Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHz channels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scattering indices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) to develop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit between the rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfall using the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. The validation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro and Marks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithm is able to retrieve the rainfall with reasonably good accuracy.     

Improved rainfall estimation over the Indian region using satellite infrared technique

The GOES Precipitation Index (GPI) technique (Arkin, 1979) for rainfall estimation has been in operation for the last three decades. However, its applications are limited to the larger temporal and spatial scales. The present study focuses on the augmentation on GPI technique by incorporating a moisture factor for the environmental correction developed by Vicente et al. (1998). It consists of two steps; in the first step the GPI technique is applied to the Kalpana-IR data for rainfall estimation over the Indian land and oceanic region and in the second step an environmental moisture correction factor is applied to the GPI-based rainfall to estimate the final rainfall. Detailed validation with rain gauges and comparison with Tropical Rainfall Measuring Mission (TRMM) merged data product (3B42) are performed and it is found that the present technique is able to estimate the rainfall with better accuracy than the GPI technique over higher temporal and spatial domains for many operational applications in and around the Indian regions using Indian geostationary satellite data. Further comparison with the Doppler Weather Radar shows that the present technique is able to retrieve the rainfall with reasonably good accuracy.     

Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan

Multi-sensor precipitation datasets including two products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and estimates from Climate Prediction Center Morphing Technique (CMORPH) product were quantitatively evaluated to study the monsoon variability over Pakistan. Several statistical and graphical techniques are applied to illustrate the nonconformity of the three satellite products from the gauge observations. During the monsoon season (JAS), the three satellite precipitation products captures the intense precipitation well, all showing high correlation for high rain rates (>30 mm/day). The spatial and temporal satellite rainfall error variability shows a significant geo-topography dependent distribution, as all the three products overestimate over mountain ranges in the north and coastal region in the south parts of Indus basin. The TMPA-RT product tends to overestimate light rain rates (approximately 100%) and the bias is low for high rain rates (about ±20%). In general, daily comparisons from 2005 to 2010 show the best agreement between the TMPA-V7 research product and gauge observations with correlation coefficient values ranging from moderate (0.4) to high (0.8) over the spatial domain of Pakistan. The seasonal variation of rainfall frequency has large biases (100–140%) over high latitudes (36N) with complex terrain for daily, monsoon, and pre-monsoon comparisons. Relatively low uncertainties and errors (Bias ±25% and MAE 1–10 mm) were associated with the TMPA-RT product during the monsoon-dominated region (32–35N), thus demonstrating their potential use for developing an operational hydrological application of the satellite-based near real-time products in Pakistan for flood monitoring.     

Reliability of satellite-based precipitation products in capturing extreme precipitation indices over Iran

The goal of this study is to assess the performance of four widely-used satellite precipitation products in capturing extreme precipitation indices across Iran over the period 2001–2018; these products include GPM IMERG (Integrated Multi-Satellite Retrievals for Global Precipitation Measurement), TRMM 3B42 (Tropical Rainfall Measuring Mission), CHIRPS (Climate Hazards Center InfraRed Precipitation with Station data), and PERSIANN-CDR (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). For this aim, a national gridded precipitation dataset was developed using a dense network of rain gauges as a reference dataset. The results suggest that the IMERG product outperforms the other three precipitation products in capturing extreme precipitation indices both temporally and spatially. TRMM 3B42 data show promising results in identifying many extreme indices, while the CHIRPS and PERSIANN-CDR products show less performance in accurately generating many of the extreme precipitation indices.     

Development of a regional rain retrieval algorithm for exclusive mesoscale convective systems over peninsular India

The present study emphasize the development of a region specific rain retrieval algorithm by taking into accounts the cloud features. Brightness temperatures (T_bs) from various TRMM Microwave Imager (TMI) channels are calibrated with near surface rain intensity as observed from the TRMM – Precipitation Radar. It shows that T_b–R relations during exclusive-Mesoscale Convective System (MCS) events have greater dynamical range compared to combined events of non-MCS and MCS. Increased dynamical range of T_b–R relations for exclusive-MCS events have led to the development of an Artificial Neural Network (ANN) based regional algorithm for rain intensity estimation. By using the exclusive MCSs algorithm, reasonably good improvement in the accuracy of rain intensity estimation is observed. A case study of a comparison of rain intensity estimation by the exclusive-MCS regional algorithm and the global TRMM 2A12 rain product with a Doppler Weather Radar shows significant improvement in rain intensity estimation by the developed regional algorithm.     

