Radio-frequency interference （RFI） affects greatly the quality of the data and retrieval products from space-borne microwave radiometry. Analysis of the Advanced Microwave Scanning Radiometer on the Earth Observing ...Radio-frequency interference （RFI） affects greatly the quality of the data and retrieval products from space-borne microwave radiometry. Analysis of the Advanced Microwave Scanning Radiometer on the Earth Observing System （AMSR-E） Aqua satellite observations reveals very strong and widespread RFI contam- inations on the C- and X-band data. Fortunately, the strong and moderate RFI signals can be easily identified using an index on observed brightness temperature spectrum. It is the weak RFI that is diffi- cult to be separated from the nature surface emission. In this study, a new algorithm is proposed for RFI detection and correction. The simulated brightness temperature is used as a background signal （B） and a departure of the observation from the background （O–B） is utilized for detection of RFI. It is found that the O–B departure can result from either a natural event （e.g., precipitation or flooding） or an RFI signal. A separation between the nature event and RFI is further realized based on the scattering index （SI）. A positive SI index and low brightness temperatures at high frequencies indicate precipitation. In the RFI correction, a relationship between AMSR-E measurements at 10.65 GHz and those at 18.7 or 6.925 GHz is first developed using the AMSR-E training data sets under RFI-free conditions. Contamination of AMSR-E measurements at 10.65 GHz is then predicted from the RFI-free measurements at 18.7 or 6.925 GHz using this relationship. It is shown that AMSR-E measurements with the RFI-correction algorithm have better agreement with simulations in a variety of surface conditions.展开更多
Land retrievals using passive microwave radiometers are sensitive to small fluctuations in land brightness temperatures. As such, the radio-frequency interference （RFI） signals emanating from man-made microwave radi...Land retrievals using passive microwave radiometers are sensitive to small fluctuations in land brightness temperatures. As such, the radio-frequency interference （RFI） signals emanating from man-made microwave radiation transmitters can result in large errors in land retrievals. RFI in C-and X-band channels can con-taminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer （AMSR-E） and the WindSat sensor. In this work, applications of an RFI detection and correction algorithm in retrieving a comprehensive suite of geophysical parameters from AMSR-E measurements using the one-dimensional variational retrieval （1-DVAR） method are described. The results indicate that the values of retrieved parameters, such as land skin temperature （LST）, over these areas contaminated by RFI are much higher than those from the global data assimilation system （GDAS） products. The results also indicate that the differences between new retrievals and GDAS products are decreased evidently through taking into account the RFI correction algorithm. In addition, the convergence metric （χ2） of 1-DVAR is found to be a new method for identifying regions where land retrievals are affected by RFI. For example, in those regions with much stronger RFI, such as Europe and Japan, χ2 of 1-DVAR is so large that convergence cannot be reached and retrieval results may not be reliable or cannot be obtained. Furthermore,χ2 also decreases with the RFI-corrected algorithm for those regions with moderate or weak RFI. The results of RFI detected byχ2 are almost consistent with those identified by the spectral difference method.展开更多
随着无线电技术的发展,射频干扰(radio frequency interference,RFI)对射电天文观测的影响越来越大,尤其是周期性RFI对天文观测的影响越来越显著。本文围绕宽带、高时间分辨率频谱数据,采用阈值计算、噪声通道过滤、快速傅里叶变换、周...随着无线电技术的发展,射频干扰(radio frequency interference,RFI)对射电天文观测的影响越来越大,尤其是周期性RFI对天文观测的影响越来越显著。本文围绕宽带、高时间分辨率频谱数据,采用阈值计算、噪声通道过滤、快速傅里叶变换、周期计算、通道合并、来源分析和模板库建立等步骤,提出一种面向宽带频谱序列的周期RFI统计方法。将该方法应用于新疆天文台南山观测站26 m射电望远镜(Nan Shan 26 m Radio Telescope,NSRT)脉冲星观测终端数据,有效地检测并提取出了频谱序列中的周期性RFI,可为进一步电磁干扰缓解提供数据支撑。展开更多
