National Centers for Environmental Prediction – National Center for Atmospheric Research (NCEP–NCAR) reanalyses data for hydrologic modelling on a basin scale
Bibliographic record
Abstract
This paper evaluates the National Centers for Environmental Prediction – National Center for Atmospheric Research (NCEP–NCAR) reanalyses hydroclimatic data as an initial check for assessment of hydrologic impacts of climate change at the basin scale. A reanalysis dataset for daily precipitation, maximum temperature, and minimum temperature from the NCEP–NCAR global (NNGR) and regional (North American Regional Reanalysis or NARR) reanalysis project has been used as input into the semidistributed hydrologic model (Hydrologic Engineering Center Hydraulic Modeling System or HEC–HMS) for the period 1980–2005. An extensive analysis has been performed for assessing the performance of the reanalysis data generated flows compared with the observed inputs during May–November. The stream flows generated from the NARR dataset show encouraging results in simulating summertime low flows with less variability and fewer errors. The results indicate that NNGR results are less accurate and highly variable. This study suggests that NARR can be adequately used as an alternative in data-scarce regions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".