基于数值预报模式的Ka频段卫星系统降雨衰减短时预测

为精确地实现Ka频段星地链路降雨衰减的短时趋势和强度预测,提出了一种基于数值天气预报的降雨衰减短时预测方法,设计了降雨衰减预测流程,并应用上述方法对某地区发生的降雨过程进行研究,比较分析了降雨衰减的实测值和基于数值预报的降雨衰减预测值。试验结果表明,基于数值预报的降雨衰减和实测值变化趋势符合较好,误差较小,在20GHz频段平均误差约为1.31dB,在30GHz频段平均误差约为2.39dB,在20GHz频段平均误差约为1.31dB,在30GHz频段平均误差约为2.39dB。该方法可实现Ka频段卫星通信系统的短时降雨趋势预测。     

Rainfall estimation over a Mediterranean region using a method based on various spectral parameters of SEVIRI-MSG

The ultimate objective of this paper is the estimation of rainfall over an area in Algeria using data from the SEVIRI radiometer (Spinning Enhanced Visible and Infrared Imager). To achieve this aim, we use a new Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The satellite rainfall retrieval technique is based on various spectral parameters of SEVIRI that express microphysical and optical cloud properties. It uses a multispectral thresholding technique to distinguish between stratiform and convective clouds. This technique (CS-RADT) is applied to the complex situation of the Mediterranean climate of this region. The tests have been conducted during the rainy seasons of 2006/2007 and 2010/2011 where stratiform and convective precipitation is recorded. The developed scheme (CS-RADT) is calibrated by instantaneous meteorological radar data to determine thresholds, and then rain rates are assigned to each cloud type by using radar and rain gauge data. These calibration data are collocated with SEVIRI data in time and space.     

Impact of four dimensional assimilation of satellite data on long-range simulations over the Indian region during monsoon 2006

A number of experiments were conducted to study the impact of updating model basic fields by satellite data (Quick Scatterometer (QSCAT) surface winds and Atmospheric Infrared Sounder (AIRS) temperature and humidity profiles) on long-range simulation during the Indian summer monsoon 2006. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model version5 (MM5) and its four dimensional data assimilation (FDDA) technique was used for the numerical simulations. The spatial distribution and temporal variation in model simulated basic meteorological parameters and rainfall were verified against the observed fields from National Center for Environmental Prediction (NCEP) analysis and Tropical Rainfall Measuring Mission (TRMM), respectively. The overall analysis of the results from QSCAT surface wind assimilation as compared to control simulation (CNT; without the satellite data assimilation) suggest that a better representation of a single level wind field during model integration fail to make significant improvement in the model simulation both in the basic meteorological parameters and rainfall. The assimilation of temperature and humidity profiles from the AIRS during model integration significantly improved the rainfall prediction during monsoon period. It is found that the improvement in rainfall prediction is attributed to improved thermodynamics structure due to AIRS profile assimilation and the degree of improvement is more in temperature prediction as compared to humidity prediction. It is also found that the prediction over the regions, such as south west part of India and foothills of Himalaya, where a complex orography exists, is not significantly benefited from satellite data assimilation which highlights the need of improvement in the model in addition to a better representation of atmospheric state.     

Analysis of rain attenuation prediction models at Ku-band in Thailand

The performance of the existing rain attenuation models in tropical zones is still a debated issue due to the lack of measurements reported from these areas of the world to develop and validate prediction models. A three-year (2003–2005) campaign of rainfall rate and rain attenuation measurements was conducted on a satellite beacon link located in a tropical region of Thailand. The cumulative distributions of rain attenuation derived from the measured data are presented and compared with those obtained with existing prediction models.     

