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The use of High Frequency Radar (HFR) data is increasing worldwide for operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian based empirical real-time, Short-Term Prediction (L-STP) system is presented in order to provide short term forecasts of up to 48 hours of ocean currents from HFR data. The method is based on the finding of historical gridded analogues of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as did the historical analogue, we obtain a short-term prediction of the surface currents. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. The L-STP improves on previous prediction systems implemented for the SE Bay of Biscay and provides good results for the Red Sea study area. A comparison of the L-STP methodology with predictions based on persistence and reference fields has been performed in order to quantify the error introduced by this Lagrangian approach. Furthermore, a temporal sensitivity analysis has been addressed to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill-score has been developed using the results of this analysis which allows to identify periods when the short-term prediction performance is more likely to be low and persistence can be used as a better predictor for the future currents. [ABSTRACT FROM AUTHOR]

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the idea of extending the Taylor diagram to two dimensional quantities such as currents, wind, or horizontal fluxes of water vapour, salinity, energy and other geophysical variables. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the empirical orthogonal functions of the data. We test the performance of this new diagram to identify the differences amongst the reference dataset and the different model outputs by using examples with wind, current, vertically integrated moisture transport and wave energy flux time series. An alternative setup is shown in the last examples, where the spatial average of surface wind in the Northern and Southern Hemispheres according to different reanalyses and realizations of CMIP5 models are compared. The examples of use of the Sailor diagram presented show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows also to easily retrieve the individual diagnostics of the different components of the mean squared error. [ABSTRACT FROM AUTHOR]