Development of new predictor climate variables for statistical downscaling of daily precipitation process
Bibliographic record
Abstract
Statistical downscaling (SD) procedures have been frequently used for assessing the potential impacts of climate change and variability on hydrological regime. These procedures are based on the empirical relationships between large-scale atmospheric variables (predictors) and surface environment parameters (e.g., precipitation and temperature). The present research work is hence concerned with the development of new predictor climate variables that could be used for improving the accuracy of downscaling of daily precipitation process at a local site. The new predictors should be able to provide a more accurate simulation of the local variable since they could describe more accurately the physical characteristics of the precipitation process. In particular, a better reproduction of summer rainfall event is expected through an improved inclusion of main thermodynamic forcings from humidity and stability parameters. The first part of this study focuses on the re-computation of the geostrophic circulation predictor variables developed by Wilby and Wigley (2000), reconstructed from mean sea level pressure or geopotential heights. The same circulation variables are re-computed from prognostic winds of the National Centre for Environmental Prediction (NCEP) re-analysis data set (Kalnay et al., 1996). Assessment of the performance of the re-computed predictors is carried out using the Statistical DownScaling Model (SDSM), developed by Wilby et al. (2002), and based on a number of climate indices characterizing the frequency, intensity and extremes of daily precipitation process. Two different predictor sets are considered, the first consisting of circulation-only variables, the second including a raw specific humidity predictor. For each predictor set, results obtained from the two computation techniques are compared. Daily precipitation data available at Montreal-Dorval Airport station for the 1961-1990 period were used in this assessment. Results indicated that the re-computation of geostrophic variables for both sets could yield significant improvements in the reproduction of local precipitation characteristics for the validation 1976-1990 period. The most striking improvement can be achieved for winter, as expected from the greater influence of large-scale circulation forcings on precipitation in this season. In the second part, new advection variables are developed based on a generalized omega equation. It is found that the Laplacian of temperature advection and the differential vorticity advection appear as direct forcings of the vertical velocity, strongly correlated with the precipitation process. Precipitable water and atmospheric instability indices are also included in the predictor range, mainly to reach a better simulation of convective precipitation. Next, a new statistical downscaling scheme is developed, combining a Principal Component Analysis (PCA) of the new predictors and the SDSM model. Analysis of the different computed principal components confirms the major role of the two identified advection terms and the humidity/instability predictors. Assessment of the new PCA+SDSM scheme shows significant improvements of the simulation of precipitation intensity, although results are less conclusive regarding the precipitation occurrence. Finally, the influence of the calibration period length on the new downscaling scheme performance was carried out by comparing the simulation results obtained from two calibration runs of 15 and 30 years of length: for the 1961-1975 period and for the 1961-1990 one. It was found that doubling the calibration period length could lead to significant improvements in the reproduction of the local precipitation characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".