基于资源一号02B卫星红外地球敏感器和星敏感器的自主导航

 本文阐述了利用星敏感器和红外地球敏感器进行自主导航的原理。以资源一号02B卫星的飞行数据为基础,进行了自主导航的研究,结果表明采用国产红外地球敏感器和星敏感器的资源一号02B卫星可以实现较高精度的自主导航,说明了该方案的可行性。     

A multi-spectral spatial convolution approach of rainfall forecasting using weather satellite imagery

Flood forecasting has long been a major topic of hydrologic research. Recent events and studies indicate that the success of flood forecasting in Taiwan depends heavily on the accuracy of real-time rainfall forecasting. In this study, we demonstrate a multi-spectral spatial convolution approach for real-time rainfall forecasting using geostationary weather satellite images. The approach incorporates cloud-top temperatures of three infrared channels in a spatial convolution context. It not only characterizes the input–output relationship between cloud-top temperature and rainfall at the ground level, but also is more consistent with physical and remote sensing principles than single-pixel matches. Point rainfall measurements at raingauge sites are up-scaled to pixel-average-rainfall by block kriging, then related to multi-spectral cloud-top temperatures derived from Geostationary Meteorological Satellite images by spatial convolution. The kernel function of the multispectral spatial convolution equation is solved by the least squares method. Through a cross-validation procedure, we demonstrate that the proposed approach is capable of achieving high accuracy for 1- to 3-h-lead pixel-average-rainfall forecasting.     

基于移动站的转发式地面站设备时延标校方法

在转发式卫星测定轨系统中,基于伪码测距原理的星地距离测量是实现卫星精密定轨和高精度时间比对的基础。为获得高精度的星地距离,需要将地面站设备时延从伪码测距值中精确扣除。在转发式卫星测轨原理的基础上,提出了基于移动站的转发式地面站设备时延标校方法,实现了对转发式地面站设备时延的标校,标校精度能够优于0.5ns,对提高转发式卫星定轨精度和卫星双向时间比对精度具有重要作用。     

Investigation of vertical profile of rain microstructure at Ahmedabad in Indian tropical region

The microstructure of rain has been studied with observations using a vertical looking Micro Rain Radar (MRR) at Ahmedabad (23.06°N, 72.62°E), a tropical location in the Indian region. The rain height, derived from the bright band signature of melting layer of radar reflectivity profile, is found to be variable between the heights 4600 m and 5200 m. The change in the nature of rain, classified on the basis of radar reflectivity, is also observed through the MRR. It has been found that there are three types of rain, namely, convective, mixed and stratiform rain, prevailing with different vertical rain microstructures, such as, Drop Size Distribution (DSD), mean drop size, rain rate, liquid water content and average fall speed of the drops at different heights. It is observed that the vertical DSD profile is more inhomogeneous for mixed and stratiform type rain than for convective type rain. It is also found that the large number of drops of size <0.5 mm is present in convective rain whereas in stratiform rain, drops concentration is appreciable up to 1 mm. A comparison of measurements taken by ground based Disdrometer and that from the 200 m level obtained from MRR shows good agreement for rain rate and DSD at smaller rain rate values. The results may be useful for understanding rain structures over this region.     

A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.     

A comparison of atmospheric aerosol measurements by various satellite sensors

Investigations to measure the vertical optical thickness of aerosols over ocean surfaces has been conducted using several different satellite sensors. Landsat 1 and Landsat 2 data originally confirmed that a linear relationship exists between the upwelling visible radiance and the aerosol optical thickness (about 90% of this thickness is generally in the lowest 3 km of the atmosphere). Similar relationships have also been found for sensors on GOES-1, SMS-2, NOAA-5, and NOAA-6 satellites. The linear relationship has been shown theoretically to vary with the aerosol properties, such as size distribution and refractive index, although the Landsat data obtained at San Diego showed little variability in the relationship. The differences between the results found for the various satellite sensors are discussed, and are attributed mainly to uncertainties in the calibration of the sensors. To investigate the general applicability of the technique to different locations, a global-scale ground truth experiment was conducted with the AVHRR sensor on NOAA-6 to determine the relationship at eleven ocean sites around the globe. Analysis of the data shows good agreement between the satellite and ground truth values of the aerosol optical thickness, and indicates that the technique has global application. At two of the sites, multispectral radiometric measurements of the Junge aerosol size distribution parameter were made, and showed good agreement with a value inferred from the AVHRR Channels 1 and 2 radiances.     

