Improving Satellite-Retrieved Cloud Base Height with Ground-Based Cloud Radar Measurements

Cloud base height(CBH) is a crucial parameter for cloud radiative effect estimates, climate change simulations, and aviation guidance. However, due to the limited information on cloud vertical structures included in passive satellite radiometer observations, few operational satellite CBH products are currently available. This study presents a new method for retrieving CBH from satellite radiometers. The method first uses the combined measurements of satellite radiometers and ground-based cloud radars to develop a lookup table(LUT) of effective cloud water content(ECWC), representing the vertically varying cloud water content. This LUT allows for the conversion of cloud water path to cloud geometric thickness(CGT), enabling the estimation of CBH as the difference between cloud top height and CGT. Detailed comparative analysis of CBH estimates from the state-of-the-art ECWC LUT are conducted against four ground-based millimeter-wave cloud radar(MMCR) measurements, and results show that the mean bias(correlation coefficient) is0.18±1.79 km(0.73), which is lower(higher) than 0.23±2.11 km(0.67) as derived from the combined measurements of satellite radiometers and satellite radar-lidar(i.e., Cloud Sat and CALIPSO). Furthermore, the percentages of the CBH biases within 250 m increase by 5% to 10%, which varies by location. This indicates that the CBH estimates from our algorithm are more consistent with ground-based MMCR measurements. Therefore, this algorithm shows great potential for further improvement of the CBH retrievals as ground-based MMCR are being increasingly included in global surface meteorological observing networks, and the improved CBH retrievals will contribute to better cloud radiative effect estimates.

Time-varying baseline error correction method for ground-based micro-deformation monitoring radar

In recent years, ground-based micro-deformation monitoring radar has attracted much attention due to its excellent monitoring capability. By controlling the repeated campaigns of the radar antenna on a fixed track, ground-based micro-deformation monitoring radar can accomplish repeat-pass interferometry without a space baseline and thus obtain highprecision deformation data of a large scene at one time. However, it is difficult to guarantee absolute stable installation position in every campaign. If the installation position is unstable, the stability of the radar track will be affected randomly, resulting in time-varying baseline error. In this study, a correction method for this error is developed by analyzing the error distribution law while the spatial baseline is unknown. In practice, the error data are first identified by frequency components, then the data of each one-dimensional array(in azimuth direction or range direction) are grouped based on numerical distribution period, and finally the error is corrected by the nonlinear model established with each group.This method is verified with measured data from a slope in southern China, and the results show that the method can effectively correct the time-varying baseline error caused by rail instability and effectively improve the monitoring data accuracy of groundbased micro-deformation radar in short term and long term.

Constrained geometry analysis to resolve 3-D deformations from three ground-based radars

When multiple ground-based radars(GB-rads)are utilized together to resolve three-dimensional(3-D)deformations,the resolving accuracy is related with the measurement geometry constructed by these radars.This paper focuses on constrained geometry analysis to resolve 3-D deformations from three GB-rads.The geometric dilution of precision(GDOP)is utilized to evaluate 3-D deformation accuracy of a single target,and its theoretical equation is derived by building a simplified 3-D coordinate system.Then for a 3-D scene,its optimal accuracy problem is converted into determining the minimum value of an objective function with a boundary constraint.The genetic algorithm is utilized to solve this constrained optimization problem.Numerical simulations are made to validate the correctness of the theoretical analysis results.

An approach to wide-field imaging of linear rail ground-based SAR in high squint multi-angle mode

Ground-based synthetic aperture radar(GB-SAR) has been successfully applied to the ground deformation monitoring.However, due to the short length of the GB-SAR platform, the scope of observation is largely limited. The practical applications drive us to make improvements on the conventional linear rail GB-SAR system in order to achieve larger field imaging. First, a turntable is utilized to support the rotational movement of the radar.Next, a series of high-squint scanning is performed with multiple squint angles. Further, the high squint modulation phase of the echo data is eliminated. Then, a new multi-angle imaging method is performed in the wave number domain to expand the field of view. Simulation and real experiments verify the effectiveness of this method.

SIMULATION AND ANALYSIS ON OBSERVATIONAL ERRORS OF CLOUD INTENSITY AND STRUCTURE WITH TRMM PR AND GROUND-BASED RADAR

In order to explain theoretically the observational biases of reflectivity and structure of precipitation systems by TRMM Precipitation Radar (TRMM PR) and ground-based radar,the effects of wavelengths,incident direction of radar waves and radar beam width on the reflectivity observation are simulated.The results show that the error due to the different wavelength and incident direction of radar wave is within 2.0 dB,TRMM PR can observe a larger reflectivity than ground-based radar in echo center.TRMM PR smoothes the cloud structure,overestimates and underestimates reflectivity by 3-5 dB in strong and week echo areas,respectively.Beam width and long distance from TRMM PR to target cause it to overestimate the large echo area and area integrated rainfall amount,and to underestimate the averaged refleetivity.The theoretical results above can only explain part of observational facts,meaning that the comparison of observation results between TRMM PR and ground-based radar is complicated,the attenuation of radar wave within precipitation area is the main factor to affect the observed result.

