MétaCan
Menu
Back to cohort
Record W2788099502 · doi:10.3390/atmos9030080

Moist Orographic Convection: Physical Mechanisms and Links to Surface-Exchange Processes

2018· article· en· W2788099502 on OpenAlexafffund
Daniel J. Kirshbaum, Bianca Adler, Norbert Kalthoff, Christian Barthlott, Stefano Serafin

Bibliographic record

VenueAtmosphere · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvectionAdvectionConvection cellGeologyOrographyAtmospheric sciencesEnvironmental scienceConvective inhibitionMechanicsMeteorologyCombined forced and natural convectionPrecipitationPhysicsNatural convection

Abstract

fetched live from OpenAlex

This paper reviews the current understanding of moist orographic convection and its regulation by surface-exchange processes. Such convection tends to develop when and where moist instability coincides with sufficient terrain-induced ascent to locally overcome convective inhibition. The terrain-induced ascent can be owing to mechanical (airflow over or around an obstacle) and/or thermal (differential heating over sloping terrain) forcing. For the former, the location of convective initiation depends on the dynamical flow regime. In “unblocked” flows that ascend the barrier, the convection tends to initiate over the windward slopes, while in “blocked” flows that detour around the barrier, the convection tends to initiate upstream and/or downstream of the high terrain where impinging flows split and rejoin, respectively. Processes that destabilize the upstream flow for mechanically forced moist convection include large-scale moistening and ascent, positive surface sensible and latent heat fluxes, and differential advection in baroclinic zones. For thermally forced flows, convective initiation is driven by thermally direct circulations with sharp updrafts over or downwind of the mountain crest (daytime) or foot (nighttime). Along with the larger-scale background flow, local evapotranspiration and transport of moisture, as well as thermodynamic heterogeneities over the complex terrain, regulate moist instability in such events. Longstanding limitations in the quantitative understanding of related processes, including both convective preconditioning and initiation, must be overcome to improve the prediction of this convection, and its collective effects, in weather and climate models.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations219
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueAtmosphereSame topicMeteorological Phenomena and SimulationsFrench-language works237,207