Journal cover Journal topic
Ocean Science An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/os-2017-35
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
23 May 2017
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Ocean Science (OS) and is expected to appear here in due course.
Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method
Reiner Onken Helmholtz-Zentrum Geesthacht (HZG), Max-Planck-Straße 1, 21502 Geesthacht, Germany
Abstract. A Relocatable Ocean Prediction System (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (Western Mediterranean) in the mainframe of the REP14-MED experiment. The observational data, comprising almost 5000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS) which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy (1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 hours, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available Personal Computer or a laptop.

Citation: Onken, R.: Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method, Ocean Sci. Discuss., https://doi.org/10.5194/os-2017-35, in review, 2017.
Reiner Onken
Reiner Onken

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Short summary
An ocean prediction model was driven by observations via assimilation. The best forecast was obtained using a smoothing scale of 12.5 km and a time window of 24 hours for data selection. Mostly, the forecasts were better than that of a run without assimilation, the skill score increased with increasing forecast range, and the score for temperature was higher than the score for salinity. It is shown that a vast number of data can be managed by the applied method without data reduction.
An ocean prediction model was driven by observations via assimilation. The best forecast was...
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