Statistical downscaling of daily and hourly climate scenarios for the various meteorological variables at Montreal
Bibliographic record
Abstract
A lot of research has been carried out using statistical methods in downscaling large-scale GCM outputs to the local site or small-scale region. Using statistical methods, empirical relationships are developed between the large-scale GCM outputs and the local site weather variables. In this methodology, it is assumed that these empirical relationships will also hold true even in the future periods of time. Environmental assessment studies for the future decade under the influence of a changing climate is the need of the hour. In order to identify the future synoptic weather types, future daily and hourly projections of the weather variables are required. Hence, this research is motivated by the lack of a comprehensive and statistically significant downscaling methodology for the various weather variables. The present study is based on the various statistical downscaling techniques developed by the researchers in the past using linear regression model because of the advantage of being less computationally intensive. In addition, an attempt has been made to develop an improved statistical downscaling methodology by combining different techniques to develop a robust method with a detailed performance assessment of the models. Linear Regression models are derived to downscale daily climate scenarios using NCEP reanalysis datasets for the predictors and weather station data available at Trudeau International Airport for the predictands during the reference period 1958-2001. The NCEP predictors are regridded to the GCM scale as the GCM outputs are to be used for deriving future climate projections. Standardization and Deseasonalization of the predictor variables are carried out followed by principal component analysis prior to their introduction into the stepwise regression model. CANESM2 is selected as the GCM model in the present work whose outputs are used as predictors in the NCEP derived regression models. A Bias Correction procedure is used to correct the systematic biases present using a quantile-quantile mapping technique on the downscaled variable using CANESM2 predictors. After downscaling the daily climate variables, hourly downscaling transfer functions are derived based on the historical relationships of the hourly values with its daily mean as well as other weather predictors where appropriate. For the future climate projections, RCP2.6, 4.5 and 8.5 are used as greenhouse gas trajectories representing the change in climate in the future decade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".