Applications of wavelet analysis in differential propagation phase shift data de-noising

Using numerical simulation data of the forward differential propagation shift （ΦDP） of polarimetric radar,the principle and performing steps of noise reduction by wavelet analysis are introduced in detail.Profiting from the multiscale analysis,various types of noises can be identified according to their characteristics in different scales,and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied,a default threshold strategy through which the threshold value is determined by the noise intensity,or a ΦDP penalty threshold strategy through which a special value is designed for ΦDP de-noising.Then,a hard-or soft-threshold function,depending on the de-noising purpose,is selected to reconstruct the signal.Combining the three noise suppression strategies and the two signal reconstruction functions,and without loss of generality,two schemes are presented to verify the de-noising effect by dbN wavelets：（1） the penalty threshold strategy with the soft threshold function scheme （PSS）; （2） the ΦDP penalty threshold strategy with the soft threshold function scheme （PPSS）.Furthermore,the wavelet de-noising is compared with the mean,median,Kalman,and finite impulse response （FIR） methods with simulation data and two actual cases.The results suggest that both of the two schemes perform well,especially when ΦDP data are simultaneously polluted by various scales and types of noises.A slight difference is that the PSS method can retain more detail,and the PPSS can smooth the signal more successfully.     

A Velocity Dealiasing Scheme for C-band Weather Radar Systems简

A dealiasing algorithm for radar radial velocity observed by C-band Doppler radars is presented as an extension of an existing S-band dealiasing algorithm. This has operational significance in that many portable and many commercial broadcast radars, as well as approximately one half of the Chinese weather radar network （CINRAD）, are C-band radars. With a wavelength of about 5 cm, the Nyquist interval of C-band radars is just about one half that of S-band radars （wavelength of about 10 cm） and thus has more velocity folding. The proposed algorithm includes seven modules to remove noisy data, find the starting radials, dealias velocities, and apply least squares error checking in both the radial and azimuth directions. The proposed velocity dealiasing method was applied to one widespread rain case and three strong convective cases from radars operating in China. It was found that, on average, 92.95% of the aliased radial velocity data could be correctly de-aliased by the algorithm, resulting in 96.65% of the data being valid.     

Spectrum Analysis of Wind Profiling Radar Measurements

Unlike previous studies on wind turbulence spectrum in the planetary boundary layer, this investigation focuses on high-altitude （1-5 km） wind energy spectrum and turbulence spectrum under various weather conditions. A fast Fourier transform （FFT） is used to calculate the wind energy and turbulence spectrum density at high altitudes （1-5 km） based on wind profiling radar （WPR） measurements. The turbulence spectrum under stable weather conditions at high altitudes is expressed in powers within a frequency range of 2 × 10-5-10-3 s-1, and the slope b is between -0.82 and -1.04, indicating that the turbulence is in the transition from the energetic area to the inertial sub-range. The features of strong weather are reflected less obviously in the wind energy spectrum than in the turbulence spectrum, with peaks showing up at different heights in the latter spectrum. Cold windy weather appears over a period of 1.5 days in the turbulence spectrum. Wide-range rainstorms exhibit two or three peaks in the spectrum over a period of 15-20 h, while in severe convective weather conditions, there are two peaks at 13 and 9 h. The results indicate that spectrum analysis of wind profiling radar measurements can be used as a supplemental and helpful method for weather analysis.     

Statistical Characteristics of Convective Initiation in the Beijing-Tianjin Region Revealed by Six-Year Radar Data

Characteristics of convective initiation (CI) in the Beijing-Tianjin region during the warm season of2008-2013 are examined. A total of 38877 CI cases are identified by a thunderstorm identification, tracking, analysis, and nowcasting algorithm. CI cases are evaluated in the context of associated terrain, weather systems, and land cover properties. The spatial distribution of all CI cases shows that there are dense CI activities around the 200-m elevation, which means that convective storms are more easily triggered over foothills. From 1500-1800 to 0300-0600 BT (Beijing Time), the high-occurrence CI region tends to propagate southeastward (i.e., from mountains to plains, then to ocean). Among the four local weather systems, the Mongolian cold vortex has the highest CI frequency while the after-trough system has the lowest CI frequency. For the land cover relationships with CI, the urban land cover has the highest CI density and the forest-type land cover has the second highest CI density; these two types of land cover are more conducive to CI formation.     

