Blending of satellite and tide gauge sea level observations and its assimilation in a storm surge model of the North Sea and Baltic Sea. Issue 9 (23rd September 2015)

• Record Type: Journal Article
• Authors: Madsen, Kristine S.; Høyer, Jacob L.; Fu, Weiwei; Donlon, Craig
• Is Part Of: Journal of geophysical research. Volume 120:Issue 9(2015:Sep.)
• Page Start: 6405
• Page End: 6418
• Publication Date: 2015-09-23
• Subjects: Oceanography -- Periodicals; 551.4605
• DOI: 10.1002/2015JC011070
• Languages: English
• ISSNs: 2169-9275

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Coastal storm surge forecasts are typically derived from dedicated hydrodynamic model systems, relying on Numerical Weather Prediction (NWP) inputs. Uncertainty in the NWP wind field affects both the preconditioning and the forecast of sea level. Traditionally, tide gauge data have been used to limit preconditioning errors, providing point information. Here we utilize coastal satellite altimetry sea level observations. Careful processing techniques allow data to be retrieved up to 3 km from the coast, combining 1 Hz and 20 Hz data. The use of satellite altimetry directly is limited to times when the satellite passes over the area of interest. Instead, we use a stationary blending method developed by Madsen et al. (2007) to relate the coastal satellite altimetry with corresponding tide gauge measurements, allowing generation of sea level maps whenever tide gauge data are available. We apply the method in the North Sea and Baltic Sea, including the coastal zone, and test it for operational nowcasting and hindcasting of the sea level. The feasibility to assimilate the blended product into a hydrodynamic model is assessed, using the ensemble optimal interpolation method. A 2 year test simulation shows decreased sea level root mean square error of 7–43% and improved correlation by 1–23% in all modeled areas, when validated against independent tide gauges, indicating the feasibility to limit preconditioning errors for storm surge forecasting, using a relatively cost effective assimilation scheme.</p> </abstract>