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基于漂浮式测风激光雷达观测数据的海上预报风速订正
易侃1, 李肖雅2, 朱碧泓3, 顾晨3, 王浩1, 于淼2, 王彩霞2, 王言哲4, 杨靖文4
1. 中国长江三峡集团有限公司科学技术研究院,北京 100038;2. 青岛华航环境科技有限责任公司,山东 青岛 266000;3. 上海勘测设计研究院有限公司,上海 200434;4. 北京瑞科同创能源科技有限公司,北京 100071
摘要:
本文阐述了海上漂浮式测风设备对数据的测量方式和校准方法后,利用三部漂浮式测风激光雷达 50、80和100m三个高度的风速数据,对同地点和高度的GFS全球天气预报模式的风速数据进行预报订正。结 果表明,直接采用滑动自适应权重的Kalman滤波方法产生累积的滞后平均偏差对风速进行订正的结果并不理 想,统计发现通过降低预报订正公式中的滞后平均偏差项的比值,可以获得更好的标准偏差结果。滞后平均偏 差比值减小至0.1~0.5后经Kalman滤波方法订正后的风速标准偏差整体要低于订正前的风速标准偏差,其中最 佳的比值为0.3。利用这一滞后偏差比值对三部雷达不同预报时刻和预报时效的风速样本组进行订正后发现, 除了个别数据样本组外,超过90%数据样本组的风速标准偏差较订正前是减小的。经改进的算法订正后各高 度层风速预报值的改善率大致在5~20%范围内。
关键词:  漂浮式测风激光雷达  设备性能  偏差订正方法  预报改善率
DOI:10.16032/j.issn.1004-4965.2024.022
分类号:
基金项目:
Correction of Offshore Forecast Wind Speed Based on Floating LiDAR Observation Data
YI Kan1, LI Xiaoya2, ZHU Bihong3, GU Chen3, WANG Hao1, YU Miao2, WANG Caixia2, WANG Yanzhe4, YANG Jingwen4
1. Institute of Science and Technology, China Three Gorges Corporation, Beijing 100038, China;2. Qingdao Huahang Seaglet Environmental Technology Ltd., Qingdao, Shandong 266000, China;3. Shanghai Investigation, Design &Research Institute Co., Ltd., Shanghai 200434, China;4. Beijing RETEC New Energy Technology Co., Ltd., Beijing 100071, China
Abstract:
This study focused on correcting short-term wind speed forecasts extracted from GFS global forecast data sets at corresponding locations and heights. Data measurement mode and calibration method of the LiDAR data were discussed, and wind speed at three different heights (50m, 80m, and 100m) from floating LiDARs were employed. The results indicated that directly using the Kalman filter method with sliding adaptive weights to generate the average hysteresis deviation to correct the wind speed did not yield satisfactory results. Statistical analysis suggested that better standard deviation results can be obtained by lowering the ratio of the average hysteresis deviation in the forecast correction formula. When the average hysteresis deviation ratio was reduced to 0.1—0.5, the wind speed standard deviation after correction by the Kalman filtering method was generally lower than the wind speed standard deviation before correction, and the best ratio was 0.3. Using this hysteresis deviation ratio for wind speed correction at different forecast times and periods for these three LiDAR devices revealed that, except for individual data sets, more than 90% of the wind speed data sets exhibited reduced standard deviation after correction compared to the uncorrected data sets. After the introduction of this improved algorithm, the wind speed forecast improvement rate at each height ranged from 5% to 20%.
Key words:  floating LiDAR  equipment performance  bias-corrected method  forecast improvement ratio
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