Forecast performance of different centres for forecasts initialised at 00 UTC and 12 UTC are shown in terms of scatter index over the Northern-hemisphere and the Mediterranean Sea for:
Data
Sea state and ocean surface meteorological in-situ observations are routinely collected by several national organisations via networks of moored buoys or weather ships and fixed platforms deployed in their near-shore and offshore areas of interest. The data are usually exchanged via the GTS. As part of this intercomparison, observations that are not commonly available on the GTS are also gathered on a case-by-case basis.
Before using observations for verification, care has to be taken to process the data to remove any erroneous observations. Moreover, extra care has to be taken to match the scale of both model and observations. This scale matching is achieved by averaging the hourly data in ±1 hour time windows centered on the four major synoptic times corresponding to the normal model output times. The original quality control and averaging procedure was discussed in Bidlot et al. (2002). It was extended to include platform data as described in Sætra and Bidlot (2004).
Scatter index
The scatter index is a measure of the size of the deviation of forecasts from observations relative to the magnitude of the observations. A smaller scatter index value means better forecasts.
Mathematically the scatter index is defined as the standard deviation of the difference between predicted values and observations normalised by the mean of the observations. For example, if the standard deviation of the difference between predicted values of significant wave height and observations is 0.5 metres and the mean of the observations is 2 metres, then the scatter index value is 0.5/2, which is 25%.
References
Bidlot J.-R., D. J. Holmes, P. A. Wittmann, R. Lalbeharry, H. S. Chen, 2002: Intercomparison of the performance of operational ocean wave forecasting systems with buoy data. Wea. Forecasting, 17, 287-310.
Sætra, Ø. and J.-R. Bidlot, 2004: On the potential benefit of using probabilistic forecast for waves and marine winds based on the ECMWF ensemble prediction system. Wea. Forecasting, 19, 673-689.