Augmenting the BSH operational forecasting system by in situ data assimilation


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Svetlana.Losa [ at ] awi.de

Abstract

Quality of the forecast provided by the German Maritime and Hydrographic Agency (BSH) for the North and Baltic Seas had been previously improved by assimilating satellite sea surface temperature SST (project DeMarine, Losa et al., 2012). We investigate possible further improvements using in situ observational temperature and salinity data: Marnet time series and CTD and ScanFish measurements. To assimilate the data, we implement the Singular Evolutive Interpolated Kalman (SEIK) filter (Pham et al., 1998). The SIEK analysis is performed locally (Nerger et al. 2006) accounting for/assimilating the data within a certain radius. In order to determine suitable localisation conditions for Marnet data assimilation, the BSHcmod error statistics have been analysed based on LSEIK filtering every 12 hours over a one year period (September 2007 – October 2008) given a 12-hourly composites of NOAA’s SST and under the experiment conditions corresponding the maximum entropy. The principle of Maximum Entropy is also used as an additional criterion of plausibility of the augmented system performance.



Item Type
Conference (Poster)
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Not peer-reviewed
Publication Status
Published
Event Details
MyOcean Science Days, 19 Nov 2012 - 21 Nov 2012, Geesthacht (Germany).
Eprint ID
31549
Cite as
Loza, S. , Danilov, S. , Schröter, J. , Nerger, L. , Maßmann, S. and Janssen, F. (2012): Augmenting the BSH operational forecasting system by in situ data assimilation , MyOcean Science Days, Geesthacht (Germany), 19 November 2012 - 21 November 2012 .


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