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Record W3129827918 · doi:10.5194/gmd-14-961-2021

The Vertical City Weather Generator (VCWG v1.3.2)

2021· article· en· W3129827918 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeoscientific model development · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaMitacsEidgenössische Technische Hochschule ZürichAlbert-Ludwigs-Universität FreiburgGovernment of OntarioCentro de Investigaciones Energéticas, Medioambientales y TecnológicasOntario Centres of ExcellenceUniversity of Guelph
KeywordsWind speedMicroclimateEnvironmental scienceMeteorologyTurbulence kinetic energyAtmospheric sciencesHumidityTurbulencePlanetary boundary layerRelative humidityWind directionMean squared errorMomentum (technical analysis)DiffusionMathematicsGeographyGeologyPhysicsStatisticsThermodynamics

Abstract

fetched live from OpenAlex

Abstract. The Vertical City Weather Generator (VCWG) is a computationally efficient urban microclimate model developed to predict temporal and vertical variation of potential temperature, wind speed, specific humidity, and turbulent kinetic energy. It is composed of various sub-models: a rural model, an urban vertical diffusion model, a radiation model, and a building energy model. Forced with weather data from a nearby rural site, the rural model is used to solve for the vertical profiles of potential temperature, specific humidity, and friction velocity at 10 m a.g.l. The rural model also calculates a horizontal pressure gradient. The rural model outputs are applied to a vertical diffusion urban microclimate model that solves vertical transport equations for potential temperature, momentum, specific humidity, and turbulent kinetic energy. The urban vertical diffusion model is also coupled to the radiation and building energy models using two-way interaction. The aerodynamic and thermal effects of urban elements, surface vegetation, and trees are considered. The predictions of the VCWG model are compared to observations of the Basel UrBan Boundary Layer Experiment (BUBBLE) microclimate field campaign for 8 months from December 2001 to July 2002. The model evaluation indicates that the VCWG predicts vertical profiles of meteorological variables in reasonable agreement with the field measurements. The average bias, root mean square error (RMSE), and R2 for potential temperature are 0.25 K, 1.41 K, and 0.82, respectively. The average bias, RMSE, and R2 for wind speed are 0.67 m s−1, 1.06 m s−1, and 0.41, respectively. The average bias, RMSE, and R2 for specific humidity are 0.00057 kg kg−1, 0.0010 kg kg−1, and 0.85, respectively. In addition, the average bias, RMSE, and R2 for the urban heat island (UHI) are 0.36 K, 1.2 K, and 0.35, respectively. Based on the evaluation, the model performance is comparable to the performance of similar models. The performance of the model is further explored to investigate the effects of urban configurations such as plan and frontal area densities, varying levels of vegetation, building energy configuration, radiation configuration, seasonal variations, and different climate zones on the model predictions. The results obtained from the explorations are reasonably consistent with previous studies in the literature, justifying the reliability and computational efficiency of VCWG for operational urban development projects.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.020
GPT teacher head0.210
Teacher spread0.190 · 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