The Hydromet Decision Support System: operational applications in hydrometeorology and flash flood prediction
Bibliographic record
Abstract
Weather Decision Technologies (WDT) in collaboration with the National Severe Storms Laboratory (NSSL) in the USA, and the Regional Agency for Environmental Protection and Prevention of Veneto (ARPAV) in Italy have developed a severe weather monitoring and hydrometeorological package termed the Hydromet Decision Support System (HDSS). This system integrates data from radars, rain gauges, satellite and numerical models to provide high resolution Quantitative Precipitation Estimates (QPE) and Quantitative Precipitation Forecasts (QPF). The focus of this paper is to briefly describe the hydrometeorological components of the system that include: • radar quality control including clutter removal, brightband identification, hybrid scans, and scan filling • mosaicking of radars in the Veneto region • processing of the data using a suite of applications called Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPE-SUMS) for the derivation of QPE fields • forecasts of radar reflectivity fields using the McGill Algorithm for Nowcasting Precipitation Using SemiLagrangian Extrapolation (MAPLE) • derivation of QPF fields using the results of MAPLE • a Flash Flood Prediction Algorithm (FFPA) which combines QPE and QPF values to forecast flash flood areas based on basin Flash Flood Guidance (FFG) values • automated alerting of basins that have exceeded,, or are forecast to, approach or exceed FFG values
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 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".