| 风廓线雷达反演大气比湿廓线的初步试验 |
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| 引用本文： | 孙康远,阮征,魏鸣,葛润生,董保举.风廓线雷达反演大气比湿廓线的初步试验[J].应用气象学报,2013,24(4):407-415. |
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| 作者姓名： | 孙康远 阮征 魏鸣 葛润生 董保举 |
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| 作者单位： | 1.南京信息工程大学，南京 210044 |
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| 基金项目： | 资助项目：国家自然科学基金项目(41075023),公益性行业(气象)科研专项(GYHY200906039)，中国气象科学研究院基本科研业务项目“大气水凝物微物理参数及风场垂直结构多波长遥感探测和反演方法研究”，“青藏高原观测预试验与资料分析研究” |
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| 摘 要： | 基于湍流散射理论，运用边界层风廓线雷达 (WPR) 联合RASS (Radio Acoustic Sounding System), GPS/PWV (Global Position System/Precipitable Water Vapor) 进行全遥感系统的大气比湿廓线反演试验，并对影响因子进行分析。利用2011年8—9月云南大理综合探测试验数据的反演结果与探空数据进行比较分析，结果表明:WPR联合探空的温度廓线和起始边界比湿 (q0) 反演大气比湿廓线，与探空大气比湿廓线相比具有相同的变化趋势，标准差为0.84 g·kg-1，误差随高度增加呈递增趋势；WPR联合RASS, GPS/PWV数据反演大气比湿廓线，与探空大气比湿廓线的标准差为0.85 g·kg-1。参加反演的数据中，折射指数结构常数Cn2与谱宽σturb2对反演影响最大，反演算法中大气折射指数梯度M符号的判断对反演精度也有较大影响。
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| 关 键 词： | 风廓线雷达 大气比湿廓线 大气折射指数梯度 折射指数结构常数 |
| 收稿时间： | 2012-10-12 |
Preliminary Estimation of Specific Humidity Profiles with Wind Profile Radar |
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| Sun Kangyuan,Ruan Zheng,Wei Ming,Ge Runsheng and Dong Baoju.Preliminary Estimation of Specific Humidity Profiles with Wind Profile Radar[J].Quarterly Journal of Applied Meteorology,2013,24(4):407-415. |
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| Authors: | Sun Kangyuan Ruan Zheng Wei Ming Ge Runsheng and Dong Baoju |
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| Affiliation: | 1.Nanjing University of Information Science & Technology, Nanjing 2100442.Chinese Academy of Meteorological Sciences, Beijing 1000813.Dali National Climate Observatory of Yunnan Province, Dali 671003 |
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| Abstract: | As a new type of detection instrument, wind profile radar (WPR) can detect meteorological factors such as wind profiles, spectral width, and refractive index structure constant and so on. The special detecting ability of WPR decides its broad application in atmospheric science research, meteorological operation application, climate research, aviation security and many other areas. With the advances of detection, a new specific humidity profiles retrieving method with WPR is proposed.Based on the turbulent backscattering theory, the method of estimating specific humidity profiles using boundary layer wind profile radar characteristics of clear-air echoes is devised. A retrieving test of specific humidity profiles are carried out with the data of observational campaign conducted from 15 Aug to 10 Sep in 2011 at a meteorological station of Dali, Yunnan Province, analyzing the main factors of retrieving accuracy. In the low atmosphere the refractive index gradient (M) is mainly influenced by three factors: dq/dz by 80.39%, the atmospheric temperature (T) by 12.75%, and the specific humidity q by 6.86% on average, respectively. Obviously, the dq/dz item is the most important factor, namely, there is a close relationship between the refractive index gradient and specific humidity.It is the measurement of refractive index gradient that turns out to retrieve specific humidity profiles with the help of WPR. The volume reflectivity (η) of turbulence echoes can indicate the fluctuation of atmosphere specific humidity due to the good correlation with M. Another factor that matters is the atmospheric turbulence dissipation rate which is under the influence of the signal spectral width observed by WPR. Radio Acoustic Sounding System (RASS) provides virtual temperature in retrieving of specific humidity profiles with the measurement of acoustic speed; PWV from GPS provides a method to obtain the initial boundary specific humidity; estimation of specific humidity profiles comparing with radiosonde data at the same time is conducted with WPR, RASS and GPS data.Results show that WPR can successfully retrieve specific humidity profiles with a certain degree of error. Among many factors that affect the retrieving accuracy, the determining of the sign of M, the refractive index structure constant and turbulence spectral width plays the key role, while the temperature and pressure are not so important. This new method can retrieve specific humidity profiles simply with the remote sensing instruments. The specific humidity retrieving from WPR, temperature profilers and initial boundary specific humidity from radiosonde shows the same trend comparing with which observed by radiosonde. The mean deviation and standard deviation turns out to be 0.75 g·kg-1 and 0.8 g·kg-1 respectively, both showing an increasing trend with height. With the assistance of WPR, GPS/PWV and RASS data, the retrieving mean deviation and standard deviation of specific humidity is 0.64 g·kg-1 and 0.85 g·kg-1 comparing with the observation of radiosonde. |
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