AVHRR warm‐season cloud climatologies under various synoptic regimes across the Iberian Peninsula and the Balearic Islands
Bibliographic record
Abstract
ABSTRACT In this study we retrieved the spatial distribution of mid‐afternoon clouds under various synoptic regimes across the Iberian Peninsula and the Balearic Islands for the warm/convective‐season, from May to October. Accurate daily cloud masks were derived by applying a daytime over land multispectral convective cloud detection algorithm spanning 15 years (1997–2011) of Advanced Very High Resolution Radiometer ( AVHRR ) HRPT data. We processed a total of 2094 afternoon overpasses (between 1230 and 1720 UTC ) corresponding to the NOAA ‐14, NOAA ‐16 and NOAA ‐18 spacecrafts, and stratified daily cloud masks as a function of: (1) the automated circulation‐typing scheme of Jenkinson and Collinson and (2) the prevailing wind field at 850 hPa . The AVHRR warm‐season cloud climatology with high spatial resolution (1.1‐km) identified six representative areas (regions of interest; ROIs ) with intensified cloud activity (hot spots). The results also revealed the typical spatial distribution of clouds for each synoptic regime across the whole region, identified the synoptic patterns and wind regimes under which high amounts of clouds occur for each ROIs , and showed that strong boundary layer winds in general increase the frequency of clouds. The regional cloud climatology presented here could be useful, e.g. to improve convective short‐term forecasting by identifying active cloud areas for each atmospheric type.
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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 | 0.001 | 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 | 0.001 | 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".