Measuring the Impact of Observations on the Predictability of the Kuroshio Extension in a Shallow-Water Model

In this paper, sequential importance sampling is used to assess the impact of observations on an ensemble prediction for the decadal path transitions of the Kuroshio Extension. This particle-filtering approach gives access to the probability density of the state vector, which allows the predictive power-an entropy-based measure-of the ensemble prediction to be determined. The proposed setup makes use of an ensemble that, at each time, samples the climatological probability distribution. Then, in a postprocessing step, the impact of different sets of observations is measured by the increase in predictive power of the ensemble over the climatological signal during one year. The method is applied in an identical-twin experiment for the Kuroshio Extension using a reduced-gravity shallow-water model. This study investigates the impact of assimilating velocity observations from different locations during the elongated and the contracted meandering states of the Kuroshio Extension. Optimal observation locations correspond to regions with strong potential vorticity gradients. For the elongated state the optimal location is in the first meander of the Kuroshio Extension. During the contracted state it is located south of Japan, where the Kuroshio separates from the coast.