jas-d-14-0257.1

The Role of Wave Dynamics and Small-Scale Topography for Downslope Wind Events in Southeast Greenland

2015· article· en· W1993227925 on OpenAlexafffund
Marilena Oltmanns, Fiammetta Straneo, Hyodae Seo, G. W. K. Moore

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsKatabatic windMesoscale meteorologyGeologyClimatologyBreaking waveWeather Research and Forecasting ModelConvectionAtmosphere (unit)Atmospheric sciencesMeteorologyWave propagationGeography

Abstract

fetched live from OpenAlex

In Ammassalik, in southeast Greenland, downslope winds can reach hurricane intensity and represent a hazard for the local population and environment. They advect cold air down the ice sheet and over the Irminger Sea, where they drive large ocean–atmosphere heat fluxes over an important ocean convection region. Earlier studies have found them to be associated with a strong katabatic acceleration over the steep coastal slopes, flow convergence inside the valley of Ammassalik, and—in one instance—mountain wave breaking. Yet, for the general occurrence of strong downslope wind events, the importance of mesoscale processes is largely unknown. Here, two wind events—one weak and one strong—are simulated with the atmospheric Weather Research and Forecasting (WRF) Model with different model and topography resolutions, ranging from 1.67 to 60 km. For both events, but especially for the strong one, it is found that lower resolutions underestimate the wind speed because they misrepresent the steepness of the topography and do not account for the underlying wave dynamics. If a 5-km model instead of a 60-km model resolution in Ammassalik is used, the flow associated with the strong wind event is faster by up to 20 m s −1 . The effects extend far downstream over the Irminger Sea, resulting in a diverging spatial distribution and temporal evolution of the heat fluxes. Local differences in the heat fluxes amount to 20%, with potential implications for ocean convection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.222
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2015
Admission routes2
Has abstractyes

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