Skill assessment of Korea operational oceanographic system (KOOS)

For the ocean forecast system in Korea, the Korea operational oceanographic system (KOOS) has been developed and pre-operated since 2009 by the Korea institute of ocean science and technology (KIOST) funded by the Korean government. KOOS provides real time information and forecasts for marine environmental conditions in order to support all kinds of activities in the sea. Furthermore, more significant purpose of the KOOS information is to response and support to maritime problems and accidents such as oil spill, red-tide, shipwreck, extraordinary wave, coastal inundation and so on. Accordingly, it is essential to evaluate prediction accuracy and efforts to improve accuracy. The forecast accuracy should meet or exceed target benchmarks before its products are approved for release to the public.

In this paper, we conduct error quantification of the forecasts using skill assessment technique for judgement of the KOOS performance. Skill assessment statistics includes the measures of errors and correlations such as root-mean-square-error (RMSE), mean bias (MB), correlation coefficient (R), and index of agreement (IOA) and the frequency with which errors lie within specified limits termed the central frequency (CF).

The KOOS provides 72-hour daily forecast data such as air pressure, wind, water elevation, currents, wave, water temperature, and salinity produced by meteorological and hydrodynamic numerical models of WRF, ROMS, MOM5, WAM, WW3, and MOHID. The skill assessment has been performed through comparison of model results with in-situ observation data (Figure 1) for the period from 1 July, 2010 to 31 March, 2015 in Table 1 and model errors have been quantified with skill scores and CF determined by acceptable criteria depending on predicted variables (Table 2).  Moreover, we conducted quantitative evaluation of spatio-temporal pattern correlation between numerical models and observation data such as sea surface temperature (SST) and sea surface current produced by ocean sensor in satellites and high frequency (HF) radar, respectively.

Those quantified errors can allow to objective assessment of the KOOS performance and used can reveal different aspects of model inefficiency. Based on these results, various model components are tested and developed in order to improve forecast accuracy.