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Record W4247750313 · doi:10.5194/acpd-10-2503-2010

Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain

2010· preprint· en· W4247750313 on OpenAlexaff
X. Wang, L. Zhang, Michael D. Moran

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsEnvironment and Climate Change Canada
FundersHelsingin Yliopisto
KeywordsScavengingParticle sizeParticle-size distributionParticle (ecology)Current (fluid)Environmental scienceMagnitude (astronomy)Terminal velocityAerosolMeteorologyField (mathematics)MechanicsParticle numberAtmospheric sciencesComputational physicsPhysicsMathematicsThermodynamicsChemistryGeology

Abstract

fetched live from OpenAlex

Abstract. A detailed review has been conducted of current size-resolved parameterizations of below-cloud scavenging by rain, including their formulation in terms of scavenging coefficient (Λ), their associated input parameters and comparisons with size-resolved Λ values obtained from field measurements. The three dominant factors in the theoretical formulations of Λ – raindrop-particle collection efficiency, raindrop number size distribution and raindrop terminal fall velocity – are investigated through numerical sensitivity tests. It is found that the use of different formulations for raindrop-particle collection efficiency can cause uncertainties in the Λ values of nearly one order of magnitude for particles smaller than 3 μm. The use of different formulations of raindrop number size distribution can cause the Λ values to vary by a factor of 3 to 5 for all particle sizes. The uncertainty in Λ, caused by the use of different droplet terminal velocity formulations, is generally smaller than a factor of 2. All of the current theoretical Λ parameterizations, however, underpredict the Λ values by one to two orders of magnitude for particles smaller than 3 μm, compared with most available field measurements or with empirical formulas generated from field observations. The combined uncertainties from known sources are, thus, not enough to explain the large discrepancies between the theoretical and experimental studies, suggesting a need for further investigations of the collection mechanisms through field, laboratory and numerical studies. The differences in the predicted particle concentrations, due to the use of different Λ parameterizations, can be larger than a factor of 10 for ultrafine and coarse particles even after a small amount of rain (e.g., 2–5 mm). The differences for submicron-sized particles can also be larger than a factor of 2 if sufficient rainfall occurs. Lastly, predicted bulk concentrations (integrated over the particle size distribution) from using different theoretical and empirical Λ parameterizations can differ by up to 50% for particle number and by up to 25% for particle mass after just 2–5 mm of rain.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.305
Teacher spread0.261 · 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 designSimulation or modeling
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

Citations6
Published2010
Admission routes1
Has abstractyes

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