Bias correction of 20 years of IMERG satellite precipitation data over Canada and Alaska
Bibliographic record
Abstract
We define two northern study areas: one covering all of Canada and Alaska and a second, smaller subregion surrounding the Peace-Athabasca Delta for testing. This study aims to use bias correction to improve satellite precipitation data over a relatively data-sparse high latitude region using a network of in-situ rain gauges. We evaluate the satellite data and derive a linear bias-elevation relationship and apply the correction with a digital elevation model at a monthly scale, and further disaggregate it to produce corrected data at a daily scale. We find that the underestimation in the satellite data increases linearly with increasing elevation, above 500 m a.s.l. at the continental scale and for all elevations at the regional scale. Bias also varies seasonally, with higher bias in summer and lower bias in winter. Compared with uncalibrated data, the monthly continental correction reduces absolute bias by 16% and the root mean squared error by 6%, while the daily continental correction improves absolute bias by 17% but degrades root mean squared error slightly by 2%. We conclude that applying elevation-based bias correction reduces systematic elevational bias in northern high-latitude satellite precipitation data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".