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Record W2948566727 · doi:10.3390/rs11111347

Parameterization of Urban Sensible Heat Flux from Remotely Sensed Surface Temperature: Effects of Surface Structure

2019· article· en· W2948566727 on OpenAlexaff
Jinxin Yang, Massimo Menenti, E. Scott Krayenhoff, Zhifeng Wu, Qian Shi, Xiaoying Ouyang

Bibliographic record

VenueRemote Sensing · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversity of Guelph
FundersGuangzhou University
KeywordsSensible heatEnvironmental scienceHeat fluxLatent heatRadiometerFlux (metallurgy)Atmospheric sciencesNadirRadiometryDaytimeMeteorologyMicroclimateUrban heat islandRemote sensingHeat transferMaterials scienceGeographySatelliteMechanicsGeologyPhysics

Abstract

fetched live from OpenAlex

Sensible heat exchange has important consequences for urban meteorology and related applications. Directional radiometric surface temperatures of urban canopies observed by remote sensing platforms have the potential to inform estimations of urban sensible heat flux. An imaging radiometer viewing the surface from nadir cannot capture the complete urban surface temperature, which is defined as the mean surface temperature over all urban facets in three dimensions, which includes building wall surface temperatures and requires an estimation of urban sensible heat flux. In this study, a numerical microclimate model, Temperatures of Urban Facets in 3-D (TUF-3D), was used to model sensible heat flux as well as radiometric and complete surface temperatures. Model data were applied to parameterize an effective resistance for the calculation of urban sensible heat flux from the radiometric (nadir view) surface temperature. The results showed that sensible heat flux was overestimated during daytime when the radiometric surface temperature was used without the effective resistance that accounts for the impact of wall surface temperature on heat flux. Parameterization of this additional resistance enabled reasonably accurate estimates of urban sensible heat flux from the radiometric surface temperature.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.004
GPT teacher head0.189
Teacher spread0.184 · 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 designBench or experimental
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

Citations14
Published2019
Admission routes1
Has abstractyes

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