Seasonal Precipitation Forecast Using an Ensemble of Artificial Neural Networks and Climate Oscillation Indices. A Case Study of Ceará, northeastern Brazil.
Why this work is in the frame
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Bibliographic record
Abstract
This research assesses the deterministic and probabilistic skill of an Artificial Neural Networks ensemble (EANN) for a 1-month-lead precipitation forecast. The EANN employs low-frequency climate oscillation indices to predict precipitation in the Brazilian state of Ceará, a key region for climate forecasting studies due to its high seasonal predictability. Additionally, a combination of the EANN and dynamical models into a hybrid multi-model ensemble (MME) is proposed. The EANN's forecasting ability is compared to a Multiple Linear Regression, a Multinomial Logistic Regression and North American Multi-Model Ensemble (NMME) models through leave-one-out cross-validation based on 40 years of data. A spatial comparison showed that the EANN was among the models with the highest deterministic and probabilistic accuracy, except in the southern region of the state. Moreover, an analysis of the area-aggregated reliability and sharpness diagrams showed that the EANN is better calibrated than the individual dynamical models and has better resolution than traditional statistical models for above-normal (AN) and below-normal (BN) categories. Both statistical and dynamical models depict a bad-calibrated NN category. It is also shown that combining the EANN and dynamical models improves forecast system reliability compared to an MME based only on NMME models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it