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Record W2781682963 · doi:10.5194/acp-18-11375-2018

Transport of Canadian forest fire smoke over the UK as observed by lidar

2018· article· en· W2781682963 on OpenAlexaboutno aff
G. Vaughan, Adam P. Draude, Hugo Ricketts, David M. Schultz, Mariana Adam, Jacqueline Sugier, D. P. Wareing

Bibliographic record

VenueAtmospheric chemistry and physics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
FundersNatural Environment Research CouncilMet OfficeEuropean Organization for the Exploitation of Meteorological SatellitesUniversity of ManchesterSight Research UKNational Aeronautics and Space Administration
KeywordsLidarCeilometerAerosolEnvironmental sciencePlumeSmokeAtmospheric sciencesTroposphereDepolarization ratioRange (aeronautics)MeteorologyRemote sensingGeologyGeographyMaterials science

Abstract

fetched live from OpenAlex

Abstract. Layers of aerosol at heights between 2 and 11 km were observed with Raman lidars in the UK between 23 and 31 May 2016. A network of these lidars, supported by ceilometer observations, is used to map the extent of the aerosol and its optical properties. Space-borne lidar profiles show that the aerosol originated from forest fires over western Canada around 17 May, and indeed the aerosol properties – weak volume depolarisation (<5 %) and a lidar ratio at 355 nm in the range 35–65 sr – were consistent with long-range transport of forest fire smoke. The event was unusual in its persistence – the smoke plume was drawn into an atmospheric block that kept it above north-western Europe for 9 days. Lidar observations show how the smoke layers became optically thinner during this period, but the lidar ratio and aerosol depolarisation showed little change. The results demonstrate the value of a dense network of observations for tracking forest fire smoke, and show how the dispersion of smoke in the free troposphere leads to the emergence of discrete thin layers in the far field. They also show how atmospheric blocking can keep a smoke plume in the same geographic area for over a week.

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 score0.189
Threshold uncertainty score0.993

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.009
GPT teacher head0.193
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.

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

Citations60
Published2018
Admission routes1
Has abstractyes

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