Development of the NWS’ Probabilistic Extratropical Storm Surge Model and Postprocessing Methodology

The National Weather Service’s (NWS) Meteorological Development Laboratory (MDL) developed the Extra-Tropical Storm Surge (ETSS) model in 1995 and developed a post-processing methodology in 2000 to statistically account for components such as sea level rise, waves, river flooding and model error. More recently, MDL has enhanced the ETSS model to operationally provide deterministic inundation guidance four times a day based on storm surge and tide in coastal areas along the United States’ Eastern and Western Seaboards, the Gulf of Mexico and Alaska.

Storm surge guidance has various uncertainties associated with it such as (a) the atmospheric forcing (wind speed, wind direction and atmospheric pressure), (b) the initial water conditions, (c) the included physical processes, (d) the numerical scheme, etc. While some of these can be reduced by enhancing the storm surge model, others, such as atmospheric forcing, rely on external inputs. Uncertainty in atmospheric forcing is of particular importance as it is the main source of uncertainty in storm surge based inundation guidance. Ensemble techniques combining atmospheric forcing and storm surge modeling are necessary to produce quantitative estimates of storm surge based inundation risk.

MDL has recently implemented one such ensemble technique in the form of the Probabilistic Extra-Tropical Storm Surge (P-ETSS) model. P-ETSS uses the 21 ensemble members from the Global Ensemble Forecast System for atmospheric input to a storm surge and tide inundation model. It then equally weights the resulting set of inundation guidance. Since the inundation model does not currently account for components such as sea level rise, waves, river flooding and model error, a statistical post processing methodology similar to ETSS’ is used to enhance the overall guidance. This paper describes the details of this effort and provides statistical verification of the P-ETSS products for several case studies.