Low frequency water level correction in storm surge models using data assimilation

• Published in: Ocean modelling (Oxford) 2019-12, Vol.144, p.101483-None, Article 101483
• Main Authors: Asher, Taylor G., Luettich Jr, Richard A., Fleming, Jason G., Blanton, Brian O.
• Format: Article
• Language: English
• ISSN: 1463-5003; 1463-5011
• DOI: 10.1016/j.ocemod.2019.101483

Research performed to-date on data assimilation (DA) in storm surge modeling has found it to have limited value for predicting rapid surge responses (e.g., those accompanying tropical cyclones). In this paper, we submit that a well-resolved, barotropic hydrodynamic model is typically able to capture the surge event itself, leaving slower processes that determine the large scale, background water level as primary sources of water level error. These “unresolved drivers” reflect physical processes not included in the model’s governing equations or forcing terms, such as far field atmospheric forcing, baroclinic processes, major ocean currents, steric variations, or precipitation. We have developed a novel, efficient, optimal interpolation-based DA scheme, using observations from coastal water level gages, that dynamically corrects for the presence of unresolved drivers. The methodology is applied for Hurricane Matthew (2016) and results demonstrate it is highly effective at removing water level residuals, roughly halving overall surge errors for that storm. The method is computationally efficient, well-suited for either hindcast or forecast applications and extensible to more advanced techniques and datasets. •A water level data assimilation method for coastal hydrodynamic models is developed.•The method is tested with 3 sources of meteorological forcing for Hurricane Matthew.•The method roughly halves modeled storm surge errors.•It corrects for background water levels and errors in far-field meteorology.•It increases computational burden by roughly 2–8% in operational forecasts.