Vertical Characteristics of Reflectivity in Intense Convective Clouds using TRMM PR Data
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Bibliographic record
Abstract
Tropical Rainfall Measuring Mission Precipitation Radar (TRMM-PR) based vertical structure in intense convective precipitation is presented here for Indian and Austral summer monsoon seasons. TRMM 2A23 data is used to identify the convective echoes in PR data. Two types of cloud cells are constructed here, namely intense convective cloud (ICC) and most intense convective cloud (MICC). ICC consists of PR radar beams having Ze>=40 dBZ above 1.5 km in convective precipitation area, whereas MICC, consists of maximum reflectivity at each altitude in convective precipitation area, with at least one radar pixel must be higher than 40 dBZ or more above 1.5 km within the selected areas. We have selected 20 locations across the tropics to see the regional differences in the vertical structure of convective clouds. One of the important findings of the present study is identical behavior in the average vertical profiles in intense convective precipitation in lower troposphere across the different areas. MICCs show the higher regional differences compared to ICCs between 5-12 km altitude. Land dominated areas show higher regional differences and Southeast south America (SESA) has the strongest vertical profile (higher Ze at higher altitude) followed by Indo-Gangetic plain (IGP), Africa, north Latin America whereas weakest vertical profile occurs over Australia. Overall SESA (41%) and IGP (36%) consist higher fraction of deep convective clouds (>10 km), whereas, among the tropical oceanic areas, Western (Eastern) equatorial Indian ocean consists higher fraction of low (high) level of convective clouds. Nearly identical average vertical profiles over the tropical oceanic areas, indicate the similarity in the development of intense convective clouds and useful while considering them in model studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it