Finer Scale Rainfall Projections for Kerala Meteorological Subdivision, India Based on Multivariate Empirical Mode Decomposition
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Bibliographic record
Abstract
This study proposes an innovative approach for statistical downscaling of rainfall based on scaling property of meteorological variables. The reanalysis data of five dominant meteorological variables mean sea level pressure, relative humidity, surface temperature, wind velocity (zonal and meridional components) extracted from National Centre for Environmental Prediction (NCEP) are used as predictors to project monthly rainfall of Kerala meteorological subdivision in India. The multiscale decomposition of predictor dataset of the region and the monthly rainfall of a specific grid point is performed simultaneously by employing the Multivariate Empirical Mode Decomposition (MEMD) technique. The individual modes are predicted by fitting stepwise linear regression (SLR) by considering the potential predictors based on p-value statistics. Subsequent addition of the predicted modes gives the monthly rainfall. The method is demonstrated by a specific grid point of Chalakkudi river basin in Kerala, India. The method is found to be superior over the linear regression and M5 model tree based transfer function approaches. Further, the MEMD-SLR hybrid model is used for rainfall projections of the state of Kerala under three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) provided by Canadian Centre for Climate Modeling and Analysis (CCCMa).
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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 | 0.001 |
| 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 it