Snowfall rate estimation using C‐band polarimetric radars
Bibliographic record
Abstract
ABSTRACT: Radar quantitative precipitation estimation plays an important role in weather forecasting, nowcasting and hydrological models. This study evaluates the Sekhon and Srivastava (1970) snow water equivalent (SWE) algorithm currently implemented by the Canadian Radar Network of Environment and Climate Change Canada, suggests an improved algorithm and also evaluates the ability of polarimetric radars in estimating SWE. The radar data were collected from the dual polarimetric King City radar (CWKR) near Toronto, Ontario, and the Doppler Holyrood radar (CWTP) in Newfoundland. SWE data were collected at Oakville, Ontario, at Pearson International Airport (CYYZ), Toronto, Ontario, and at Mount Pearl, Newfoundland. The ground observations show that the polarimetric variables could be used to infer a few of the microphysical processes during snowfall. It is suggested that the co‐polar correlation co‐efficient (ρhv) could be sensitive to the size ranges of different snow habits. Also, higher differential reflectivity (Zdr) values were measured with large aggregates. The results show a severe underestimation of SWE rates by the Sekhon and Srivastava algorithm. One hour accumulations from each site were used to develop SWE(ZeH) and SWE(ZeH, ZDR) algorithms (ZeH and ZDR are the reflectivity factor and differential reflectivity, respectively). Similarly, algorithms were developed using SWE at 10 min intervals from CYYZ and Mount Pearl but these algorithms appeared to overestimate SWE. The hourly SWE accumulations from the three sites were combined to produce an additional SWE(ZeH) algorithm which showed better statistical results. A modest difference was found between the conventional and polarimetric algorithms for estimating snowfall amounts (SWE).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".