The stevens flood advisory system: operational H3E flood forecasts for the greater New York / New Jersey Metropolitan Region
Bibliographic record
Abstract
This paper presents the automation, website interface, and verification of the Stevens Flood Advisory System (SFAS, http://stevens.edu/SFAS).The fully-automated, ensemble-based flood advisory system dynamically integrates real-time observations and river and coastal flood models forced by an ensemble of meteorological models at various scales to produce and serve street scale flood forecasts over urban terrain.SFAS is applied to the Greater NY/NJ Metropolitan region, and is used routinely by multiple forecast offices and departments within the US National Weather Service (NWS), regional and municipal Offices of Emergency Management, as well as the general public.Every six hours, the underlying H 3 E (Hydrologic-Hydraulic-Hydrodynamic Ensemble) modelling framework, prepares, runs, data-assimilates, and integrates results from 375 dynamic model simulations to produce actionable, probabilistic ensemble forecasts of upland and coastal (storm surge) flooding conditions with an 81-h forecast horizon.Meteorological forcing to the H 3 E models is provided by 125 weather model ensemble members as well as deterministic weather models from major weather agencies (NCEP, ECMWF, CMC) and academia.The state-of-the-art SFAS, a replacement of the well-known, but deterministic, Storm Surge Warning System (SSWS) that was highlighted during Hurricanes Irene and Sandy and more recently extratropical cyclone Jonas, has been operational since the end of 2015.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".