湖陆山地复杂地形下近地层风速预报研究
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摘要
以鄱阳湖区典型湖陆山地复杂地形为试验区域,采用WRF模式MRF和MYJ两种边界层参数化方案,对2010年该区域近地层风速进行高时空分辨率预报,并结合3个测风塔观测资料对预报结果进行检验。结果表明:WRF模式对鄱阳湖区70 m高度风速预报效果较好,预报值能够较好地反映近地层风速变化,且边界层MRF方案预报效果略好于MYJ方案。地形对近地层风速预报影响明显,地形相对平坦的吉山预报效果最好,而地形最为复杂的狮子山预报效果相对较差。不同强度的近地层风速预报效果差异较大,5～25 m·s~(-1)风速段预报效果明显优于0～5 m·s~(-1)风速段。位相偏差是造成鄱阳湖区近地层风速预报误差的主要来源,其贡献率在60%以上,而系统偏差和振幅偏差的误差贡献率相对较小,通过线性订正方法可在一定程度上提高该区域风速预报效果。
Taken the complex topography with lake, plain and mountains in Poyang Lake and its surrounding as a test area, the near ground wind speed with high temporal and spatial resolution was simulated under two boundary layer parameterized schemes(MRF and MYJ) of WRF model in 2010, and combined the observation data from three wind towers, the prediction results were tested. The results show that the effect of wind speed prediction at 70 m height by WRF model under two boundary layer parameterized schemes was well in complex underlying of Poyang Lake, the temporal change of predicted wind speed was in good agreement with the observations, and the prediction of wind speed with boundary layer of MRF scheme was slightly better than that of MYJ scheme. The influence of terrain on near ground wind speed prediction was obvious in Poyang Lake area, and the prediction of wind speed at 70 m height in Jishan with flat terrain was the best, while that in Shizishan with the most complex terrain was poor relatively. The forecast effect of wind speed with different levels was different. The forecast effect of 5-25 m·s~(-1) wind speed section was better than that of 0-5 m·s~(-1) wind speed section. The contribution rate of phase deviation was 60% and above as the main source influencing the error of wind speed prediction near surface layer of Poyang Lake area, while that of system deviation and amplitude deviation was small, so the effect of wind speed prediction could be improved to some extent through linear correction to system deviation and amplitude deviation.
Taken the complex topography with lake, plain and mountains in Poyang Lake and its surrounding as a test area, the near ground wind speed with high temporal and spatial resolution was simulated under two boundary layer parameterized schemes(MRF and MYJ) of WRF model in 2010, and combined the observation data from three wind towers, the prediction results were tested. The results show that the effect of wind speed prediction at 70 m height by WRF model under two boundary layer parameterized schemes was well in complex underlying of Poyang Lake, the temporal change of predicted wind speed was in good agreement with the observations, and the prediction of wind speed with boundary layer of MRF scheme was slightly better than that of MYJ scheme. The influence of terrain on near ground wind speed prediction was obvious in Poyang Lake area, and the prediction of wind speed at 70 m height in Jishan with flat terrain was the best, while that in Shizishan with the most complex terrain was poor relatively. The forecast effect of wind speed with different levels was different. The forecast effect of 5-25 m·s~(-1) wind speed section was better than that of 0-5 m·s~(-1) wind speed section. The contribution rate of phase deviation was 60% and above as the main source influencing the error of wind speed prediction near surface layer of Poyang Lake area, while that of system deviation and amplitude deviation was small, so the effect of wind speed prediction could be improved to some extent through linear correction to system deviation and amplitude deviation.
引文
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[19] 李玉鹏,王东海,尹金方.蒙西地区不同边界层参数化方案的近地层风场预报效果评估[J].中山大学学报(自然科学版),2018,57(4):16-29.
[20] 穆清晨,王咏薇,邵凯,等.山地复杂条件下三种边界层参数化方案对近地层风模拟精度初步评估分析[J].资源科学,2017,39(7):1349-1360.
[21] 张小培,银燕.复杂地形地区WRF模式四种边界层参数化方案的评估[J].大气科学学报,2013,36(1):68-76.
