Comparison of land surface scheme simulations with field observations <i>versus</i> atmospheric model output as forcing
Bibliographic record
Abstract
The low density of meteorological stations in parts of Canada necessitates using numerical weather prediction (NWP)/assimilation output for hydrological modelling. In this study, comparisons are made of simulated land surface variables when using field observations versus NWP output as forcing for two well-instrumented sites: the mountainous and forested Marmot Creek Basin (MCRB) in the Canadian Rocky Mountains, and a prairie cropland/grassland site (Kenaston). The Canadian Land Surface Scheme 3.6 (CLASS) was used for modelling. The Global Environmental Multiscale (GEM) model with Canadian Precipitation Analysis (CaPA) was also used as forcing. There was good agreement between observed meteorology and GEM/CaPA, though some deficiencies in GEM/CaPA were identified: the effects of sub-grid topography on incoming radiation and wind speed were not accounted for at MCRB, and CaPA did not capture some convective rainfall events at Kenaston. CLASS simulations using both sets of forcing showed difficulties in simulating snow depth, soil moisture and evapotranspiration; certain difficulties were linked to GEM/CaPA deficiencies and/or CLASS. Both sets of forcing tended to overestimate the duration of snow cover at MCRB, but during different years. With GEM/CaPA as forcing, CLASS overestimated the duration of frozen soils. The GEM/CaPA precipitation difficulties at Kenaston degraded soil moisture simulations.EDITOR A. Castellarin; ASSOCIATE EDITOR E. Volpi
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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 | 0.004 |
| 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 | 0.001 | 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".