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Record W2968406496 · doi:10.1029/2019jd030607

Satellite Limb Observations of Unprecedented Forest Fire Aerosol in the Stratosphere

2019· article· en· W2968406496 on OpenAlexaff
Adam Bourassa, Landon Rieger, Daniel Zawada, Sergey Khaykin, L. W. Thomason, D. A. Degenstein

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAerosolStratosphereAtmospheric sciencesOccultationEnvironmental scienceExtinction (optical mineralogy)SatelliteAltitude (triangle)Atmosphere (unit)Depth soundingLatitudeClimatologyMeteorologyGeologyGeographyPhysicsGeodesyAstrophysicsOceanographyMineralogyAstronomy

Abstract

fetched live from OpenAlex

Abstract Intense forest fires in western North America during August 2017 caused smoke plumes that reached the stratosphere. While this phenomenon has often been observed, this particular event caused increases in stratospheric aerosol extinction at higher altitudes with greater magnitude than previously observed in the satellite record. Here we use multiple satellite limb sounding observations, which provide high sensitivity to thin aerosol layers and good vertical resolution, to show that enhancements in aerosol extinction from the fires reached as high as 23 km in altitude and persisted for more than 5 months. Within 1 month, the aerosol is observed to cover latitudes from 20°N to 60°N, which is essentially the northernmost limit of the observations. At midlatitudes between 15‐ and 20‐km altitudes, the sustained level of median aerosol extinction measured at 750 nm increased by almost an order of magnitude, from approximately 10 −4 km −1 to nearly 10 −3 km −1 . Agreement between limb scatter and occultation measurements is generally within 20% despite potential bias due to modified aerosol shape and composition.

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)0.001
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 score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.300
Teacher spread0.252 · 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

Citations61
Published2019
Admission routes1
Has abstractyes

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