Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

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 5 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) 10 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. IntroductionA Relocatable Ocean Prediction System (ROPS) is presented which enables rapid nowcasts and forecasts of ocean environmen-15 tal parameters in limited regions. In this study, ROPS was implemented for the waters west of Sardinia (Western Mediterranean Sea) within the framework of the REP14-MED experiment (Onken et al. (2014, 2017a)).The major components of ocean operational systems are observations and ocean circulation models coupled with data assimilation systems, to combine the observations with dynamics and issue nowcasts and forecasts which are delivered to the customers. While systems on global scale are utilised to provide estimates on large-scale circulation patterns and associated 20 features, regional operational systems are focusing more on societally relevant oceanographic information for e.g. search and rescue operations, pollutant dispersal, fishery management , and military applications. Meanwhile, quite a number of real-time ocean operational systems are available, spanning the scales of ocean horizontal circulation patterns from global to coastal (Dombrowsky (2011), Zhu (2011)). significant improvement of the forecast skill. For extremely small forecast ranges, the skill score even became negative, because the assimilation disequilibrated the balance of forces in the dynamical model.In all ROPS runs, including the run without assimilation, the skill score for temperature was mostly higher than the corresponding score for salinity. This is in a...