Evaluation of linear regression methods as downscaling tools in temperature projections over the Pichola Lake Basin in India
Bibliographic record
Abstract
Abstract In this paper, downscaling models are developed using various linear regression approaches, namely direct, forward, backward and stepwise regression, for obtaining projections of mean monthly maximum and minimum temperatures ( T max and T min) to lake‐basin scale in an arid region in India. The effectiveness of these regression approaches is evaluated through application to downscale the predictands for the Pichola lake region in the state of Rajasthan in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (i) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000 and (ii) the simulations from the third‐generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. The selection of important predictor variables becomes a crucial issue for developing downscaling models as reanalysis data are based on a wide range of meteorological measurements and observations. A simple multiplicative shift was used for correcting predictand values. Direct regression was found to yield better performance among all other regression techniques for the training data set, while the forward regression technique performed better in the validation data set, explored in the present study. For trend analysis, the Mann–Kendall non‐parametric test was performed. The results of downscaling models show that an increasing trend is observed for T max and T min for A1B, A2 and B1 scenarios, whereas no trend is discerned with the COMMIT scenario by using predictors. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".