Forecasting the Indian Ocean Dipole With Deep Learning Techniques
Bibliographic record
Abstract
Abstract In the present research, Indian Ocean Dipole (IOD) prediction was explored using statistical methods based on deep learning techniques. First, convolutional neural network (CNN) models were trained using sea‐surface temperature anomaly (SSTA) maps of the Indian Ocean from 1854 to 1989, and the properly trained CNN models were then validated with the period from 1991 to 2019. The results indicate that the deep learning approach is capable of forecasting the IOD at lead times up to 7 months. The forecast skills of CNN are superior to those of the dynamic models in the North American Multi‐Model Ensemble (NMME). The CNN outperforms the NMME with lower sensitivity to predictability barriers and fewer systematic errors. Moreover, the gradient heat map analysis demonstrates that the triggering precursors selected by CNN models for IOD events are novel and physically sensible. These results suggest the CNN to be a new and effective tool for both IOD prediction and comprehension.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".