| 深圳356米气象塔观测数据的质量控制方法和空气动力学参数研究 |
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| 引用本文： | 谢洁岚,卢超,高瑞泉,王宝民,孙天乐,何龙,范绍佳.深圳356米气象塔观测数据的质量控制方法和空气动力学参数研究[J].热带气象学报,2020,36(2):189-198. |
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| 作者姓名： | 谢洁岚 卢超 高瑞泉 王宝民 孙天乐 何龙 范绍佳 |
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| 作者单位： | 1.中山大学大气科学学院, 广东 珠海 519082 |
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| 基金项目： | 国家重点研发计划课题2017YFC0209606国家自然科学基金重点项目41630422国家重点研发项目课题2016YFA0602701 |
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| 摘 要： | 气象高塔数据资料弥足珍贵, 对其进行质量控制将为后续科学研究和业务工作的开展提供便利; 此外, 利用塔基观测资料计算空气动力学参数有助于校正模式空气动力学参数理论值。对2017-2018年深圳356 m气象梯度观测塔共13层的每10 s风速、风向、相对湿度、温度探测资料进行数据质量控制, 基于莫宁-奥布霍夫相似理论和数据质量控制后的气象梯度观测塔近地层(10 m、20 m、40 m、50 m和80 m) 1分钟平均的风温资料, 利用最小二乘法拟合迭代计算了近中性条件下深圳气象梯度观测塔下垫面空气动力学粗糙度(z0)和零平面位移(d)。结果表明:深圳气象梯度观测塔的气象探测资料数据质量很高, 连续两年平均数据缺失率为1.28%, 数据错误率为0.01%。近中性边界层条件下, 深圳气象梯度观测塔下垫面空气动力学粗糙度均值为0.35 m, 零平面位移均值为5.33 m, 结果合理可信。研究表明空气动力学参数受下垫面非均匀性、植株柔软性、气流来向、风速等的共同影响。
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| 关 键 词： | 塔基梯度探测 数据质量控制 粗糙度 零平面位移 深圳356 m气象梯度观测塔 |
| 收稿时间： | 2019-10-15 |
DATA PROCESSING METHOD OF THE METEOROLOGICAL DATA FROM THE 356-METER METEOROLOGICAL TOWER IN SHENZHEN AND RESEARCH ON AERODYNAMIC PARAMETERS |
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| XIE Jie-lan,LU Chao,GAO Rui-quan,WANG Bao-min,SUN Tian-le,HE Long,FAN Shao-jia.DATA PROCESSING METHOD OF THE METEOROLOGICAL DATA FROM THE 356-METER METEOROLOGICAL TOWER IN SHENZHEN AND RESEARCH ON AERODYNAMIC PARAMETERS[J].Journal of Tropical Meteorology,2020,36(2):189-198. |
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| Authors: | XIE Jie-lan LU Chao GAO Rui-quan WANG Bao-min SUN Tian-le HE Long FAN Shao-jia |
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| Institution: | 1.School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China2.Shenzhen National Climate Observatory, Shenzhen, Guangdong 518040, China3.Shenzhen Environment Monitoring Center, Shenzhen, Guangdong 518049, China4.Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou 510275, China5.Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai), Zhuhai, Guangdong 519082, China |
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| Abstract: | Data collected from high meteorological tower is extremely valuable and data processing will facilitate future scientific research and meteorological service. Besides, calculation of aerodynamic parameters using tower-based observation data will help correct theoretical aerodynamic parameter values in numerical models. In our paper, data processing is conducted on meteorological data from the 356-m meteorological tower in Shenzhen, including data of wind speed, wind direction, relative humidity and temperature at 13 layers with a time interval of 10 seconds from 2017 to 2018. Based on the Monin-Obukhov similarity theory, the least squares fitting is applied to preprocessed wind and temperature data at near surface layers, i.e., layers at the altitudes of 10 m, 20 m, 40 m, 50 m, and 80 m of the meteorological tower, in order to calculate the aerodynamic roughness length (z0) and the zero plane displacement (d). The results show that the quality of meteorological data from the meteorological tower in Shenzhen is remarkably high. The average data loss rate is 1.28% and the data error rate is 0.01%. Under the condition of near-neutral boundary layer, the mean value of the aerodynamic roughness length of the underlying surface of the meteorological tower in Shenzhen is 0.35m, and the mean value of zero plane displacement is 5.33 m. Compared with previous studies, these results are reasonable and reliable. We find that the aerodynamic parameters are affected by the unevenness and plant softness of the underlying surface, as well as wind direction and wind speed. |
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| Keywords: | meteorological tower observation data processing roughness length zero plane displacement 356-m meteorological tower in Shenzhen |
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