江淮夏季不同对流过程的无人机边界层观测特征分析  

Observation characteristics of the atmospheric boundary layer using rotorcraft UAVs for different convective processes in the Jianghuai region during summer

作　　者：李厚凝 李昕 李煜斌[4] 吴泓 曾明剑 LI Houning;LI Xin;LI Yubin;WU Hong;ZENG Mingjian(Chinese Academy of Meteorological Sciences,Beijing 100081,China;Nanjing Innovation Institute for Atmospheric Sciences,Chinese Academy of Meteorological Sciences-Jiangsu Meteorological Service,Nanjing 210041,China;Jiangsu Key Laboratory of Severe Storm Disaster Risk/Key Laboratory of Transportation Meteorology of CMA,Nanjing 210041,China;School of Atmospheric Physics,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]中国气象科学研究院,北京100081 [2]中国气象科学研究院-江苏省气象局南京气象科技创新研究院,江苏南京210041 [3]江苏省强对流灾害风险预警重点实验室/中国气象局交通气象重点开放实验室,江苏南京210041 [4]南京信息工程大学大气物理学院,江苏南京210044

出　　处：《大气科学学报》2025年第1期136-151,共16页Transactions of Atmospheric Sciences

基　　金：江苏省自然科学基金项目(BK20211396);国家重点研发计划青年科学家项目(2022YFC3080500);国家自然科学基金项目(42322507,42275167);江苏省科技支撑计划项目(BE2022851);江苏省科研基金重点项目(KZ202304);中国气象服务协会气象科技创新平台重点项目(CMSA2023ZA001)。

摘　　要：利用2019年7—10月江苏盐城5个站点的旋翼无人机观测资料,对夏季对流天气发生前的大气边界层特征进行分析。选取ERA5和L波段雷达探空对旋翼无人机观测的温度、湿度和风场数据进行了观测偏差和误差的定量化估计。结果表明,在不同高度层,无人机观测温度与探空观测的平均偏差为-0.2~0.02℃,观测误差为0.44~0.59℃;相对湿度与ERA5的平均偏差为1.27%~7.14%,观测误差为7.14%~10.71%;风速、风向与探空观测的平均偏差分别为0.40~1.34 m/s和-3.87°~4.98°,观测误差分别为1.24~1.62 m/s和10.50°~23.96°。各变量同两个参考数据集具有较好的一致性,数据准确度满足边界层分析要求。针对3类不同天气背景下的夏季对流天气过程进行了边界层特征分析,无人机观测揭示了重要的信息:对于非天气尺度强迫的弱降水个例,无人机观测捕捉到了降水前边界层顶逆温层高度变化和局地风场辐合,边界层内的状态变化解释了弱降水成因;对于天气尺度强迫且主导系统位置稳定的降水个例,降水由大尺度低层不稳定导致,边界层特征呈现出对大尺度强迫的响应;对于天气尺度强迫且系统快速移动的降水个例,因平流引起边界层内气象要素迅速变化,边界层高度由于冷空气入侵急剧下降,近地层出现风向与主导系统移动方向一致的风速大值区。This study leverages rotorcraft Unmanned Aerial Vehicle(UAV)observations from five stations near Yancheng city,Jiangsu Province,collected between July and October 2019,to analyze atmospheric boundary layer characteristics during summer convective weather.ERA5 analysis and L-band sounding observations were used to quantitatively evaluate biases and errors in temperature,humidity,and wind fields derived from UAV observations.At different vertical levels,the average temperature deviations between UAV and sounding observations ranged from-0.2 to 0.02℃,with observation errors between 0.44 and 0.59℃.Average humidity deviations between UAV observations and ERA5 reanalysis were 1.27%to 7.14%,with corresponding observation errors of 7.14%to 10.71%.Deviations in wind speed and direction were 0.40 to 1.34 m/s and-3.87°to 4.98°,respectively with errors of 1.24 to 1.62 m/s and 10.50°to 23.96°.The triangular cap method,used for error estimation assumes mutual independence of datasets,leading to inherent uncertainties in standard deviation calculations.Observational errors derived via this method depend on discrepancies between reference datasets;large discrepancies yield smaller observational errors,whereas smaller discrepancies increase the errors.Despite these limitations,the overall accuracy of UAV data aligns with the reference datasets,meeting requirements for boundary layer analysis.The boundary layer characteristics during three distinct convective weather scenarios were examined:1)In the case of weak synoptic-scale forcing,precipitation was driven caused by localized wind convergence and changes in inversion layer height prior to precipitation,as captured by UAV observations.Intense wind shear below the lifting condensation level was identified as a precursor to precipitation events;2)In the case of strong synoptic-scale forcing,precipitation was caused by large-scale low-level instability,evidenced by an unstable superadiabatic layer within the boundary layer.The boundary layer characteristics showed clear res

关 键 词：无人机观测 对流天气过程 边界层观测特征 

分 类 号：P412.24[天文地球—大气科学及气象学]