Evaluation of Candidate Predictors for Seasonal Precipitation Forecasting
Bibliographic record
Abstract
This research proposes to carry out a principal component analysis using the maximum covariance method, with the aim of finding the most robust spatio-temporal relationships between several candidate predictors and the accumulated monthly precipitation recorded in Cuba during the period 1980-2020. This process will make it possible to establish quantitative relationships that, together with theoretical considerations, make it possible to reduce the list of predictors to be used for the purpose of obtaining seasonal predictions. The values of the predictors are represented through monthly averages obtained from ERA5 reanalysis, while monthly accumulated precipitation data were obtained from a national-scope grid with 4 km of spatial resolution, used as predictand. The results obtained reflect the highest spatio-temporal correlation values with the first variability mode in all cases, indicating that the usual regime conditions are predominant and have a greater coupling with the precipitation variability in the analyzed temporal scale. In addition, they suggest that the candidates that explain the transport of moisture at low levels, as well as the gradients between the middle and lower troposphere, show the most robust associations. In the same way, the surface temperature of tropical Atlantic Sea, the flow related to Quasi-Biennial Oscillation and the thermodynamic indices, K Index and Galvez-Davison Index, present good degrees of association, for which reason they can be considered the most recommendable for carrying out forecasting experiments.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".