THE STEVENS FLOOD ADVISORY SYSTEM: OPERATIONAL H³E FLOOD FORECASTS FOR THE GREATER NEW YORK / NEW JERSEY METROPOLITAN REGION

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.

An integrated weather–hydrologic–coastal–stormwater framework to model urban‐coastal interactions: City of Hoboken application

“…The open boundary conditions used by NYHOPS are derived from the regional scale Stevens Northwest Atlantic Prediction (SNAP) model domain, which is also based on the sECOM code but uses a 5‐km constant resolution grid (Figure ). NYHOPS currently runs in parallel on the Stevens Institute of Technology supercomputer (Jordi & Wang, ) with a processing time of 3.5 hr to produce a forecast of an ensemble of water levels for a lead time of 81 hr for the eastern seaboard of the United States (Georgas et al, ).…”

“…The map located in the upper left panel depicts the closest available rainfall gauge at Newark Airport and the tidal station at Battery Park, New York Flood Advisory System (SFAS) and uses an ensemble of wind-stress and mean sea-level pressure from different forecasting agencies to obtain an ensemble of water levels in the New York/New Jersey Harbor and the surrounding waters. More details regarding this application can be found in Georgas et al (2016). The specific modelling components of this framework were selected because of extensive research and validation in the New York/New Jersey metropolitan region.…”

Modeling wave attenuation by salt marshes in Jamaica Bay, New York, using a new rapid wave model

“…For example, a comparison of computational expense of MDO and SWAN on an idealized coastal ocean grid showed MDO to be 58 times less expensive [ Marsooli et al ., ], yet accurately captures all important wave‐related processes for enclosed bays and estuaries, and thus provides a valuable new option for modeling of coastal systems. Thus, it can be a new and widely used wave model for use in operational ensemble forecasting systems [e.g., Georgas et al ., ], as well as annual or longer simulations of biogeochemistry or water quality [e.g., Feng et al ., ], or salt marsh erosion [e.g., Wang et al ., ]. To include a physics‐based feature in MDO that captures wave‐vegetation interactions, here, we formulate a vegetation‐induced energy dissipation term based on the method of Mendez and Losada [] and implement it in the energy balance equation of MDO.…”

Physical processes in the Santos-São Vicente-Bertioga Estuarine System: from observations to forecasts

“…Further progress in the model performance would include the use of an ensemble of weather forecasting models. This change would theoretically converge to a more realistic prediction and decrease the error in the subtidal hydrodynamic simulations (Georgas et al, 2016a).…”