Spaceborne-airborne-ground-based" radar remote sensing experiment

Much work has been done on radar calibration in recent years since the inversion and classification algorithms rely heavily on the precision of measured radar data. In order to take the quantitative backscattering measurement, external calibration should be carried out At precent, one of the common calibration methods is the point-target method. However,

苏南三部地基雷达反射率因子一致性和偏差订正个例研究

中国新一代天气雷达可以进行较大范围降水的定量估测,但相邻站雷达之间的资料可能存在不一致性而影响组网应用效果。将热带降雨测量卫星TRMM(Tropical Rainfall Measuring Mission)上携带的降水雷达PR(Precipitation Radar)作为统一的参照,针对2010年5-8月份长江中下游降水天气期间PR与苏南三部(南京、常州、南通)雷达的7次匹配事件的资料进行一致性分析,并利用经质量控制后得到的比较适宜于对比分析的数据集建立订正关系,进行偏差订正,分析订正效果,并详细比较其中两次事件订正前后地基雷达数据之间的差异。结果表明:(1)南京雷达反射率因子强度比常州雷达低3.5 d B左右,常州比南通低0.9 d B左右,3 km高度的回波强度拼图存在明显不连续;(2)将TRM M PR作为参照,利用预处理后的雷达观测数据样本,计算得出南京、常州、南通各地基雷达与PR的差值,并进行偏差订正后,南京与常州、常州与南通之间的反射率因子差值减小成为0.3和0.2,拼图效果也明显改善。

TRMM星载测雨雷达和地基雷达反射率因子数据的三维融合

TRMM卫星上的测雨雷达(TRMM PR)探测资料分布均匀且具有很高的垂直分辨率,但灵敏度较低;地基雷达(GR)水平分辨率较高且具有较高的灵敏度,但其垂直分辨率低。通过将TRMM PR与GR反射率因子数据的三维数据融合,得到了更优的反射率因子图像。测雨雷达与地基雷达三维数据融合主要分为以下几步:测雨雷达与地基雷达数据预处理——如去杂波、衰减校正;测雨雷达与地基雷达时空匹配;选取和应用合适的三维图像融合算法;对融合后的图像进行效果评估。试验结果表明:融合后的图像不仅增大了信息量,更好地检测弱降水,还提高了空间三维(3D)分辨率,能更好地反映降水区域细节,且使得数据总体上具有更高的完整性和可靠性。此外,还将基于雷达资料估测的降水数据与地面雨量计数据进行对比,估计反射率因子数据融合在降水测量上的有效性。

Partition of GB-InSAR deformation map based on dynamic time warping and k-means

Ground-based interferometric synthetic aperture radar(GB-InSAR)can take deformation measurement with a high accuracy.Partition of the GB-InSAR deformation map benefits analyzing the deformation state of the monitoring scene better.Existing partition methods rely on labelled datasets or single deformation feature,and they cannot be effectively utilized in GBInSAR applications.This paper proposes an improved partition method of the GB-InSAR deformation map based on dynamic time warping(DTW)and k-means.The DTW similarities between a reference point and all the measurement points are calculated based on their time-series deformations.Then the DTW similarity and cumulative deformation are taken as two partition features.With the k-means algorithm and the score based on multi evaluation indexes,a deformation map can be partitioned into an appropriate number of classes.Experimental datasets of West Copper Mine are processed to validate the effectiveness of the proposed method,whose measurement points are divided into seven classes with a score of 0.3151.

Consistency correction of echo intensity data for multiple radar systems and its application in quantitative estimation of typhoon precipitation

Calibration error is one of the primary sources of bias in echo intensity measurements by ground-based radar systems.Calibration errors cause data discontinuity between adjacent radars and reduce the effectiveness of the radar system.The Global Precipitation Measurement Kuband Precipitation Radar(GPM KuPR)has been shown to provide stable long-term observations.In this study,GPM KuPR observations were converted to S-band approximations,which were then matched spatially and temporally with ground-based radar observations.The measurements of stratiform precipitation below the melting layer collected by the KuPR during Typhoon Ampil were compared with those of multiple radar systems in the Yangtze River Delta to determine the deviations in the echo intensity between the KuPR and the ground-based radar systems.The echo intensity data collected by the ground-based radar systems was corrected using the KuPR observations as reference,and the correction results were verified by comparing them with rain gauge observations.It was found that after the correction,the consistency of the echo intensity measurements of the multiple radar systems improved significantly,and the precipitation estimates based on the revised ground-based radar observations were closer to the rain gauge measurements.