A Study of the Error Covariance Matrix of Radar Rainfall Estimates in Stratiform Rain. Part II: Scale Dependence
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground has been recently quantified at a resolution typically used in schemes assimilating rainfall at the ground onto mesoscale models. Here, the contribution of the two most important sources of uncertainty at nonattenuating wavelengths (the range-dependent error and the uncertainty due to the Z–R transformation) and their interaction are studied as a function of the resolution of radar observations. The analysis is carried out using a large dataset of collocated reflectivity profiles from the McGill S-band radar and disdrometric measurements obtained in stratiform rainfall at resolutions of 1 × 1, 5 × 5, and 15 × 15 km2. Results show that the errors affecting radar quantitative precipitation estimation (QPE) have a strong dependence with range, and that their structure is scale dependent. At the analyzed resolutions, QPE errors are significantly correlated in time and over several grid points.
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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 | 0.000 | 0.000 |
| 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 it