Rainfall Accuracy Considerations Using Radar and Rain Gauge Networks for Rainfall-Runoff Monitoring
Why this work is in the frame
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Bibliographic record
Abstract
Components of urban drainage during wet weather affecting water quality in receiving waters are stormwater and overflows from sanitary or combined sewers. A common element affecting each of these components is the spatial distribution of rainfall over contributing areas. Knowing quantities of stormwater arriving at inlets, infiltrating into sanitary sewers, and the inflow into combined sewers is critical to successful hydraulic model calibration and sewer system design. Accuracy and representativeness of the spatial and temporal distribution of rainfall over contributing areas is an important determinant of model accuracy. It is not always feasible to install sufficient rain gauges to measure spatially representative rainfall over a metropolitan sewer district at the scale of sewersheds. Nor is it feasible to install streamflow monitoring stations or sample priority pollutants in every impacted watershed. Thus the combination of radar and rain gauges to characterize the distribution of rainfall offers technical advantages for monitoring both rainfall and runoff in urban areas. Evaluation of a 55-event series, the median accuracy, as measured by gauge-radar comparison, has a median average difference of 8%. Gauge network density requirements should take into account the variability of precipitation, distribution over sewershed areas, and local or climatological trends caused by terrain or large water bodies. Runoff measured by streamflow is used to validate the radar to gauge correction and to test the influence of random and systematic error in the radar input. Because simulated runoff is dependent on the rainfall input
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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 | 0.001 | 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 | 0.001 |
| 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