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Record W4210924915 · doi:10.3389/fmars.2021.747083

Dynamics of Probable Maximum Precipitation Within Coastal Urban Areas in a Convection-Permitting Regional Climate Model

2022· article· en· W4210924915 on OpenAlexafffundabout
Ju Liang, Yangyang Yong

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersNatural Science Foundation of Guangxi ProvinceCompute Canada
KeywordsPrecipitationEnvironmental scienceClimatologySnowClimate modelConvectionAtmospheric sciencesClimate changeMeteorologyGeologyOceanographyGeography

Abstract

fetched live from OpenAlex

The west coast of Canada is strongly affected by the extreme precipitation events triggered by frequent atmospheric river (AR) activities over the eastern North Pacific. Across the region, assessing the probable maximum precipitation (PMP), can provide valuable information for resilience building of the coastal communities that are vulnerable to hydrological risks. In this study, a 3-km convection-permitting regional climate model is used to physically estimate the PMP in Vancouver. This technique maximizes the effect of AR-related water vapor transport by spatially adjusting the lateral boundary conditions (LBCs) of the model simulations for the selected AR-related extreme precipitation events. The PMP in Vancouver is identified among the simulations driven by the spatially adjusted LBCs that are corresponding with the AR-induced “worst-case scenario,” i.e., landfalling ARs hit Vancouver with optimal landfalling location and transport direction. Results suggest that the PMP in Vancouver, in terms of the maxima of the regionally averaged 72-h total precipitation for the historical extreme precipitation events, is up to 790 mm, which is 130% greater than the historical peak precipitation for the period 1980∼2017. On average, all the PMP simulations shows an overall increase by 81% in precipitation by relative to historical simulations. In addition, the PMP simulations suggested an overall decrease in snowfall by 12% due to the warmer near-surface air temperature; however, a pronounced increase in freezing rain is seen. The precipitation increase for the estimated PMP relative to the historical extreme precipitation is closely associated with the increased atmospheric moisture transport and the changes in the atmospheric dynamic factors when the AR effects are maximized. These include the enhanced low-tropospheric ascent and moisture transport convergence, which can induce stronger depletion of atmospheric moistures as indicated by the increased precipitation efficiency.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.588

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.221
Teacher spread0.209 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

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