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Record W3200128736 · doi:10.3390/atmos12091181

Model Sensitivity Study of the Direct Radiative Impact of Saharan Dust on the Early Stage of Hurricane Earl

2021· article· en· W3200128736 on OpenAlexafffund
Jianyu Liang, Yongsheng Chen, Avelino F. Arellano, Abdulla Al Mamun

Bibliographic record

VenueAtmosphere · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsEnvironment and Climate Change CanadaYork University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Science and Technology of ChinaEnvironment and Climate Change CanadaYork UniversityCompute Canada
KeywordsWeather Research and Forecasting ModelAfrican easterly jetAtmospheric sciencesAerosolRadiative transferEnvironmental scienceClimatologyTropical cycloneTropical waveMineral dustMeteorologyGeologyPhysics

Abstract

fetched live from OpenAlex

Current studies report inconsistent results about the impacts of Saharan dust on the development of African Easterly Waves (AEWs), the African Easterly Jet (AEJ), and tropical cyclones (TCs). We present a modeling case study to further elucidate the direct radiative impacts of dust on the early development stage of a TC. We conducted experiments using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem-V3.9.1) to simulate Hurricane Earl (2010) which was influenced by the dusty Saharan Air Layer (SAL). We used the aerosol product from ECMWF MACC-II as the initial and boundary conditions to represent aerosol distribution, along with typical model treatment of its radiative and microphysical effects in WRF. Our simulations at 36-km resolution show that, within the first 36 h, the presence of dust weakens the low-pressure system over North Africa by less than 1 hPa and reduces its mean temperature by 0.03 K. Dust weakens and intensifies the AEJ at its core and periphery, respectively, with magnitudes less than 0.2 m/s. Dust slightly shifts the position of 600 hPa AEW to the south and reduces its intensity prior to impacting the TC. Finally, TC with dust remains weaker.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score1.000

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)0.0010.000

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 head0.030
GPT teacher head0.264
Teacher spread0.234 · 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.

Study designObservational
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

Citations6
Published2021
Admission routes2
Has abstractyes

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