较可见光和红外遥感而言,微波遥感不易受大气影响,具有全天时、全天候的监测能力以及对云、雨、大气较强的穿透能力,并且微波传感器对于植被特性的变化、地表土壤水分和积雪参数十分敏感,微波数据已被广泛应用于地表参数的监测和反演应...较可见光和红外遥感而言,微波遥感不易受大气影响,具有全天时、全天候的监测能力以及对云、雨、大气较强的穿透能力,并且微波传感器对于植被特性的变化、地表土壤水分和积雪参数十分敏感,微波数据已被广泛应用于地表参数的监测和反演应用之中.然而,用于反演地表参数的低频微波观测资料均不同程度地受到地面无线电频率的干扰(Radio Frequency Interference,RFI).这些干扰往往是由地面主动微波传感器的发射信号或陆面反射辐射信号产生的,很容易覆盖地表产生的相对较弱的自然热发射辐射信号,使得星载被动微波传感器接收的信息不能真实地反映地表状况.如果不能准确地将其识别和剔除,往往导致较大的反演误差,降低遥感数据反演产品的质量,从而显著降低现有以及将来的被动微波资料的利用率.本文从目前常用的干扰识别方法,包括谱差法、平均值和标准差法、多通道回归法、主分量分析法和一维变分反演收敛度量识别法等等,回顾了识别星载微波辐射计数据中RFI信号的研究进程及其研究中存在的问题,并对这些方法的优、缺点分别进行了评价,阐述了存在的问题.最后对星载微波资料RFI识别的研究做出展望,指出今后应进一步完善RFI信号的识别方法,开发RFI信号的订正算法,将其应用到卫星遥感数据的产品反演与同化过程中,并获取可靠的陆面、洋面RFI源分布和分类信息,更好地评估多种识别方法的可靠性、准确性和适用性.展开更多
Radio Frequency Interference （RFI） causes severe contamination to passive and active microwave sensing observations and corresponding retrieval products. RFI signals should be detected and filtered before applying t...Radio Frequency Interference （RFI） causes severe contamination to passive and active microwave sensing observations and corresponding retrieval products. RFI signals should be detected and filtered before applying the microwave data to retrieval and data assimilation. It is difficult to detect RFI over land surfaces covered by snow because of the scattering effect of snow surface. The double principal component analysis （DPCA） method is adopted in this study, and its ability in identifying RFI signals in AMSR-E data over snow covered regions is investigated. Results show that the DPCA method can detect RFI signals effectively in spite of the impact of snow scattering, and the detected RFI signals persistent over time. Compared to other methods, such as PCA and normalized PCA, DPCA is more robust and suitable for operational application.展开更多
从海量的天文观测数据中快速搜寻罕见的快速射电暴(Fast Radio Burst,FRB)事件,干扰缓解是其中一项关键而具有挑战的工作.射频干扰(Radio Frequency Interference,RFI)会淹没真实的天文事件,还会导致搜寻管线输出大量的假阳性候选体.由...从海量的天文观测数据中快速搜寻罕见的快速射电暴(Fast Radio Burst,FRB)事件,干扰缓解是其中一项关键而具有挑战的工作.射频干扰(Radio Frequency Interference,RFI)会淹没真实的天文事件,还会导致搜寻管线输出大量的假阳性候选体.由于干扰来源及其种类的复杂性,目前并没有一种通用的方法可以解决这个问题.为了降低干扰对FRB观测搜寻的影响,分析和研究了南山26m射电望远镜L波段观测数据中的干扰情况,针对主要的窄带干扰和宽带干扰建立了3层次的干扰缓解处理流程,从而有效缓解了观测数据的干扰污染情况.将该流程嵌入到FRB色散动态谱搜寻(Dispersed Dynamic Spectra Search,DDSS)管线中,实验结果表明,搜寻管线的检测率和检测精度得到了进一步的提高.该方法为FRB观测数据干扰缓解处理提供了有价值的参考.展开更多
基金Supported by the National Key Basic Research and Development (973) Program of China(2010CB951600)National Natural Science Foundation of China(40875015,40875016,and40975019)+2 种基金Special Fund for University Doctoral Students of China(20060300002)Chinese Academy of Meteorological Sciences"Application of Meteorological Data in GRAPES-3DVar" ProgramNOAA/NESDIS/Center for Satellite Applications and Research (STAR) CalVal Program
文摘Radio-frequency interference （RFI） affects greatly the quality of the data and retrieval products from space-borne microwave radiometry. Analysis of the Advanced Microwave Scanning Radiometer on the Earth Observing System （AMSR-E） Aqua satellite observations reveals very strong and widespread RFI contam- inations on the C- and X-band data. Fortunately, the strong and moderate RFI signals can be easily identified using an index on observed brightness temperature spectrum. It is the weak RFI that is diffi- cult to be separated from the nature surface emission. In this study, a new algorithm is proposed for RFI detection and correction. The simulated brightness temperature is used as a background signal （B） and a departure of the observation from the background （O–B） is utilized for detection of RFI. It is found that the O–B departure can result from either a natural event （e.g., precipitation or flooding） or an RFI signal. A separation between the nature event and RFI is further realized based on the scattering index （SI）. A positive SI index and low brightness temperatures at high frequencies indicate precipitation. In the RFI correction, a relationship between AMSR-E measurements at 10.65 GHz and those at 18.7 or 6.925 GHz is first developed using the AMSR-E training data sets under RFI-free conditions. Contamination of AMSR-E measurements at 10.65 GHz is then predicted from the RFI-free measurements at 18.7 or 6.925 GHz using this relationship. It is shown that AMSR-E measurements with the RFI-correction algorithm have better agreement with simulations in a variety of surface conditions.
