The Extra-Tropical Storm Surge (ETSS) model is developed and maintained by the National Weather Service’s (NWS) Meteorological Development Laboratory (MDL). It runs operationally four times a day to predict overland surge with tide guidance for extra-tropical storms along the U.S. East Coast, Gulf of Mexico, West Coast and all of the coasts of Alaska.
Like all forecasts, 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.
In 2017 (H. Liu and A. Taylor), MDL 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. More recently (H. Liu, A. Taylor and K. Kang, 2019), MDL enhanced P-ETSS by using the 42-member North American Ensemble Forecast System (NAEFS) instead of the 21‑member Global Ensemble Forecast System.
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. The results are then visualized via a website to facilitate user access.
METHODOLOGY AND PRODUCTS
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)
As the diagram shows, P-ETSS uses the 21 ensemble members from the GEFS as atmospheric input to the ETSS model. In each grid cell, the surge + tide values from each ensemble run are sorted in ascending order. The probability and exceedance products are created from this sorted set.
Specifically for the Probability of Surge + Tide greater than X products for X in {0, 1, 2, 3, 6, 7, 8, 9, 10, 13 and 16 feet above datum or ground level}, it counts in each grid cell the number (N) of ensemble runs greater than X. The probability in that grid cell is then: N*100/21. See Fig. 1 for an example where the probability of > 1 feet is 80%.
![](data:image/png;base64,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)
Fig. 1 is an example of using the number of values with surge + tide greater than a threshold to determine the probability
For the X% Exceedance Height products, for X in {10, 20, 30, 40, 50, 90}, it searches down the sorted list for the height (above datum or ground level) in the “correct” spot. For example, the 10% exceedance with 21 members is the value that is matched or exceeded by 2.1 ensemble members, so we want the 2nd spot in the sorted list (Fig 2). Thus the “correct” spots for {10, 20, 30, 40, 50, 90%} are {2nd, 4th, 6th, 8th, 10th, 19th} respectively.
The final P-ETSS product is the Ensemble Mean, Minimum and Maximum computed by taking the algebraic mean, minimum and maximum values in each grid cell respectively, in feet above datum or ground level.
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)
Fig. 2 is an example of determining the 10% exceedance value from a specific element in the stored list of surge + tide values.