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Record W2515605530 · doi:10.1175/jhm-d-15-0225.1

Influence of Open Water Bodies on the Modeling of Summertime Convection over the Canadian Prairies

2016· article· en· W2515605530 on OpenAlexafffundabout
Deepti Joshi, Marco L. Carrera, Stéphane Bélair, Sylvie Leroyer

Bibliographic record

VenueJournal of Hydrometeorology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersAgriculture and Agri-Food Canada
KeywordsPrecipitationEnvironmental scienceForcing (mathematics)ConvectionClimatologyAtmospheric sciencesConvective storm detectionAtmosphere (unit)Latent heatMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract There are numerous water features on the Canadian landscapes that are not monitored. Specifically, there are water bodies over the prairies and Canadian shield regions of North America that are ephemeral in nature and could have a significant influence on convective storm generation and local weather patterns through turbulent exchanges of sensible and latent heat between the land and the atmosphere. In this study a series of numerical experiments is performed with Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) model at 2.5-km grid spacing to examine the sensitivity of the atmospheric boundary layer and the resulting precipitation to the presence of open water bodies. Operationally, the land–water fraction in GEM is specified by means of static geophysical databases that do not change with time. Uncertainty is introduced in this study into this land–water fraction and the sensitivity of the resulting precipitation is quantified for a convective precipitation event occurring over the Canadian Prairies in the summer of 2014. The results indicate that with an increase in open water bodies, accumulated precipitation, peak precipitation amounts, and intensities decrease. Moreover, shifts are seen in times of peak for both precipitation amounts and intensities, in the order of increasing wetness. Additionally, with an increase in open water bodies, convective available potential energy decreases and convective inhibition increases, indicating suppression of forcing for convective precipitation.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.027
GPT teacher head0.256
Teacher spread0.229 · 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 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

Citations2
Published2016
Admission routes3
Has abstractyes

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