Quantitative Precipitation Estimation from a C-Band Dual-Polarized Radar for the 8 July 2013 Flood in Toronto, Canada
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Bibliographic record
Abstract
Abstract A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dual-polarized radar (WKR). Horizontal reflectivity Z and differential reflectivity ZDR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R(Z) and R(Z, ZDR) algorithms. Specific differential phase KDP was used to compute rain rates from three R(KDP) algorithms, one modified to use positive and negative KDP, and an R(KDP, ZDR) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R(A) algorithm. High-temporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R(Z) and S-band dual-polarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R(Z) and R(Z, ZDR) algorithms. Rainfall estimation from the Brandes et al. R(KDP) and R(A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and −6.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R(KDP) and R(A) algorithms were comparable to the NEXRAD S-band estimates.
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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 | 0.001 |
| 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