Testing convective transport on short time scales: Comparisons with mass divergence and ozone anomaly patterns about high rain events
Bibliographic record
Abstract
High rain events in the western tropical Pacific were identified using the Tropical Rainfall Measuring Mission (TRMM) 3B42 gridded rainfall data set. Horizontal wind measurements from radiosonde arrays, cloud top frequency measurements from CALIPSO, and ozonesonde measurements from SHADOZ were used to construct anomaly patterns of divergence, cloud top frequency, and ozone mixing ratio about the high rain events. The observed divergence anomaly pattern was compared with patterns produced by the Goddard Earth Observing System version 4 (GEOS‐4) and GEOS‐5 assimilated meteorological data sets. The midlevel (∼6 km) divergence dipole seen in observations, consisting of a local maximum in divergence 8 h before peak rainfall and a local maximum in convergence 8 h after peak rainfall, is not represented in the GEOS‐4 or GEOS‐5 meteorology. The ozone anomaly pattern shows ozone decreases at midlevels (3–8 km) up to 16 h prior to peak rainfall. These decreases occur in association with increases in the frequency of midlevel cloud tops and are due to increased detrainment from cumulus congestus clouds. The observed ozone anomaly pattern was compared with anomaly patterns produced by the GEOS‐Chem chemical transport model, driven by GEOS‐4 and GEOS‐5 assimilated winds and temperatures. The GEOS‐Chem simulations also exhibit negative ozone anomalies at midlevels in association with high rain events. However, these anomalies are nearly symmetric about peak rainfall, rather than strongest prior to peak rainfall. The upper level negative ozone anomalies of the models are more persistent than the observations. These results help characterize some of the difficulties of meteorological data sets in capturing the layered character of tropical convective outflow and its timing with respect to high rain events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".