[22] LANGE M.On the uncertainly of wind power predictions-analysis of the forecast accuracy and statistical distribution of errors[J].Journal of Solar Energy Engineering,2005,127(2):177-184.
[23] LANGE M.Analysis of the uncertainty of wind power predictions[D].Oldenburg:University of Oldenburg,2003:28-29.
[2] 吴琼,聂秋生,周荣卫,等.江西省山地风场风能资源储量及特征分析[J].自然资源学报,2013,28(9):1605-1614.
[3] 张宇,郭振海,林一骅,等.中尺度模式风电场风速短期预报能力研究[J].大气科学,2013,37(4):955-962.
[4] 何晓凤,周荣卫,孙逸涵.3个全球模式对近地层风场预报能力的对比检验[J].高原气象,2014,33(5):1315-1322.
[5] 周荣卫,何晓凤,孙逸涵.3种全球环流预报场近地层风速突变预报能力评估[J].资源科学,2013,35(10):2121-2129.
[6] 阎丽凤,盛春岩,肖明静,等.MM5、WRF-RUC及T639模式对山东沿海风力预报分级检验[J].气象科学,2013,33(3):340-346.
[7] 石岚,徐丽娜,郝玉珠,等.BJ-RUC在内蒙古某风电场的风速预报检验分析[J].干旱区地理,2015,38(3):510-516.
[8] 刘丽珺,梁友嘉.BJ-RUC模式的低层风场预报产品质量检验——以甘肃省当金山风电场为例[J].干旱气象,2016,34(4):743-751.
[9] 张驰,王东海,巩远发.基于WRF/CALMET的近地面精细化风场的动力模拟试验研究[J].气象,2015,41(1):34-44.
[10] 孙玉婷,粘新悦,闵锦忠,等.中国沿海风能分布特征及其影响因子的数值模拟[J].大气科学学报,2017,40(6):823-832.
[11] 杨明祥,王浩,蒋云钟,等.基于数值模拟的雅砻江流域风能资源初步评估[J].清华大学学报(自然科学版),2018,58(1):101-107.
[12] 于丽娟,尹承美 ,林应超,等.中国区域近地面风速动力降尺度研究[J].干旱气象,2017,35(1):23-28.
[13] 周荣卫,何晓凤.海面动力学粗糙度参数化方案对近海风资源评估结果的影响[J].自然资源学报,2015,30(3):513-522.
[14] 马媛媛,杨毅,胡小明,等.WRF中三种边界层参数化方案对新疆“2.28”大风过程模拟的对比分析[J].沙漠与绿洲气象,2014,8(3):8-18.
[15] 王澄海,胡菊,靳双龙,等.中尺度WRF模式在西北西部地区低层风场模拟中的应用和检验[J].干旱气象,2011,29(2):161-167.
[16] 杨光焰,吴息,周海.WRF模式对福建沿海风电场风速预测的效果分析[J].气象科学,2014,34(5):530-535.
[17] 孙逸涵,程兴宏,柳艳香,等.不同参数化方案对风预报效果影响个例研究[J].气象科技,2013,41(5):870-877.
[18] 李艳,成培培,路屹雄,等.典型复杂地形风能预报的精细化研究[J].高原气象,2015,34(2):413-425.
[19] 李玉鹏,王东海,尹金方.蒙西地区不同边界层参数化方案的近地层风场预报效果评估[J].中山大学学报(自然科学版),2018,57(4):16-29.
[20] 穆清晨,王咏薇,邵凯,等.山地复杂条件下三种边界层参数化方案对近地层风模拟精度初步评估分析[J].资源科学,2017,39(7):1349-1360.
[21] 张小培,银燕.复杂地形地区WRF模式四种边界层参数化方案的评估[J].大气科学学报,2013,36(1):68-76.
[22] LANGE M.On the uncertainly of wind power predictions-analysis of the forecast accuracy and statistical distribution of errors[J].Journal of Solar Energy Engineering,2005,127(2):177-184.
[23] LANGE M.Analysis of the uncertainty of wind power predictions[D].Oldenburg:University of Oldenburg,2003:28-29.