A Spatial-Temporal Statistical Downscaling Approach to Estimation of Extreme Precipitations for Climate-Related Impact Studies at a Local Site
Bibliographic record
Abstract
This paper presents a spatial-temporal downscaling approach to describe the linkage between large-scale climate variables for daily scale to annual maximum (AM) precipitations for daily and sub-daily scales at a local site. The proposed approach is based on a combination of a spatial downscaling method to link large-scale climate variables as provided by General Circulation Model (GCM) simulations with daily extreme precipitations at a local site and a temporal downscaling procedure to describe the relationships between daily extreme precipitations with sub-daily extreme precipitations using the scaling General Extreme Value (GEV) distribution. The feasibility of the proposed downscaling method has been tested based on climate simulation outputs from two GCMs under the A2 scenario (HadCM3A2 and CGCM2A2) and using available AM precipitation data for durations ranging from 5 minutes to 1 day at 15 raingage stations in Quebec (Canada) for the 1961–1990 period. Results of this numerical application has indicated that it is feasible to link large-scale climate predictors for daily scale given by GCM simulation outputs with daily and sub-daily AM precipitations at a local site. Furthermore, it was found that AM precipitations at a local site downscaled from the HadCM3A2 displayed a small change in the future, while those values estimated from the CGCM2A2 indicated a large increasing trend for future periods.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".