Multisegment statistical bias correction of daily GCM precipitation output
Bibliographic record
Abstract
Abstract An improved bias correction method for daily general circulation model (GCM) precipitation is presented. The method belongs to the widely used family of quantile mapping correction methods. The method uses different instances of gamma function that are fitted on multiple discrete segments on the precipitation cumulative density function (CDF), instead of the common quantile‐quantile approach that uses one theoretical distribution to fit the entire CDF. This imposes to the method the ability to better transfer the observed precipitation statistics to the raw GCM data. The selection of the segment number is performed by an information criterion to poise between complexity and efficiency of the transfer function. The global precipitation output of Institut Pierre Simon Laplace Coupled Model for the period 1960–2000 is bias corrected using the precipitation observations of WATCH Forcing Data. The 1960–1980 period of observations was used to calibrate the bias correction method, while 1981–2000 was used for validation. The proposed method performs well on the validation period, according to two performance estimators.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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 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".