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Record W2486303733 · doi:10.1002/2016jd025309

Radiative characteristics of clouds embedded in smoke derived from airborne multiangular measurements

2016· article· en· W2486303733 on OpenAlexaboutno aff
Ritesh Gautam, Charles K. Gatebe, Manoj K. Singh, Tamás Várnai, Rajesh Poudyal

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
FundersH2020 European Research CouncilNational Aeronautics and Space Administration
KeywordsRadiative transferEnvironmental scienceSmokeAtmospheric sciencesSingle-scattering albedoCloud topSatelliteAerosolRemote sensingAtmospheric radiative transfer codesTroposphereCloud albedoOptical depthCirrusCloud coverAlbedo (alchemy)RadiometerMeteorologyCloud computingPhysicsGeologyAstronomyOptics

Abstract

fetched live from OpenAlex

Abstract Clouds in the presence of absorbing aerosols result in their apparent darkening, observed at the top of atmosphere (TOA), which is associated with the radiative effects of aerosol absorption. Owing to the large radiative effect and potential impacts on regional climate, above‐cloud aerosols have recently been characterized in multiple satellite‐based studies. While satellite data are particularly useful in showing the radiative impact of above‐cloud aerosols at the TOA, recent literature indicates large uncertainties in satellite retrievals of above‐cloud aerosol optical depth (AOD) and single scattering albedo (SSA), which are among the most important parameters in the assessment of associated radiative effects. In this study, we analyze radiative characteristics of clouds in the presence of wildfire smoke using airborne data primarily from NASA's Cloud Absorption Radiometer, collected during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites campaign in Canada during the 2008 summer season. We found a strong positive reflectance (R) gradient in the UV‐visible (VIS)‐near infrared (NIR) spectrum for clouds embedded in dense smoke, as opposed to an (expected) negative gradient for cloud‐free smoke and a flat spectrum for smoke‐free cloud cover. Several cases of clouds embedded in thick smoke were found, when the aircraft made circular/spiral measurements, which not only allowed the complete characterization of angular distribution of smoke scattering but also provided the vertical distribution of smoke and clouds (within 0.5–5 km). Specifically, the largest darkening by smoke was found in the UV/VIS, with R0.34μm reducing to 0.2 (or 20%), in contrast to 0.8 at NIR wavelengths (e.g., 1.27 µm). The observed darkening is associated with large AODs (0.5–3.0) and moderately low SSA (0.85–0.93 at 0.53 µm), resulting in a significantly large instantaneous aerosol forcing efficiency of 254 ± 47 W m−2 τ−1. Our observations of smoke‐cloud radiative interactions were found to be physically consistent with theoretical plane‐parallel 1‐D and Monte Carlo 3‐D radiative transfer calculations, capturing the observed gradient across UV‐VIS‐NIR. Results from this study offer insights into aerosol‐cloud radiative interactions and may help in better constraining satellite retrieval algorithms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.046
GPT teacher head0.313
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2016
Admission routes1
Has abstractyes

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