摘要
多普勒激光雷达在台风等强天气背景下的探测能力亟待研究,为此将多普勒激光雷达与70 m测风塔超声风温仪在同址同高度探测台风“利奇马”影响期间的边界层风场数据进行对比,并分析多普勒激光雷达的误差分布以及变化情况。结果显示:在高度70 m上,两者的水平风速、风向相关系数分别为0.97和0.99,垂直风速的相关系数为0.36。以超声风温仪为参考值,激光雷达水平风速、垂直风速和风向均方根误差分别为1.06 m/s、0.46 m/s和17.10°。深入研究表明:降水对多普勒激光雷达测量水平风速和垂直风速误差均有一定影响。当激光雷达信噪比大于2000时,各参量的误差与信噪比呈负相关关系。研究表明多普勒激光雷达至少可以较好地刻画台风环流内的水平风场结构及演变,可应用于台风外围环流影响下(即较弱降雨条件下)边界层风场的高分辨率探测和研究。
The detection capability of Doppler wind lidar(DWL)in strong weather such as typhoon needs to be studied urgently.In the present study,the boundary layer wind field data collected by Doppler lidar and 70 meter wind tower ultrasonic wind thermometer at the same location and height are compared,and the error distribution and variation of Doppler lidar data are analyzed.The results show that,at the height of 70 meters,the correlation coefficients of horizontal wind speed and wind direction are 0.97 and 0.99,respectively,and the correlation coefficient of vertical wind speed is 0.36.With ultrasonic wind thermometer data as reference,it is found that the root mean square errors of horizontal wind speed,vertical wind speed and wind direction of lidar are 1.06 m/s,0.46 m/s and 17.10℃,respectively.Precipitation can affect the horizontal and vertical wind speeds measured by DWL.When the signal-tonoise ratio of lidar is greater than 2000,the error of each parameter is negatively correlated with the signal-to-noise ratio.The detection results show that DWL can characterize the distribution and evolution of horizontal wind field,and thus can be used in the high-resolution detection and research of boundary layer wind field under the influence of typhoon peripheral circulation,i.e.,under weak rainfall conditions.
作者
史文浩
汤杰
陈勇航
赵兵科
汤胜茗
杨文杰
邬贤文
SHI Wen-hao;TANG Jie;CHEN Yong-hang;ZHAO Bing-ke;TANG Sheng-ming;YANG Wen-jie;WU Xian-wen(Donghua University,Shanghai 201600,China;Shanghai Typhoon Institute,China Meteorological Administration,Shanghai 200030,China)
出处
《热带气象学报》
CSCD
北大核心
2020年第5期577-589,共13页
Journal of Tropical Meteorology
基金
国家重点研发计划国际合作专项(2017YFE0107700)
国家重点研发计划(2018YFC1506305)
国家自然科学基金(41775065、41475060、41805088)
上海市自然科学基金(18ZR1449100、19dz1200101)共同资助