Application of stochastic downscaling techniques to Global Climate Model data for regional climate prediction
Bibliographic record
Abstract
Global warming is the most important issue of the present day that affects the climate drastically. This research was carried out to find out the effects of Global warming on Louisiana in future on a very finer spatial and temporal scale. For this purpose spatial downscaling technique is used, where finer resolution climate information is derived from a coarser resolution Global Climate Model (GCM) output. Empirical/statistical downscaling method is used in which sub grid scale changes are calculated as a function of large scale climate. For this purpose a stochastic weather generator and two Global models are considered. The two global models are CCCma (Canadian Center for Climate Modeling and Analysis) and CSIRO (Australia's Commonwealth Scientific and Industrial Research Organization). The stochastic weather generator used is Climate Generator (CLIGEN). The global monthly means are calculated until the year 2090 from the available daily data of CCCma and CSIRO and the units are converted according to that used in CLIGEN. The monthly means of the parameter files of CLIGEN are replaced with the Global monthly means, and the other statistical parameters such as standard deviation, skewness, etc are changed accordingly and weather is generated using the CLIGEN until the year 2090 for Louisiana. Statistical analysis is performed for the climate generated using the two Global models and comparisons are made between the results of the two models. Also time series plots are drawn for the generated climate of the two models taking one year as a representative year.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".