Interactions between convection and the background atmosphere during high rain events: observations and comparisons with models
Bibliographic record
Abstract
The thesis consists of three projects. Each of these projects is a diagnostic study of the interaction between strong convective events and the background atmosphere. In all projects, we use a satellite rainfall dataset to identify strong rain events. We then use radiosonde soundings to generate composite anomaly patterns of meteorological variables about the strong rain events.\nIn Project 1, we examine temperature, relative humidity, and divergence anomalies about strong convective events in the Western Tropical Pacific. A low-level convergence coupled to a midlevel divergence develops prior to peak rainfall. A midlevel convergence coupled to a low-level divergence develops after peak rainfall. Strong surface cold pools develop in response to high rainfall. Observations were compared to models and reanalyses. In general, models and reanalyses do not fully represent the timing, strength, and altitude of the mid-level convergence and divergence features. The surface cold anomaly is also underestimated in models. These discrepancies suggest that the mesoscale downward transport of mid-level air into the boundary layer in models may be too weak.\nIn Project 2, we investigate the impact of convection on the background distribution of a chemical tracer (ozone). Negative ozone anomalies and higher frequency of midlevel cloud tops occur in a layer between 3 and 8 km prior to peak rainfall. Negative ozone anomalies in the upper troposphere develop in response to high rainfall. Chemistry transport model simulations also exhibit negative ozone anomalies at upper and midlevels. However, the ozone anomalies in the model are symmetric about peak rainfall and are more persistent than observations. \nIn Project 3, we identify regional variations in the interaction between convection and the background atmosphere. In all four regions, deep convection imposes cooling in the lower and warming in the upper troposphere. In mid-latitudes, convection is associated with stronger anomalies in surface pressure, geopotential height, and CAPE. Over land, a low-level warm anomaly develops prior to peak rainfall and the surface cold pool that develops during peak rainfall is more persistent. The PV generated prior to peak rainfall, is advected towards the surface after peak rainfall and may play a role in hurricane genesis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".