Wavelet Analysis on the Variability, Teleconnectivity, and Predictability of the Seasonal Rainfall of Taiwan
Bibliographic record
Abstract
Abstract Using wavelet analysis, the variability and oscillations of November–January (NDJ) and January–March (JFM) rainfall (1974–2006) of Taiwan and seasonal sea surface temperature (SST) of the Pacific Ocean were analyzed. From the scale-average wavelet power (SAWP) computed for the seasonal rainfall, it seems that the data exhibit interannual oscillations at a 2–4-yr period. On the basis of correlation fields between decadal component removed wavelet PC (DCR-WPC1) of seasonal rainfall and decadal component removed scale-averaged wavelet power (DCR-SAWP) of SST of Pacific Ocean at one-season lead time, SST of some domains of the western Pacific Ocean (July–September SST around 0°–30°N, 120°–160°E; October–December SST around 0°–60°N, 125°E–160°W) were selected as predictors to predict seasonal NDJ and JFM rainfall of Taiwan at one-season lead time, respectively, using an Artificial Neural Network calibrated by the Genetic Algorithm (ANN-GA). The ANN-GA was first calibrated using the 1975–99 data and independently validated using 2000–06 data. In terms of summary statistics such as the correlation coefficient, root-mean-square error (RMSE), and Hanssen–Kuipers (HK) scores, the prediction of seasonal rainfall of northern and western Taiwan using ANN-GA are generally good for both calibration and validation stages, but not so for southeastern Taiwan because the seasonal rainfall of the former are much more significantly correlated to the SST of selected sectors of the Pacific Ocean than the latter.
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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 | 0.002 |
| 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) | 0.000 | 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 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".