Downscaling and Bias Correcting a Cold Season Precipitation Climatology over Coastal Southern British Columbia Using the Regional Atmospheric Modeling System (RAMS)
Bibliographic record
Abstract
Abstract Thirty years of the North American Regional Reanalysis (NARR) are dynamically downscaled to an 8-km grid spacing using the Regional Atmospheric Modeling System (RAMS) to generate a climatology of glacier winter accumulation over the southern Coast Mountains in British Columbia (BC), Canada. RAMS precipitation fields are bias corrected using observations from Environment Canada (EC) synoptic and climate stations and BC provincial snow pillow stations. Raw and bias-corrected model output is compared with observations from EC Reference Climate Network stations, BC provincial Ministry of Transportation and Highways stations, BC Hydro stations, snow course data, and glacier mass balance studies. A water balance is also applied to 12 drainage basins located within the modeling domain to test the consistency of both the raw and bias-corrected precipitation fields with observed streamflow. Model output is compared with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and bias-corrected NARR. Isotropic spectral power densities are examined to compare the effective spatial resolution of the various precipitation fields. The spatial distribution of the bias-correction field suggests that RAMS underpredicts precipitation on the western edge of Vancouver Island, Canada, and overpredicts along the southern Coast Mountains. The bias correction helps close the water balance budgets in all basins except the Somass on Vancouver Island. The bias correction generally improves the agreement between RAMS and observed snow water equivalent amounts at the glacier and snow course sites, and observed precipitation amounts at the synoptic, climate, and snow pillow stations. The RAMS and NARR isotropic spectral power densities show a loss of variability at approximately 45 and 63 km, while PRISM shows little falloff down to 16 km.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".