Driftsonde Observations to Evaluate Numerical Weather Prediction of the Late 2006 African Monsoon
Bibliographic record
Abstract
Abstract During the international African Monsoon Multidisciplinary Analysis (AMMA) project, stratospheric balloons carrying gondolas called driftsondes capable of dropping meteorological sondes were deployed over West Africa and the tropical Atlantic Ocean. The goals of the deployment were to test the technology and to study the African easterly waves, which are often the forerunners of hurricanes. Between 29 August and 22 September 2006, 124 sondes were dropped over the seven easterly waves that moved across Africa into the Atlantic between about 10° and 20°N, where almost no in situ vertical information exists. Conditions included waves that developed into Tropical Storm Florence and Hurricanes Gordon and Helene. In this study, a selection of numerical weather prediction model outputs has been compared with the dropsondes to assess the effect of some developments in data assimilation on the quality of analyses and forecasts. By comparing two different versions of the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) model of Météo-France with the dropsondes, first the benefits of the last data assimilation updates are quantified. Then comparisons are carried out using the ARPEGE model and the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecasts. It is shown that the two models represent very well the vertical structure of temperature and humidity over both land and sea, and particularly within the Saharan air layer, which displays humidity below 5%–10%. Conversely, the models are less able to represent the vertical structure of the meridional wind. This problem seems to be common to ARPEGE and IFS, and its understanding still requires further investigations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | medium |
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".