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Record W1571688942 · doi:10.2172/1035864

Parameterization of the Extinction Coefficient in Ice and Mixed-Phase Arctic Clouds during the ISDAC Field Campaign

2012· report· en· W1571688942 on OpenAlexaff
Alexei Korolev, A Shashkov, Howard W. Barker

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsExtinction (optical mineralogy)Parametrization (atmospheric modeling)Ice crystalsScale (ratio)Cloud computingPhase (matter)Molar absorptivityRadiative transferRemote sensingIce cloudEnvironmental scienceComputational physicsMeteorologyPhysicsOpticsComputer scienceGeology

Abstract

fetched live from OpenAlex

This report documents the history of attempts to directly measure cloud extinction, the current measurement device known as the Cloud Extinction Probe (CEP), specific problems with direct measurement of extinction coefficient, and the attempts made here to address these problems. Extinction coefficient is one of the fundamental microphysical parameters characterizing bulk properties of clouds. Knowledge of extinction coefficient is of crucial importance for radiative transfer calculations in weather prediction and climate models given that Earth's radiation budget (ERB) is modulated much by clouds. In order for a large-scale model to properly account for ERB and perturbations to it, it must ultimately be able to simulate cloud extinction coefficient well. In turn this requires adequate and simultaneous simulation of profiles of cloud water content and particle habit and size. Similarly, remote inference of cloud properties requires assumptions to be made about cloud phase and associated single-scattering properties, of which extinction coefficient is crucial. Hence, extinction coefficient plays an important role in both application and validation of methods for remote inference of cloud properties from data obtained from both satellite and surface sensors (e.g., Barker et al. 2008). While estimation of extinction coefficient within large-scale models is relatively straightforward for pure water droplets, thanks to Mie theory, mixed-phase and ice clouds still present problems. This is because of the myriad forms and sizes that crystals can achieve, each having their own unique extinction properties. For the foreseeable future, large-scale models will have to be content with diagnostic parametrization of crystal size and type. However, before they are able to provide satisfactory values needed for calculation of radiative transfer, they require the intermediate step of assigning single-scattering properties to particles. The most basic of these is extinction coefficient, yet it is rarely measured directly, and therefore verification of parametrizations is difficult. The obvious solution is to be able to measure microphysical properties and extinction at the same time and for the same volume. This is best done by in situ sampling by instruments mounted on either balloon or aircraft. The latter is the usual route and the one employed here. Yet the problem of actually measuring extinction coefficient directly for arbitrarily complicated particles still remains unsolved.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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