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Record W1551625515 · doi:10.2172/947999

Science Overview Document Indirect and Semi-Direct Aerosol Campaign (ISDAC) April 2008

2007· report· en· W1551625515 on OpenAlexaboutno aff
SJ Ghan, B. Schmid, J. H. Hubbe, C. Flynn, Alexander Laskin, AA Zelenyuk, DJ Czizco, CN Long, Greg M. McFarquhar, Johannes Verlinde, Jerry Y. Harrington, JW Strapp, P Liu, Alexei Korolev, Alan G. McDonald, Mengistu Wolde, Ann M. Fridlind, T. J. Garrett, G. G. Mace, G. Kok, Sarah D. Brooks, Don Collins, Dan Lubin, P. Lawson, Manvendra K. Dubey, Cláudio Mazzoleni, Matthew D. Shupe, Shaocheng Xie, DD Turner, Qilong Min, EJ Mlawer, David L. Mitchell

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
Fundersnot available
KeywordsAerosolCloud condensation nucleiEnvironmental scienceLiquid water contentEffective radiusCloud computingMeteorologyAtmospheric sciencesCloud physicsNephelometerDrizzleGeographyPhysicsPrecipitationScatteringLight scatteringComputer scienceOptics

Abstract

fetched live from OpenAlex

The ARM Climate Research Facility’s (ACRF) Aerial Vehicle Program (AVP) will deploy an intensive cloud and aerosol observing system to the ARM North Slope of Alaska (NSA) locale for a five week Indirect and Semi-Direct Aerosol Campaign (ISDAC) during period 29 March through 30 April 2008. The deployment period is within the International Polar Year, thus contributing to and benefiting from the many ancillary observing systems collecting data synergistically. We will deploy the Canadian National Research Council Convair 580 aircraft to measure temperature, humidity, total particle number, aerosol size distribution, single particle composition, concentrations of cloud condensation nuclei and ice nuclei, optical scattering and absorption, updraft velocity, cloud liquid water and ice contents, cloud droplet and crystal size distributions, cloud particle shape, and cloud extinction. In addition to these aircraft measurements, ISDAC will deploy two instruments at the ARM site in Barrow: a spectroradiometer to retrieve cloud optical depth and effective radius, and a tandem differential mobility analyzer to measure the aerosol size distribution and hygroscopicity. By using many of the same instruments used during Mixed-Phase Arctic Cloud Experiment (M-PACE), conducted in October 2004, we will be able to contrast the arctic aerosol and cloud properties during the fall and spring transitions. The aerosol measurements can be used in cloud models driven by objectively analyzed boundary conditions to test whether the cloud models can simulate the aerosol influence on the clouds. The influence of aerosol and boundary conditions on the simulated clouds can be separated by running the cloud models with all four combinations of M-PACE and ISDAC aerosol and boundary conditions: M-PACE aerosol and boundary conditions, M-PACE aerosol and ISDAC boundary conditions, ISDAC aerosol and M-PACE boundary conditions, and ISDAC aerosol and boundary conditions. ISDAC and M-PACE boundary conditions are likely to be very different because of the much more extensive ocean water during M-PACE. The uniformity of the surface conditions during ISDAC greatly simplifies the objective analysis (surface fluxes and precipitation are very weak), so that it can largely rely on the European Centre for Medium-Range Weather Forecasts analysis. The aerosol measurements can also be used as input to the cloud models and to evaluate the aerosol retrievals. By running the cloud models with and without solar absorption by the aerosols, we can determine the semidirect effect of the aerosol on the clouds.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.033
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.019

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 head0.027
GPT teacher head0.287
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2007
Admission routes1
Has abstractyes

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