Cloud attenuation statistics from radiometric measurements over a tropical location Kolkata,India

The objective of this study is to investigate cloud attenuation at 30 GHz frequency using ground-based microwave radiometric observations at a tropical location, Kolkata. At higher frequencies and lower elevation angles, cloud attenuation is of major concern at a tropical location. The location experiences high value of liquid water path (LWP), which is responsible for cloud attenuation, during the Indian summer monsoon (ISM) and pre-monsoon season. Significant amount of cloud attenuation has been observed during monsoon season at 30 GHz. Two years observations of exceedance probability of cloud attenuation and worst month statistics are presented. The variation of cloud attenuation with frequencies for different elevation angles has also been investigated. The seasonal and diurnal patterns of cloud attenuation are examined. Cloud attenuation, inferred from radiometric measurements before rain commencement, has been compared to rain attenuation at Ku-band. Exceedance probabilities of cloud and rain attenuation have been compared.     

Attenuation prediction for fade mitigation using neural network with in situ learning algorithm

Increasing demand of bandwidth in communication satellites has forced satellite links to be designed in Ku bands and above. But at these frequencies, rain and other tropospheric elements result in large attenuation. To mitigate the tropospheric attenuation of microwave satellite signals above 10 GHz using any standard Fade Mitigation Technique (FMT), it is essential to have a priori knowledge about the level of attenuation. Hence, short-term rain attenuation prediction models play a key role in maintaining the link in which necessary compensation can be applied depending on the early information of attenuation. This paper presents a method of attenuation prediction using Adaptive Artificial Neural Network. Here In situ Learning Algorithm (ILA) has been used to enable the system to track the non-stationary nature of the attenuation. To validate this, Ku Band data, collected at three different sites in India have been used for the purpose of prediction. The performance of the algorithm is determined through the estimation of prediction accuracy by comparing the predicted values with the measured data. Results obtained using the mentioned technique shows considerably good accuracy even up to 20 s of prediction interval with acceptable ratio between the under and over predictions. The prediction performance is evaluated for different prediction intervals. Furthermore the present model is also compared with the persistence model and the relative performance is quantified.     

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.     

Ku波段卫星通信雨衰预测

介绍３种计算Ｋｕ波段雨衰的数学模型,并且讨论其雨衰特性。根据３种数学模型预测各种情况下的雨衰值,并同实测数据进行比较。最后讨论我国雨区分布情况及Ｋｕ波段雨衰裕度。     

0 °C isotherm height for satellite communication in Malaysia

The 0 °C isotherm height is an important parameter for prediction of rain attenuation of microwave and millimeter wave for Earth-space communication. The variations of 0 °C isotherm heights for two monsoon seasons have been studied based on an analysis of radiosonde over three stations. The exceedence probability statistics of rain height are compared between the two seasons. The results on the 0 °C isotherm height can be utilized for the estimation of attenuation of microwave and millimeter wave due to rain over Earth-space paths. Attenuations of radio wave due to rain at frequencies above 10 GHz and above have also been estimated using the 0 °C isotherm height so derived.     

Performance evaluation of some rain rate conversion models for microwave propagation studies

An important characteristic of rainfall levels at a particular place is the statistical distribution of rainfall rate. In this paper, 5-min integration time rainfall data for the Northcentral region of Nigeria was obtained from the Tropospheric Data Acquisition Network (TRODAN), Anyigba, Nigeria. Also, 1-min integration time rainfall was measured at Minna, Nigeria. In order to obtain the optimal rain rate model suitable for this region, two globally recognised rain rate models were critically evaluated and compared with the 1-min measurements. These are the ITU-R P.837-7 and Lavergnat-Gole (L-G) models. The results obtained showed that the ITU-R P.837-7 and L-G models respectively underestimated the measured rain rate by 7.3 mm/h and 9 mm/h at time percentage exceedance of 0.1%, while they underestimated the measured rain rate by 23.4 mm/h and 13 mm/h respectively at 0.01%. At 0.001%, the measured rain rate was overestimated by the ITU-R P.837-7 and L-G models by 27.4 mm/h and 3 mm/h respectively. Further performance evaluation of the predefined models was carried out using different error metrics such as sum of absolute error (SAE), mean absolute error (MAE), root mean square error (RMSE), standard deviation (STDEV) and Spearman’s rank correlation. The results obtained adjudged the Lavergnat-Gole model as the best rain rate prediction model for this region.     