基于传输线法模拟探地雷达在多层介质模型中的传播

数值模拟是研究探地雷达电磁波在多层介质中传播规律的有效手段,传输线算法是一种基于Huygens原理的时域分析电磁场方法。这里阐述了TLM方法的基本原理及基本关系式,用TLM方法对多层介质进行了正演模拟,将模拟结果与FDTD方法合成结果进行了对比,结果表明了TLM用于GPR正演计算的正确性及有效性。应用该算法对已有模型进行了定性分析,验证了随着介电常数和电导率增大,雷达电磁波衰减加快的结论。     

巧用GIS空间叠置分析计算遮蔽角

雷达在某个方向上发现目标的最小高低角叫遮蔽角[1]。快速准确计算出气象雷达各方向的最大遮蔽角能够为雷达站的点位和天线高度提供设计依据。本文以某机场为例,在不考虑植被或建筑物高度的提前下,利用等高线数据,采用GIS的空间叠置分析功能快速计算该机场气象雷达每隔2。5°方向的最大遮蔽角。相比传统的断面计算法,GIS空间叠置分析方法大大提高了工作效率,希望能够引导读者注重GIS在工作中的应用。     

基于气象数据的地基雷达大气扰动校正方法研究

大气扰动是影响地基雷达(Ground-based SAR,GBSAR)系统监测精度的主要因素,文中研究大气扰动对GBSAR相位的影响及相应的补偿方法。实验以自制的角反射器作为监测目标,利用气象数据补偿法改正角反射器相位,用观测区域的温度、气压和湿度建立大气折射模型,估算出大气折射率的变化来校正大气扰动误差。结果表明,文中方法能有效地剔除大气扰动相位,达到改善监测精度的效果。     

机载激光雷达系统定位精度分析

从机载激光雷达系统误差的产生机制出发,分别对姿态角误差、DGPS误差、瞬时扫描角误差、激光测距误差对激光脚点定位精度的影响进行定量的分析,从理论上分析了机载激光雷达系统的定位精度。本文的结果对实际应用具有重要的参考价值。     

基于包络检波和STFT谱分析的探地雷达土壤分层信息识别

土壤分层信息,特别是表土层结构,对土地生产力具有重要影响,是评价土壤质量的一个重要指标。为了快速、准确地获取土壤分层信息,本文利用探地雷达对分层土壤进行了回波信号采集,并分别在时域和频域分析土壤层位置和层厚信息。首先在信号预处理的基础上,借助包络检波方法确定在土壤分层界面在时域上的位置;然后获取电磁波速度,得到土壤分层厚度。考虑到土壤介电常数与电磁波在土壤中传播速度的相关性,采用短时傅里叶变换方法（Short-time Fourier Transform,STFT）获取各土壤层时频域特征值,并利用回归分析建立特征值与介电常数之间的数学关系,实现对各土壤层的介电常数估算,从而计算出电磁波传播速度,进而确定土壤各层厚度。为验证算法的有效性,分别对理想模拟实验环境和农田环境进行了探地雷达实验,结果表明利用包络检波对探地雷达回波信息进行分析,土壤层检出率达到94.5%,借助STFT谱分析进行探地雷达回波速度估计,对于70 cm深度以上土层厚度计算误差大都保持在10%以下,但随着土壤深度的增加,误差变大。总体来说,本方法能有效识别浅层土壤的分层信息,可应用于实际生产中耕层厚度的估测。     

Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.