Blending Surface Currents from HF Radar Observations and Numerical Modeling: Tidal Hindcasts and Forecasts

• Published in: Journal of Atmospheric and Oceanic Technology
• Main Authors: Stanev, E.V., Ziemer, F., Schulz-Stellenfleth, J., Seemann, J., Staneva, J., Gurgel, K.
• Format: Article in Journal/Newspaper
• Language: English
• Published: AMS 2015
• Subjects: Arctic
• Online Access: https://publications.hereon.de/id/39675; https://publications.hzg.de/id/39675; https://www.hzg.de/imperia/md/content/gkss/zentrale_einrichtungen/bibliothek/journals/2015/stanev_39675.pdf

An observation network operating three Wellen Radars (WERAs) in the German Bight, which are part of the Coastal Observing System for Northern and Arctic Seas (COSYNA), is presented in detail. Major consideration is given to expanding the patchy observations over the entire German Bight on a 1-km grid and producing state estimates at intratidal scales, and 6- and 12-h forecasts. This was achieved with the help of the proposed spatiotemporal optimal interpolation (STOI) method, which efficiently uses observations and simulations from a free model run within an analysis window of one or two tidal cycles. In this way the method maximizes the use of available observations and can be considered as a step toward the “best surface current estimate.” The performance of the analysis was investigated based on the achieved reduction of the misfit between model and observations. The complex dynamics of the study domain was illustrated based on the spatial and temporal changes of tidal ellipses for the M2 and M4 constituents from HF radar observations. It was demonstrated that blending observations and numerical modeling facilitates physical interpretation of processes such as the nonlinear distortion of the Kelvin wave in the coastal zone and in particular in front of the Elbe and Weser estuaries. Comparisons with in situ data acquired outside the area covered by the HF radar demonstrated that the analysis method is able to propagate the HF radar information to larger spatial scales.