Second order experimental statistics of rain attenuation at Ka band in a tropical location

Some second order rain attenuation statistics such as fade duration and fade slope are investigated on the basis of experimental measurements of received signals using the GSAT-14 satellite beacon signal at 20.2 GHz for three years (2014–2016) over the tropical location Ahmedabad (23.02 ⁰E, 72.51⁰N), India with an Elevation angle of 63⁰. Existing models of fade duration are compared with experimental data in this study and exponent of power law model of fade duration at Ka band is further explored. A new model for fade duration for Ka band for tropical locations is proposed where the constant of exponent of attenuation in the power law is found to be 0.143 instead of 0.055 used in ITU-R. Other relevant parameters for implementation of fade mitigation technique to prevent the link outage like cumulative distribution of signal fade rate, maximum and minimum fade rise and fade fall are also studied. Fade slope asymmetry over tropical region is also investigated. Keeping in view of exploiting the commercial launch of Ka band in Indian region there is an urgent need for validation of the existing models of fade slope (specially looking into fade symmetry) and fade duration. It will help the SATCOM (Satellite Communication) link designer to improve closed loop fade mitigation technique to minimize the possible link failure/link outage over the tropical region.     

星—地路径雨致衰减特性及其对卫星系统设计的可能影响

本文综述了影响星—地路径雨致衰减的主要因素。并利用在北京获得的实测数据,对北京地区星—地雨致衰减的特点进行了分析。此外,还对卫星定点位置的选择和北京上行站选址提出了建议。     

Impact of rain on wave retrieval from Sentinel-1 synthetic aperture radar images in tropical cyclones

The main objective of our work was to investigate the impact of rain on wave observations from C-band (~5.3 GHz) synthetic aperture radar (SAR) in tropical cyclones. In this study, 10 Sentinel-1 SAR images were available from the Satellite Hurricane Observation Campaign, which were taken under cyclonic conditions during the 2016 hurricane season. The third-generation wave model, known as Simulating WAves Nearshore (SWAN) (version 41.31), was used to simulate the wave fields corresponding to these Sentinel-1 SAR images. In addition, rainfall data from the Tropical Rainfall Measuring Mission satellite passing over the spatial coverage of the Sentinel-1 SAR images were collected. The simulated results were validated against significant wave heights (SWHs) from the Jason-2 altimeter and European Centre for Medium-Range Weather Forecasts data, revealing a root mean square error (RMSE) of ~0.5 m with a 0.25 scatter index. Winds retrieved from the VH-polarized Sentinel-1 SAR images using the Sentinel-1 Extra Wide-swath Mode Wind Speed Retrieval Model after Noise Removal were taken as prior information for wave retrieval. It was discovered that rain did indeed affect the SAR wave retrieval, as evidenced by the 3.21-m RMSE of SWHs between the SAR images and the SWAN model, which was obtained for the ~1000 match-ups with raindrops. The raindrops dampened the wave retrieval when the rain rate was < ~5 mm/hr; however, they enhanced wave retrieval for higher rain rates. It was also found that the portion of the rain-induced ring wave with a wave number > 0.05 rad/m (~125 m wavelength) was clearly observed in the SAR-derived wave spectra.     

An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to Z–R approach.     

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.     

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.     

对卫星网络轨道／频率协调中若干问题的探讨

以国际电联的相关建议为基础,就卫星网络轨道及频率协调中所碰到的若干问题,诸如天线增益,极化隔离,雨衰及ＳＣＰＣ保护比,给出了合理及可实现的估算方法,同时强调了功率密度在不同的应用场合,常有不同的定义方式。     

An empirical model for the variation of rain drop size distribution with rain rate at a few locations in southern India

A Joss–Waldvogel impact type disdrometer was installed at four different locations in the Indian peninsula during various periods from 2001 till date. The data are analysed to study the nature of rain drop size distribution (DSD) in this region. Out of the three well known distributions that describe DSD, namely, the Marshall–Palmer, Gamma and Lognormal, it has been found that Lognormal distribution fits the DSD in this region better than the other ones. Lognormal distributions for different rain rates were then derived by fitting the lognormal function to the data using a curve fitting software. Then the variation of fit parameters with rain rate was evaluated. Incorporating these variations, into the Lognormal distribution, an empirical equation that describes the DSD in this region for different rain rates was derived. Then this equation was tested with sample data from each of these stations. The data used for validation were not used for fitting lognormal equation to derive the fit parameters. The correlation between the DSD measured and derived using the empirical model was found to be quite good (0.9) except in some cases where the coefficient dropped to 0.75. The empirical model can be updated when more data are available.     