量子科学实验卫星微振动地面测试与估计方法

介绍了卫星微振动地面测试和信号估计方法.根据量子科学实验卫星特点,对动量轮运行状态下光学载荷的微振动线位移及角位移进行估计.通过地面微振动测试系统,测量了动量轮运行下光学载荷的微振动加速度;应用小波积分方法,精确估计了卫星结构敏感点的振动线位移和振动角位移.通过工程实测证明了微振动测试与估计方法的有效性.研究结果对于高精度科学实验卫星微振动地面试验具有一定工程应用意义.      

Application of SAR data to monitoring earth surface changes and displacement

Surface changes and displacement caused by earthquake and volcanic activities can be detected by satellite radar interferometric technology using L-band JERS-1 SAR images. For the detection of the coseismic deformation of the Hyogoken-nanbu earthquake, interferometric analysis shows 0.6 meter surface displacement along the radar line-of-sight direction around Kobe City and 1.1 meter displacement in Awaji Island where epicenter is located. For the detection of volcanic body deformation in Iwo-jima, the analysis shows concentric circular subsiding displacement that agrees with the results of ground measurement.     

用于卫星入轨段测控的箭载天基测控中继系统

为满足卫星入轨段关键遥测参数下传和遥控指令上传的需求,设计了一种用于卫星入轨段测控的中继系统,方案主要基于火箭现成天基测控终端和卫星现成测控设备。介绍了中继系统的组成、工作原理和工作流程,研究了天基测控相控阵天线波束指向算法,设计了一种卫星遥测、遥控信号中继功能的地面测试系统。该箭载中继系统在快舟一号甲火箭上完成了两次飞行试验验证,两次飞行试验中继转发的卫星遥测数据完整,箭载卫星通信终端接收用户卫星遥测数据的载噪比大于20dB;地面测控中心接收天基遥测返向信号比特信噪比相对接收门限有3dB以上的余量。试验表明,该箭载天基测控中继系统通信链路余量充足,工作可靠,相比通过地面测控资源保障或卫星自身使用天基测控可节省一半以上成本。     

星地无线电双向法时间比对计算模型及其误差评估

卫星导航定位系统测距的基础是测时,而定轨和定位的前提是各观测量的时间同步。因此,时间同步技术是卫星导航定位系统建设的关键技术基础之一。根据星地无线电双向法时间同步技术的基本原理,详细推导了其在地心惯性系中的基本计算模型,并以静止地球同步轨道卫星为例,分析了该计算模型中的距离改正项时延对星地间相对钟差的影响量级。     

在轨地球同步卫星自主工程测控研究

地球同步卫星在轨工程测控任务通常由地面测控系统完成. 随着技术水平的发展, 如果在轨地球同步卫星能够实现自主工程测控, 将大大减轻地面测控系统负担, 提高卫星独自生存能力, 降低系统运行成本, 并将成为卫星测控技术新的发展方向. 本文提出卫星自主在轨测控方案, 研究了当前地球同步卫星在轨工程测控的主要项目及实现算法. 对其进行的可行性分析表明, 尽管受到轨道测量能力的制约, 在轨地球同步卫星仍可实现完全或地面有限参与情况下的自主工程测控. 在此基础上, 设计了一种地球同步在轨卫星完全自主工程测控的原理方案, 通过分析其技术难点及存在的风险, 提出应对措施. 研究结果表明, 基于目前卫星制造水平和成熟的在轨测控技术, 实现在轨地球同步卫星自主工程测控技术可行, 其是解决卫星数量急剧增加与地面测控能力有限这一突出矛盾的有效途径.