A new parameterization of ice heterogeneous nucleation coupled to aerosol chemistry in WRF-Chem model version 3.5.1: evaluation through the ISDAC measurements
Bibliographic record
Abstract
Abstract. In the Arctic, during polar night and early spring, ice clouds are separated into two leading types: (1) TIC1 clouds characterized by large concentration of very small crystals, and TIC2 clouds characterized by low concentration of large ice crystals. Using suitable parameterization of heterogeneous ice nucleation is essential for properly representing ice cloud in meteorological and climate model and subsequently understanding their interactions with aerosols and radiation. Here, we describe a new parameterization for ice crystals formation by heterogeneous nucleation coupled to aerosols chemistry in WRF-Chem. The parameterization is implemented in the Milbrandt and Yau’s two-moment cloud microphysics scheme and we assess how the WRF-Chem model responds to the real time interaction between chemistry and the new parameterization. Well-documented reference cases provided us in situ data from the spring 2008 Indirect and Semi-Direct Aerosol Campaign (ISDAC) campaign over Alaska. Our analysis reveals that the new parameterization clearly improves the representation of the IWC in polluted or unpolluted air masses and shows the poor performance of the reference parameterization in representing ice clouds with low IWC. The new parameterization is, thus able to represent TIC1 and TIC2 microphysical characteristics at the top of the clouds were heterogeneous ice nucleation is most likely occurring even knowing the bias of simulated aerosols by WRF-Chem over Arctic.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".