Evaluation of differences between gridded precipitation products in the Southern Prairies of Manitoba
Bibliographic record
Abstract
Southern Manitoba, Canada Gridded precipitation products offer an alternative to sparse rainfall observations in the Prairies; however, the accuracy of the products significantly influences the accuracy of streamflow predictions. This study evaluates discrepancies among seven daily gridded precipitation datasets (2018–2023) against gauge observations. The error metrics chosen in this study are informed by the most impactful sources of error that affect hydrological modelling: systematic biases, non-stationary temporal variations, and event-related discrepancies. Seasonal analysis reveals distinct performance patterns across precipitation products. Winter estimates of precipitation exhibit the highest relative bias (RBias), with ERA5_L (199 %), GPM (161 %), and CaPA (137 %) substantially overestimating precipitation, despite having the lowest RMSE values (around 1 mm/day), reflecting the generally low observed precipitation amounts. Conversely, summer estimates of precipitation show lower RBias for MRMS (11.2 %) and ERA5_L (9.15 %), however, they exhibit the largest root mean square error (RMSE) values (up to 9.78 mm/day for GSMAP), indicating large absolute errors driven by intense convective storms. Fall and spring estimates present moderate RBias and RMSE values, with most products performing more consistently. Overall. MRMS and CaPA are the top-performing precipitation products, showing the lowest RMSE and highest correlation across seasons. NLDAS-2, ERA5-L, and PERSIANN have moderate accuracy, while GPM and GSMAP show higher errors and lower correlations. • Differences between seven gridded precipitation datasets are systematically evaluated. • Selected error metrics reflect the most impactful sources of precipitation error. • Seasonally dependent errors in precipitation estimates are dominant. • MRMS and CaPA outperform other products in accuracy and consistency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".