SET Proceedings 2978-4387 Caravel Press 10.65582/set.2008.181 2008.181 China offshore Wind Energy Resources Assessment With the QuikSCAT Satellite Data and Numerical Simulation XuJingwei xujw@cma.gov.cn Luoyong yluo@cma.gov.cn ZhangXiuzhi ZhuRong XuMin Nanjing University of Information Science & Technology, Nanjing, China National Climate Center of CMA , Beijing, China 24082008 7 1 978-1-995850-08-5 1321 1328 24 August 2008 © 2008 Copyright WSSET 2008

Applying NASA remote sensing data from Quick Scatterometer (QuikSCAT) wind observations with the 12.5km resolution twice a day (1999 – current, 105-135° E, 15-45° N). And with the help of meteorological observation station wind data on an oil platform in west Bohai bay, and another on a small islet in South China Sea, we compare these two sets data and find out the QuikSCAT data is reliable. Then we interpolate QuikSCAT data into 0.1° grid, we calculate and analysis China offshore wind resources effective density of wind energy (EDWE) at 10 meters high above the sea level. And find that EDWE is rich in offshore area. The highest EDWE is in Taiwan Strait and North and South of the strait, with the EDWE 600-800W/m², the next highest is East China Sea and South China Sea. The lowest EDWE is Yellow Sea and Bohai bay, less than 400W/m². At the same time, The PSU/NCAR meso-scale model (known as MM5) is applied to simulate the wind speed in China Sea (12 km resolution) a year. And the wind resources maps at several heights (10m etc) come out. Compared the MM5 and satellite results in China Sea with the historical ships observation data, it is showed that the distribution of wind speed from satellite remote sensing and MM5 results are similar, but needs revised. To sum up numerical simulation and satellite inversion are effective methods for wind resources assessment on the sea.

 QuikSCAT wind energy resources assessment numerical simulation data comparison 24 to 27 August 2008 7th International Conference on Sustainable Energy Technologies SET2008 7 Seoul, Korea Naoto Ebuchi, Hans C Braber, Michael J Creuso, 2002: Evaluation ofwind vectors observed byQuikSCAT/SeaWinds using ocean data. Journal of Atmospheric and Oceanic Technology, 19: 2049 - 2062. QuikSCAT Science Data Product User’s Manual. version 3.0, D-18053-RevA, JPL. September 2006, 85p. R. Scott Dunbar, 2006: Level 2B Data Software Interface Specification (SIS-2), 686-644-3 Rev C, JPL, 52p. S. C. Pryor, M. Nielsen, and R. J. Barthelmie, 2004: Can satellite of offshore wind speed realistically represent wind speed distributions? Part II: Quantifying uncertainties associated with distribution fitting methods. J. Appl. Meteor., 43, 739-750 Barthelmie, R. J., and S. C. Pryor, 2003: Can satellite sampling of offshore wind speeds realistically represent wind speed distributions? J. Appl. Meteor., 42, 83-94. Liu Chunxia and He Xicheng, 2003: The analysis on the statistical character of QuikSCAT scatteromter winds and strong wind frequency using remote sensor data from QuikSCAT. J. Trop. Meteor.,19, 107-117 Yan Junyue, Cheng Qianjin, 1993: China Offshore Climate. Science Press, 109-140. Zhang de, Zhu Rong, 2008: Application of wind energy simulation toolkit (west) to wind energy numerical simulation of China. Plateau Meteor., 27, 200-2007