Evaluation of various daily precipitation products for large-scale hydro-climatic applications over Canada
Bibliographic record
Abstract
Abstract. A number of global and regional gridded climate products based on multiple data sources and models are available that can potentially provide better and more reliable estimates of precipitation for climate and hydrological studies. However, research into the reliability of these products for various regions has been limited and in many cases non-existent. This study identifies several gridded precipitation products over Canada and develops a systematic analysis framework to assess the characteristics of errors associated with the different datasets, using the best available adjusted precipitation-gauge data as a benchmark over the period 1979 to 2012. The framework quantifies the spatial and temporal variability of the errors over 15 terrestrial ecozones in Canada for different seasons at the daily time scale. Results showed that most of the products were relatively skillful in central Canada but tended to underestimate precipitation amounts on the east coast and overestimate on the west. The global product by WATCH Forcing Data ERA-Interim (WFDEI) augmented by Global Precipitation Climatology Centre (GPCC) data (WFDEI [GPCC]) performed best with respect to different metrics. The Canadian Precipitation Analysis (CaPA) product of Meteorological Service of Canada, performed comparably with WFDEI [GPCC], however it only provides data from 2002. All the products performed best in summer, followed by autumn, spring, and winter in order of decreasing quality. Due to the sparse observational network, northern Canada (above 60° N) was most difficult to assess with the majority of products tending to significantly underestimate total precipitation. Results from this study can be used as a guidance for potential users regarding the performance of different precipitation products for a range of geographical regions and time periods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".