基金Supported by the National Natural Science Foundation of China(41305033,41275043,and 41175035)Priority Academic Program Development(PAPD)of Jiangsu Higher Education InstitutionNOAA/NESDIS/Center for Satellite Applications and Research(STAR)CalVal Program
文摘Land retrievals using passive microwave radiometers are sensitive to small fluctuations in land brightness temperatures. As such, the radio-frequency interference （RFI） signals emanating from man-made microwave radiation transmitters can result in large errors in land retrievals. RFI in C-and X-band channels can con-taminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer （AMSR-E） and the WindSat sensor. In this work, applications of an RFI detection and correction algorithm in retrieving a comprehensive suite of geophysical parameters from AMSR-E measurements using the one-dimensional variational retrieval （1-DVAR） method are described. The results indicate that the values of retrieved parameters, such as land skin temperature （LST）, over these areas contaminated by RFI are much higher than those from the global data assimilation system （GDAS） products. The results also indicate that the differences between new retrievals and GDAS products are decreased evidently through taking into account the RFI correction algorithm. In addition, the convergence metric （χ2） of 1-DVAR is found to be a new method for identifying regions where land retrievals are affected by RFI. For example, in those regions with much stronger RFI, such as Europe and Japan, χ2 of 1-DVAR is so large that convergence cannot be reached and retrieval results may not be reliable or cannot be obtained. Furthermore,χ2 also decreases with the RFI-corrected algorithm for those regions with moderate or weak RFI. The results of RFI detected byχ2 are almost consistent with those identified by the spectral difference method.
文摘随着无线电技术的发展,射频干扰(radio frequency interference,RFI)对射电天文观测的影响越来越大,尤其是周期性RFI对天文观测的影响越来越显著。本文围绕宽带、高时间分辨率频谱数据,采用阈值计算、噪声通道过滤、快速傅里叶变换、周期计算、通道合并、来源分析和模板库建立等步骤,提出一种面向宽带频谱序列的周期RFI统计方法。将该方法应用于新疆天文台南山观测站26 m射电望远镜(Nan Shan 26 m Radio Telescope,NSRT)脉冲星观测终端数据,有效地检测并提取出了频谱序列中的周期性RFI,可为进一步电磁干扰缓解提供数据支撑。
文摘较可见光和红外遥感而言,微波遥感不易受大气影响,具有全天时、全天候的监测能力以及对云、雨、大气较强的穿透能力,并且微波传感器对于植被特性的变化、地表土壤水分和积雪参数十分敏感,微波数据已被广泛应用于地表参数的监测和反演应用之中.然而,用于反演地表参数的低频微波观测资料均不同程度地受到地面无线电频率的干扰(Radio Frequency Interference,RFI).这些干扰往往是由地面主动微波传感器的发射信号或陆面反射辐射信号产生的,很容易覆盖地表产生的相对较弱的自然热发射辐射信号,使得星载被动微波传感器接收的信息不能真实地反映地表状况.如果不能准确地将其识别和剔除,往往导致较大的反演误差,降低遥感数据反演产品的质量,从而显著降低现有以及将来的被动微波资料的利用率.本文从目前常用的干扰识别方法,包括谱差法、平均值和标准差法、多通道回归法、主分量分析法和一维变分反演收敛度量识别法等等,回顾了识别星载微波辐射计数据中RFI信号的研究进程及其研究中存在的问题,并对这些方法的优、缺点分别进行了评价,阐述了存在的问题.最后对星载微波资料RFI识别的研究做出展望,指出今后应进一步完善RFI信号的识别方法,开发RFI信号的订正算法,将其应用到卫星遥感数据的产品反演与同化过程中,并获取可靠的陆面、洋面RFI源分布和分类信息,更好地评估多种识别方法的可靠性、准确性和适用性.
文摘Radio Frequency Interference （RFI） causes severe contamination to passive and active microwave sensing observations and corresponding retrieval products. RFI signals should be detected and filtered before applying the microwave data to retrieval and data assimilation. It is difficult to detect RFI over land surfaces covered by snow because of the scattering effect of snow surface. The double principal component analysis （DPCA） method is adopted in this study, and its ability in identifying RFI signals in AMSR-E data over snow covered regions is investigated. Results show that the DPCA method can detect RFI signals effectively in spite of the impact of snow scattering, and the detected RFI signals persistent over time. Compared to other methods, such as PCA and normalized PCA, DPCA is more robust and suitable for operational application.
文摘从海量的天文观测数据中快速搜寻罕见的快速射电暴(Fast Radio Burst,FRB)事件,干扰缓解是其中一项关键而具有挑战的工作.射频干扰(Radio Frequency Interference,RFI)会淹没真实的天文事件,还会导致搜寻管线输出大量的假阳性候选体.由于干扰来源及其种类的复杂性,目前并没有一种通用的方法可以解决这个问题.为了降低干扰对FRB观测搜寻的影响,分析和研究了南山26m射电望远镜L波段观测数据中的干扰情况,针对主要的窄带干扰和宽带干扰建立了3层次的干扰缓解处理流程,从而有效缓解了观测数据的干扰污染情况.将该流程嵌入到FRB色散动态谱搜寻(Dispersed Dynamic Spectra Search,DDSS)管线中,实验结果表明,搜寻管线的检测率和检测精度得到了进一步的提高.该方法为FRB观测数据干扰缓解处理提供了有价值的参考.
