Finer Scale Rainfall Projections for Kerala Meteorological Subdivision, India Based on Multivariate Empirical Mode Decomposition
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".