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。     

Variation of rain drop size distribution with rain rate at a few coastal and high altitude stations in southern peninsular India

Rain drop size distribution (DSD) was measured at four places in Southern India {Thiruvananthapuram, Kochi, Munnar and Sriharikota (SHAR)} using a Joss–Waldvogel (JW) impact type disdrometer. The data for each minute were corrected for dead time errors and rain rate was computed from the corrected data. The data for a whole month were then sorted according to rain rate (R) into several classes ranging from 0.1 to >100 mm/h. The average DSD in each class was computed, and the lognormal distribution function was fitted to the average. In all the cases, the function fitted the data very well. The fit parameters were found to have dependence on rain rate. The total number of drops (N_T), the geometric mean diameter (D_g) and the standard geometric deviation (σ) were also computed from the fit parameters. The standard geometric deviation (σ) was found to be more or less constant with rain rate at all the sites and in all months. The other two parameters (N_T and D_g) were found to vary exponentially with rain rate except in Munnar, a high altitude station. At Thiruvananthapuram, in most of the months, N_T increased exponentially with rain rate up to some value of R, which was different in different months, and then remained more or less constant or decrease slightly. In all cases, the variation of N_T and D_g was such that N_TD_g³ increased linearly with rain rate.     

Using S-band dual polarized radar for convective/stratiform rain indexing and the correspondence with AMSR-E GSFC profiling algorithm

The separation of rain types in convective and stratiform regimes has long been a goal in microwave remote sensing of precipitation research. In this essence, a dual polarized radar based indexing scheme that provides information on convective and stratiform (C/S) rain regimes has been presented in correspondence with advanced microwave scanning radiometer – earth observing system (AMSR-E) GSFC profiling algorithm estimate of convective rain percentage. The dual polarized radar based C/S indexing scheme first retrieves the normalized gamma drop size distribution parameters, median volume drop diameter (D₀) and concentration parameter (N_w), from dual polarized radar measurements Z_H and Z_DR, representing reflectivity and differential reflectivity respectively, by means of the genetic programming approach. Next, the C/S rain index is calculated based on the formulation of an empirical relation in N_w–D₀ domain. The scheme has been inspected and applied on measurements from the S-band Chilbolton dual polarized radar. A considerable number of “coincident” cases from the radar and the AMSR-E observations are investigated. It has been revealed that the dual polarized radar based C/S rain indexing is in a similar pattern with the AMSR-E GSFC profiling algorithm estimate of convective rain percentage. Generally, as C/S rain index value increases, which signifies a stratiform to convective trend, the AMSR-E convective rain percentage also increases.     

Development and performance evaluation of tropospheric scintillation model on Ku-band satellite link over Akure,Nigeria

Existing amplitude scintillation prediction models often perform less satisfactorily when deployed outside the regions where they were formulated. This necessitates the need to evaluate the performance of scintillation models developed in one region using data data from other regions while documenting their relative errors. Due to its variation with elevation angle, frequency, other link parameters and meteorological factors, we employed three years (January 2016 to December 2018) of concurrently measured satellite radio beacons and tropospheric weather parameters to develop a location-based amplitude scintillation prediction model over the Earth-space path of Akure (7.17^oN, 5.18^oE), South-western Nigeria. The satellite beacon measurement used Tektronix Y400 NetTek Analyzer at 1 s integration time while meteorological parameters, namely; temperature, pressure and relative humidity were measured using Davis Vantage Vue weather station at 1 min integration time. Comparative study of the model’s performance with nine (9) existing scintillation prediction models indicates that the best and worst performing models, in terms of root mean square error (RMSE), are the Statistical Temperature and Refractivity (STN) and direct physical and statistical prediction (DPSP) models with values 11.48 and 51.03 respectively. Also, worst month analysis indicates that April, with respective enhancement and fade values of 0.88 and 0.90 dB for 0.01% exceedance, is the overall worst calendar month for amplitude scintillation.     

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.     

基于雨衰环境下的多波束卫星资源分配策略

针对雨衰对高频段的卫星通信造成的不利影响,采用自适应前向纠错编码(FEC,Forward Error Correction)使传输的信息量减少,提出了一种雨衰下的自适应信道分配的策略.该方法是采用调整冗余信道来保障信息信道的传输量以满足系统对误码率的要求,达到可靠和有效地利用有限的卫星信道资源的目的.通过仿真分析了该策略针对不同的频段和降雨情况下对呼叫产生的影响,分析中考虑了话音业务享有比数据业务更高的优先权.仿真结果表明利用冗余信道传输用于纠错的冗余信息可以保证在不产生通信中断的前提下,可以满足系统对信道误码率的要求.