The June–September Low Cloud Cover in Western Central Africa: Mean Spatial Distribution and Diurnal Evolution, and Associated Atmospheric Dynamics
Bibliographic record
Abstract
Abstract Western central Africa (WCA) was recently shown to be one of the cloudiest areas of the tropics. Analyzing an ensemble of satellite products and surface cloud observations, we show that in June–September, WCA cloud cover is dominated by single-layered low stratiform clouds. Despite an underestimation of low cloud frequency in satellite estimates at night, comparisons with surface observations bring insights into the spatial distribution and diurnal cycle of low clouds. Both appear strongly influenced by orography: to the west, the coastal plains and the ocean-facing valleys have the largest cloud cover and a lower-amplitude diurnal cycle with a maximum cloud phase at 0400 local time (LT). To the east, across the windward slopes, plateaus, and downwind slopes, the cloud cover becomes progressively reduced and the diurnal cycle has a larger amplitude with a maximum cloud phase at 1000 LT. In terms of atmospheric dynamics, the east/west gradient observed in low cloud frequency and amount is related to a foehn effect without substantial rainfall on windward slopes. The diurnal cycle of low clouds on the windward slopes and plateaus is related to the reversal, from mean subsidence at 0700 LT over the Atlantic and inland to rising motion inland at 1300 LT. In addition, the airmass stability in low levels prevents the vertical development of cloud cover. Last, we could not detect in the European reanalyses any nocturnal jet as observed in southern West Africa (SWA), suggesting different mechanisms triggering low cloud formation in WCA compare to SWA.
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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".