CHAMP observations of global gravity wave fields in the troposphere and stratosphere
Bibliographic record
Abstract
This paper presents the analysis of 56 months of CHAMP (Challenging Minisatellite Payload) RO (radio occultation) temperature profiles to study the global gravity wave fields in the troposphere and lower stratosphere. The relatively new GPS (Global Positioning System) RO technique is suitable for global monitoring and provides high vertical resolution, long‐term stability, and opportunity of all‐weather viewing. The CHAMP data set analyzed in this paper is the largest RO data set used for the investigation of global structure of gravity waves. The analysis also makes use of the global radiosonde data provided by the British Atmospheric Data Centre (BADC). First, the global winter temperature pattern and temperature fluctuations in the troposphere and lower stratosphere are discussed. The global structure of gravity wave variances in the upper troposphere and lower stratosphere for the summer and winter seasons indicate the dominance in the tropical region. The analysis reveals that the maximum potential energy (Ep) is observed around the equator with considerable longitudinal variation. Comparison of the wave energy derived from the CHAMP and radiosonde data sets for the low‐latitude region (20°S–20°N) shows good similarity. The observed vertical wave number power spectra were compared with a model spectrum. The low‐latitude power spectra show considerable similarities with the model spectrum. However, the disparity increases in the midlatitude or high‐latitude sectors; particularly, in the high latitudes the observed spectral power is much underestimated for longer wavelengths. The results demonstrate the usefulness of GPS RO data in determining the global gravity wave fields.
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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) | 0.001 | 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".