Evaluation of variability among different precipitation products in the Northern Great Plains
Bibliographic record
Abstract
Study region The Northern Great Plains. Study focus Seasonal and extreme hydro-climatological events in the Northern Great Plains can have significant socio-economic impacts. Although a variety of precipitation datasets can be used for characterizing the hydro-climatological behavior of this region, much of our knowledge on precipitation variability among different products over this region comes from the coarse-scale evaluation studies for the whole Canada or CONUS, many of which may under-represent the performance of different precipitation products over these areas. The present study is intended to fill this gap. Daily total precipitation data derived from CaPA, ERA-Interim, ERA5, JRA-55, MERRA-2 and NLDAS-2, respectively, are evaluated over the Assiniboine River Basin (ARB), which represents many of the hydro-climatological complexities associated with the Northern Great Plains. Additionally, the spatial and year-to-year variations in total liquid water flux for spring and early summer are also examined over the ARB. New hydrological insights for the region Precipitation products typically perform better in spring and autumn than in summer and winter. Overall, CaPA performs best, except for a severe underestimation of summer precipitation. MERRA-2 is typically the second best. ERA5 typically outperforms ERA-Interim. NLDAS-2 has a fairly low performance. JRA-55 has the lowest performance, exhibiting a strong wet bias. The quantified variability among these products will help characterize sources of uncertainty for hydro-climatological analysis within the Northern Great Plains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".