Study of Atmospheric Variables using Low-Cost Stratospheric Balloon-Borne Missions
Bibliographic record
Abstract
A better understanding of atmospheric dynamics and improvement of regional weather and climate models require accurate measurement and analysis of atmospheric variables such as temperature, pressure, and wind velocity across altitudes. In this study, we present such results from a series of high-altitude balloon missions conducted by the Indian Centre for Space Physics (ICSP). These missions, in which balloons reach up to altitudes of ~42 km, provide high-resolution vertical profiles of atmospheric parameters over the Indian subcontinent, a region where such data are sparse. We analyze the payload's vertical ascent rates, horizontal displacements, and variations in some atmospheric parameters, such as temperature, pressure, and wind velocity with altitude. Wind velocity components—zonal (east-west) and meridional (north-south)—are also examined, with particular emphasis on their seasonal variability due to subtropical jet streams during pre- and post-monsoon periods. Our analysis reveals significant seasonal variation in wind patterns at stratospheric heights. We obtain clear indications that the atypical wind behaviors observed in 2019 may be linked to anomalies in monsoonal rainfall patterns. These results contribute valuable insights into upper atmospheric dynamics over the Indian region and also highlight the importance of balloon-borne observations in refining regional atmospheric models.
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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 | 0.001 | 0.002 |
| 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".