Abstract: With the reduced usage of the conventional energy and concerns of its environmental damage and air pollution if widely used, it becomes more and more important to exploit wind energy. In view of the sparse distribution of manual stations, it is necessary to add the data of automatic stations when assessing wind energy resources. However the time sequence of automatic stations is short, the short sequence adjusting method of wind speed and wind energy density by using the hourly wind speed data of automatic and manual stations in 2005 and manual stations during 1971—2000 of Jiangsu is discussed. According to the boundary layer meteorology the wind speed ratio between neighboring weather observation stations is affected by the turbulence intensity and the different roughness of land surface. The stronger the wind speed of manual station is, the smaller the ratio is. The wind of automatic station and neighboring manual station is affected much by geostrophic wind and it is less affected by turbulence. Using the inverse function to simulate the ratio in the same period, the correlativity between automatic station's wind speed and neighboring manual station's can be obtained easily. Then it is able to adjust the wind speed of automatic station in representative year according to the correlativity. The statistical connection with nearby stations is stable in the same period and in the same weather system. Therefore by using the statistical connection between average wind speed and wind energy density, the annual mean wind density is obtained in each representative year. The calculated results show that the average error percentage of annual average wind speed based on the short sequence adjusting method is 3.38%. Using the manual stations as hypothetical automatic stations to test the revised result on average climatic conditions, it is found that the average error percentage of annual average wind speed revision is 7.13%; the average error percentage of annual average wind energy density is 13.26%, and the maximal result is 21.98%, the minimal result is 4.46%. It is found that the short sequence adjusting method is effective in correcting the wind energy parameter. Finally after adding the corrected automatic stations' data, the distribution pattern of wind energy density in Jiangsu is obtained. The detailed wind energy distribution information is described by the distribution pattern especially over coastal and riverbank regions. It is better than the distribution pattern only with 67 manual stations. Scientific basis is provided by the results for the work of wind energy resources assessment.