A Non-Linear Dynamical–Statistical Model for Reconstruction of the Air–Sea Element Fields in the Tropical Pacific Ocean
Bibliographic record
Abstract
Aiming at tackling the difficulty in establishing a sea surface temperature (SST) dynamical model, this study develops a non-linear dynamical–statistical model of SST fields and their correlative factors based on Genetic Algorithms (GA) and the dynamical system reconstruction idea, which greatly improves the El Niño–Southern Oscillation (ENSO) forecast model. Using Hadley SST data, sea surface wind (SSW) and sea level pressure (SLP) data from the National Centers for Environmental Prediction-National Center for Environmental Research (NCEP-NCAR), with empirical orthogonal function (EOF) time-space for reconstruction, we carry out numerical integral forecasting experiments for SST, SSW, and SLP fields. By statistical analysis of the forecasting experiments, we find that forecasts for less than 25 months perform better than longer term forecasts. Based on the model, we forecast SST, SSW, and SLP fields in September, October, and November 2014 and predict a weak La Niña event. This study explores a novel method for the complex atmosphere–ocean system.
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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 | 0.000 |
| 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) | 0.000 | 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".