Classification of Large-Scale Environments that drive the formation of Mesoscale Convective Systems over Southern West Africa
Bibliographic record
Abstract
Abstract. Mesoscale convective systems (MCSs) are frequently observed over southern West Africa (SWA) throughout most of the year. However, it has not yet been identified what variations in typical large-scale environments of the West African monsoon seasonal cycle may favour MCS occurrence in this region. Here, six distinct synoptic states are identified and are further associated with being either a dry season, pre-, post-, or peak-monsoon synoptic circulation type using self organizing maps (SOMs) with inputs from reanalysis data. We identified a pronounced annual cycle of MCS numbers with frequency peaks in June and September which can be associated with peak rainfall during the major and minor rainy seasons respectively across SWA. Comparing daily MCS frequencies, MCSs are most likely to develop during post-monsoon conditions featuring a northward-displaced moisture anomaly (0.42 MCSs per day), which can be linked to strengthened low-level westerlies. Considering that these post-monsoon conditions occur predominantly from September and into November, these patterns may in some cases be representative of a delayed monsoon retreat. On the other hand, under peak monsoon conditions, we observe easterly wind anomalies during MCS days, which reduce moisture content over the Sahel but introduce more moisture over the coast. Finally, we find all MCS-day synoptic states to exhibit positive shear anomalies. Seasons with the strongest shear anomalies are associated with the strongest low-level temperature anomalies to the north of SWA, highlighting that a warmer Sahel can promote MCS-favourable conditions in SWA. These significant positive zonal shear anomalies for MCS days illustrate the importance of shear for MCS development in SWA throughout the year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".