An algorithm for integrating satellite precipitation estimates with in situ precipitation data on a pentad time scale
Bibliographic record
Abstract
Abstract This study proposes an algorithm for constructing pentad precipitation fields by integrating the popularly used Global Precipitation Climatology Project (GPCP) daily precipitation data set, GPCP1dd v1.2, with Canadian in situ daily precipitation data. This algorithm consists of two major steps. First, the GPCP data were adjusted to remove biases relative to the gauge data, with consideration of the differences between snowfall and rainfall, and of the gauge density. Then, a blended pentad precipitation field was constructed using the adjusted GPCP precipitation field and the differences between the gauge and adjusted GPCP precipitation fields (residual kriging). The skill of the algorithm is evaluated for three networks of sparse to medium gauge density, with the evaluation data set being much larger than the training data set. The results show that the algorithm produces better representation of pentad precipitation fields than the GPCP precipitation estimates or using the gauge data alone; it has smaller root‐mean‐square errors and higher correlation skill scores. This algorithm was used to produce the first blended pentad precipitation data set for the period of 1997–2007 for Canada (CanBP5dV1). It can be used for other regions around the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".