Multi-Layered Cloud Distribution Over Tropical Station Using Radiosonde Humidity Observations and CloudSat Measurements
Bibliographic record
Abstract
Owing to its importance of role played by multi-layered clouds in climate of earth’s atmosphere, a decadal observation (January, 2000 to December, 2009) from India Meteorological Department, Trivandrum regular radiosonde (00 & 12 UTC) ascents and CloudSat observations were used to study the distribution of multi-layered cloud formation at this location. Both the ground and space based observations at Trivandrum locations shows the more or less same percentage of occurrence of single-, double-, three-, four- and five-layered clouds. The important findings are: Radiosonde derived cloud ‐free cases and one to five cloud layers account for 30.63%, 42.51%, 19.76%, 5.85%, 1.08%, and 0.16% all cases, respectively, whereas CloudSat shows 47.17%, 24.74%, 6.41%, 1.81%, 0.13% of the total samples, respectively. In general, the thickness of cloud layers does not change much from summer to winter. However, the occurrences of multi-layered clouds are more frequent in the summer. Further, keeping the CloudSat limitations in view, an attempt is made to evaluate the CloudSat observations using radiosonde measurements and which has the great potential for studying the multi-layered cloud structures over the globe and important in climate point of view.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".