Daily Precipitation Fields Modeling across the Great Lakes Region (Canada) by Using the CFSR Reanalysis
Bibliographic record
Abstract
Abstract Reanalyses, generated by numerical weather prediction methods assimilating past observations, provide consistent and continuous meteorological fields for a specific period. In regard to precipitation, reanalyses cannot be used as a climate proxy of the observed precipitation, as biases and scale mismatches exist between the datasets. In the present study, a stochastic model output statistics (SMOS) approach combined with meta-Gaussian spatiotemporal random fields was employed to cope with these caveats. The SMOS is based on the generalized linear model (GLM) and the vector generalized linear model (VGLM) frameworks to model the precipitation occurrence and intensity, respectively. Both models use the Climate Forecast System Reanalysis (CFSR) precipitation as covariate and were locally calibrated at 173 sites across the Great Lakes region. Combined with meta-Gaussian random fields, the GLM and VGLM models allowed for the generation of spatially coherent daily precipitation fields across the region. The results indicated that the approach corrected systematic biases and provided an accurate spatiotemporal structure of daily precipitation. Performances of selected precipitation indicators from the joint Commission for Climatology (CCl)/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) were good and were systematically improved when compared to CFSR.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".