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Record W4246719960 · doi:10.5194/acp-2017-417

Comparative measurements of ambient atmospheric concentrations of ice nucleating particles using multiple immersion freezing methods and a continuous flow diffusion chamber

2017· preprint· en· W4246719960 on OpenAlexaff
Paul J. DeMott, Thomas C. J. Hill, Markus D. Petters, Allan K. Bertram, Yutaka Tobo, Ryan H. Mason, Kaitlyn J. Suski, Christina S. McCluskey, Ezra J. T. Levin, Gregory P. Schill, Yvonne Boose, Anne Marie Rauker, Anna J. Miller, J. Zaragoza, Katherine S. Rocci, Nicholas E. Rothfuss, Hans P. Taylor, John D. Hader, C. Chou, J. A. Huffman, Ulrich Pöschl, A. J. Prenni, Sonia M. Kreidenweis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsUniversity of British Columbia
FundersDivision of Atmospheric and Geospace SciencesJapan Society for the Promotion of Science
KeywordsAerosolImmersion (mathematics)Ice nucleusSupercoolingAmbient pressureEnvironmental scienceChemistryAnalytical Chemistry (journal)MeteorologyNucleationChromatographyMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract. A number of new measurement methods for ice nucleating particles (INPs) have been introduced in recent years, and it is important to address how these methods compare. Laboratory comparisons of instruments sampling major INP types are common, but few comparisons have occurred for ambient aerosol measurements exploring the utility, consistency and complementarity of different methods to cover the large dynamic range of INP concentrations that exists in the atmosphere. In this study, we assess the comparability of four offline immersion freezing measurement methods (Colorado State University Ice Spectrometer, IS; North Carolina State University Cold Stage, CS; National Institute for Polar Research Cryogenic Refrigerator Applied to Freezing Test, CRAFT; University of British Columbia Micro-Orifice Uniform Deposit Impactor – Droplet Freezing Technique, MOUDI-DFT) and an online method (continuous flow diffusion chamber, CFDC) used in a manner deemed to promote/maximize immersion freezing, for the detection of INP in ambient aerosols at different locations and in different sampling scenarios. We also investigated the comparability of different aerosol collection methods used with offline immersion freezing instruments. Excellent agreement between all methods could be obtained for several cases of co-sampling with perfect temporal overlap. Even for sampling periods that were not fully equivalent, the deviations between atmospheric INP number concentrations measured with different methods were mostly less than one order of magnitude. In some cases, however, the deviations were larger and not explicable without sampling and measurement artifacts. Overall, the immersion freezing methods seem to effectively capture INP that activate as single particles in the modestly supercooled temperature regime (> −20 °C), although more comparisons are needed in this temperature regime that is difficult to capture with online methods. Relative to the CFDC method, three immersion freezing methods that disperse particles into a bulk liquid (IS, CS, CRAFT) exhibit a positive bias in measured INP number concentrations at below −20 °C, increasing with decreasing temperature. This bias was present, but much less pronounced for a method that condenses separate water droplets onto limited numbers of particles prior to cooling and freezing (MOUDI-DFT). Potential reasons for the observed differences are discussed, and further investigations are required to elucidate the role of all factors involved.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.086
GPT teacher head0.332
Teacher spread0.245 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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