Reply on AC1
Bibliographic record
Abstract
Abstract. A historic rainstorm occurred over Henan, China in July 2021 ("7.20" extreme precipitation event), resulting in significant human casualties and socio-economic losses. A global variable-resolution model (MPAS-Atmosphere v7.3) was employed to simulate this extreme precipitation event, by bridging the hydrostatic and non-hydrostatic scales together. A series of simulations have been done at both quasi-uniform (60 km and 15 km) and variable-resolution meshes (60–15 km and 60–3 km). For the 48-hour peak precipitation duration (07/20–07/22), the 60–3 km variable-resolution simulation coupled with the scale-aware convection-permitting parameterization scheme suite stands out predominately among other simulation experiments as it reproduces this extreme precipitation event most accurately, in terms of both the intensity and location of the peak precipitation. At 15-km resolution, the 60–15 km variable-resolution simulation achieves comparable forecasting skills as the 15-km quasi-uniform simulation, but at a much reduced computing cost. In addition, at 15-km resolution, we found that the default mesoscale suite generally outperforms the convection-permitting suite at 15-km resolution as simulations coupled with convection-permitting suite missed the 3rd peak of this extreme precipitation event while the mesoscale suite did not. This implies that, when the resolution of the refined region is coarser than the cloud-resolving scale, the convection-permitting parameterization scheme suite does not necessarily work better than the default mesoscale suite, but once the refined mesh is close to the cloud-resolving scale, the convection-permitting suite becomes scale aware such that it can intelligently distinguish the convective precipitation and grid-scale precipitation, respectively. Finally, it is found that the large-scale wind field plays a vital role in affecting extreme precipitation simulations since it primarily influences the transport of the water vapor flux thereby altering the prediction of the precise peak precipitation location.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.287 | 0.182 |
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".