A new analysis of variability and predictability of seasonal rainfall of central southern Africa for 1950–94
Bibliographic record
Abstract
Abstract Using wavelet analysis and wavelet‐based empirical orthogonal function analysis on scale‐averaged‐wavelet power and individual scale power, we identified the non‐stationary sea‐surface temperature (SST) fields of the South Atlantic and Indian Oceans that are associated with coherent regions of rainfall variability in central southern Africa (CSA). The dominant mode of CSA rainfall is out of phase between the coastal areas and the centre of CSA and has been decreasing consistently since 1970. The frequencies associated with this mode are between 2–2.4 and 5.6–8 years. The Benguela ocean current SSTs form the dominant spatial pattern of the South Atlantic Ocean, and the Brazil and Guinea ocean current SSTs form the second leading mode. The Benguela spatial patterns were found to migrate seasonally between Africa's west coast and South America's east coast. The northern Indian Ocean SST forms the leading mode of variability, followed by the south Indian Ocean SST. Using predictor fields identified from both oceans, we achieved encouraging results of predicted CSA rainfall using a non‐linear statistical teleconnection artificial neural network–genetic algorithm model. At 3 month lead time, correlations of between 0.8 and 0.9, root‐mean‐square errors of between 0.4 and 0.9 and Hansen Kuipers skill scores of between 0.4 and 0.8 were obtained between observed and predicted CSA rainfall. Copyright © 2004 Royal Meteorological Society
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 | 0.001 |
| 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".