Development and validation of a size-resolved particle dry deposition scheme for application in aerosol transport models
Bibliographic record
Abstract
Abstract. A size-resolved particle dry deposition scheme is developed for inclusion in large-scale air quality and climate models where the size distribution and fate of atmospheric aerosols is of concern. The "resistance" structure is similar to what is proposed by Zhang et al. (2001), while a new "surface" deposition velocity (or surface resistance) is derived by simplification of a one-dimensional aerosol transport model (Petroff et al., 2008b, 2009). Compared to Zhang et al.'s model, the present model accounts for the leaf size, shape and area index as well as the height of the vegetation canopy. Consequently, it is more sensitive to the change of land covers, particularly in the accumulation mode (0.1–1 micron). A drift velocity is included to account for the phoretic effects related to temperature and humidity gradients close to liquid and solid water surfaces. An extended comparison of this model with experimental evidence is performed over typical land covers such as bare ground, grass, coniferous forest, liquid and solid water surfaces and highlights its adequate prediction. The predictions of the present model differ from Zhang et al.'s model in the fine mode, where the latter tends to over-estimate in a significant way the particle deposition, as measured by various investigators or predicted by the present model. The present development is thought to be useful to modellers of the atmospheric aerosol who need an adequate parameterization of aerosol dry removal to the earth surface, described here by 26 land covers. An open source code is available in Fortran90.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".