Representing Low Frequency, Spatially Varying Water Level Anomalies in Storm Surge Computations

Accurate predictions of storm surge and accompanying coastal inundation depend on an accounting of all of the significant processes that affect water levels.  State of the art models routinely include the influence of astronomical tides and surface waves, while linkages with precipitation / hydrologic models, to provide predictions of the coastal impacts of freshwater flows in major rivers, are becoming more common as well.  However, the coastal ocean is also subject to long period (e.g., weeks), spatially varying water level variations of up to a meter, due to phenomena such as steric expansion of the water column, the influence of major oceanic currents like the Gulf Stream and other oceanic processes.  Thus far, storm surge models have not accounted for such water level anomalies, except to add a spatially and seasonally constant water level offset to account for an average steric effect.  Initialization of a storm surge model with a spatially varying, low frequency elevation field is problematic since there are no internal dynamics within the model to preserve the integrity of this field during a multi-day storm surge prediction. 

            In this talk we report on several potential solutions to this low frequency water level initialization problem that improve storm surge forecasts in cases where storms impact large areas and therefore spatially constant water level initial conditions are not adequate.