The hourly wind series result from a hindcast in which the regional atmosphere model is driven with the NCEP/NCAR global re-analysis Dasatinib order in combination with spectral nudging. A detailed description of the atmosphere model and its validation are given by Weisse & Guenther (2007) and

Weisse et al. (2009). The hindcast wind series at five peninsula are analysed (Figure 1). The differences of wind time series among these points can be measured by the RMSE (Root Mean Square Error): equation(1) RMSEX,Y=∑i=1N(yi−Xi)2N,where XX = XXi, Y = yi are two separate data sets, each of N elements. By using the hourly wind series at Point 3 as the reference data, RMSE between the wind series at this point and other points are calculated and listed in Table 1. Here u represents Talazoparib supplier the east-west component of the wind (positive towards the

east) and v represents the north-south component of the wind (positive towards the north). Results indicate that the wind time series at these points are quite similar. As the hourly wind series at the five adjacent points are quite similar, we introduce here mainly the results of the statistical analysis at Point 3 as this point is closest to the western boundary of the local model, and statistical results indicate that the wind time series at Point 3 is closest to the mean value of the series at the five points (with a value of 0.34 ms−1 for the RMSE of component u   and 0.22 m s−1 for the RMSE of component v  ). Statistical results indicate that the southern Baltic Sea is dominated by westerly winds and the 50 year-averaged wind speed is 7.5 m s−1 in the Darss-Zingst area. The ratio of westerly winds (hours) to easterly winds (hours) is about 18:11. The distribution of wind directions of each month in this period shows that the winds in the Darss-Zingst area can be classified into four seasonal classes ( Figure 2). Each class has a

predominant distribution of wind direction. By combining the monthly average wind speed profiles, Class 1 (October, November, IKBKE December, January and February) can be identified as a winter class with relatively strong wind conditions; the prevailing wind direction is WSW. Class 3 (June, July and August) can be identified as a summer class with mild wind conditions dominated by the WNW winds. Class 2 (March, April and May) and Class 4 (September) are transitional classes with moderate wind conditions. Class 2 is dominated by the East-West balanced winds and Class 4 is dominated by westerly winds. The Weibull distribution is utilized to analyse the wind strength.