Earth-space Links Research Articles

ANN_ITU: Predicting rain attenuation with a hybrid model for earth-space links

Rain attenuation prediction of earth-space links is of vital significance for the application and development of satellite communication. Recently, most rain attenuation prediction methods are based on semi-empirical models or data-driven models, the former suffering from incompleteness problem, the latter faced with limited performance due to scarce data. In order to realize higher rain attenuation prediction performance, we propose a novel hybrid model ANN_ITU that combines advantages of the semi-empirical model and the artificial neural network. In ANN_ITU framework, the semi-empirical model ITU-R P.618-12 is leveraged to predict rain attenuation, and a six-layer artificial neural network is utilized to correct the rain attenuation predicted by ITU-R P.618-12, thus generating the final rain attenuation value. What’s more, we also present theories of two machine-learning based rain attenuation prediction methods, namely, random forest and support vector regression. Last but not least, we expound on processes of DBSG3 dataset filtering and data preprocessing. Experiments on DBSG3 dataset are carried out. Experimental results demonstrate that the hybrid ANN_ITU algorithm outperforms purely semi-empirical algorithms and data-driven algorithms. The evaluation indexes mean value, standard deviation, and root mean square value are 0.0355%, 19.63%, and 19.63%, respectively, which prove the effectiveness and precision of our rain attenuation prediction model ANN_ITU.

Comparison of Simulated and Measured Power of the Earth-Space Link for Satellite-Based AIS Signals.

This research aims to analyze the impact of the Earth-Space link on the Automatic Identification System (AIS) signals of ships. To achieve this, we established a simulation system that measures the receiving power of AIS signals via satellite platforms. We validated the system by utilizing observation data from Tiantuo-5. Through this simulation, we quantitatively analyzed the effects of ionospheric TEC (Total Electron Content) and space loss on the received power. During the processing of observation data, we construct a geometric propagation model utilizing the measured positions of both the satellite and the ship. We then calculate the antenna gain and remove any system errors. Additionally, we eliminate the deviation of elevation and azimuth angles caused by satellite motion. This allows us to determine the actual power of different ships reaching the receiving platform. Upon comparing the measured power data with the simulated power, it was noted that both exhibited an increasing trend as the elevation angle increased. This led to an RMSE (Root Mean Square Error) result of approximately one, indicating the accuracy of the simulation system. These findings hold significant implications for analyzing interference factors in satellite-ground links.

Detection of Bright Band Heights from Vertical Profile of Radar Reflectivity in Akure, South Western Nigeria

Bright band is an important phenomenon that occurs in radar observations of precipitation. It is a layer of intense reflectivity caused by the melting of the snowflakes in the atmosphere. Accurate detection of bright band and its associated properties are essential for the estimation of precipitation rates and characterization of precipitation types as required in Agriculture, Aviation, Navigation, Telecommunication sectors etc. Bright Band Height (BBH) is a vital parameter in the determination of rain height, rain-induced radio signal attenuation and mitigation techniques. This paper presents an empirical study on the measurement of BBH and Zero Degree Isotherm Height (ZDIH) from Micro Rain Radar (MRR) data obtained in Akure, Nigeria. Vertical profile of radar obtained during stratiform rainy events were examined to detect bright bands and its features. Analysis of the result showed that the ZDIH varies between 4.48 and 4.60 km while the BBH also vary from 4.16 to 4.48 km during intense rainy events in September and October 2010. The derived propagation data would be needed for the optimization and improvement of quality of service (QoS) offered by earth-space links during rainy events in Akure and its environs.

Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links.

In the climate change scenario the world is facing, extreme weather events can lead to increasingly serious disasters. To improve managing the consequent risks, there is a pressing need to have real-time systems that provide accurate monitoring and possibly forecasting which could help to warn people in the affected areas ahead of time and save them from hazards. The oblique earth-space links (OELs) have been used recently as a method for real-time rainfall detection. This technique poses two main issues related to its indirect nature. The first one is the classification of rainy and non-rainy periods. The second one is the determination of the attenuation baseline, which is an essential reference for estimating rainfall intensity along the link. This work focuses mainly on the first issue. Data referring to eighteen rain events were used and have been collected by analyzing a satellite-to-earth link quality and employing a tipping bucket rain gauge (TBRG) properly positioned, used as reference. It reports a comparison among the results obtained by applying four different machine learning (ML) classifiers, namely the support vector machine (SVM), neural network (NN), random forest (RF), and decision tree (DT). Various data arrangements were explored, using a preprocessed version of the TBRG data, and extracting two different sets of characteristics from the microwave link data, containing 6 or 12 different features, respectively. The achieved results demonstrate that the NN classifier has outperformed the other classifiers.

Artificial Neural Networks for Earth-Space Link Applications: A Prediction Approach and Intercomparison of Rain-influenced Attenuation Models

The impact of rain-influenced attenuation (RIA) has a more pronounced effect as frequency increases, especially in the tropical zones with heavier rainfall than the temperate zones. The International Telecommunication Union (ITU) has recommended a universal model which may not fit well in this tropical region due to the temperate data used to develop the model. It is therefore necessary to adopt locally measured data to develop a suitable model for each region, as also recommended by ITU recommendation 618-13. The experimental site for this study is at the Department of Physics, Federal University of Technology, Akure, Nigeria (7.299° N, 5.147° E) in the tropical rainforest region of Nigeria. In the present work, the backpropagation neural network (BPNN) of the artificial neural network (ANN) is trained based on time-series rain rates data collected between 2015 and 2019 to predict time-series RIA. Based on four inputs (rain rate, rain heights, elevation angle, and polarization angle), the generated data was subjected to training, validation, and testing. The ANN was further trained using the Levenberg-Marquardt algorithm to fit the inputs and the targets to create a dynamic model for RIA forecasting. Further validation was tested using actual data of rain attenuation from a Ku-band beacon at the site. Subsequently, the RIA model created by the ANN was compared to those generated using the synthetic storm technique, ITU, and the actual rain attenuation obtained from a beacon measurement. The highest rain rate observed was about 225.8 mm/hr with a corresponding rain attenuation of about 61 dB as estimated by the SST model and about 68 dB by the ITU model, while the predicted attenuation by the ANN is 55 dB. This implies that an extra power of 6 dB and 13 dB is added by the SST model and ITU model, respectively, for the downlink signal, to compensate for the rain attenuation link. The results also reveal that during 0.01 percent of an average year that signal may be attenuated, a relatively tiny margin of error between anticipated rain attenuation using ANN and the SST model is exceeded. In general, the new ANN-generated RIA model had the lowest root mean square error, average relative error, and standard deviation at the selected time percentages, according to the model validation. Hence, the new ANN model can predict more effective RIA in the region when compared with the global ITU-R model.

Preliminary Investigation of the Potentialities of a Mesoscale Meteorological Model to Reproduce Experimental Statistics of Rain Attenuation on Earth-Space Links

Current spatial resolutions achieved by mesoscale weather forecast models allow them to be used to generate the state of the lowest layers of the atmosphere over areas as small as a few square kilometers which corresponds to the typical size of the tropospheric area crossed by Earth-space links. Furthermore, they allow the evolution of the troposphere to be predicted with a time stamp of five minutes instead of every hour with large-scale weather forecast models which makes them attractive for radio propagation predictions for satellite communication applications. This paper aims at studying the capability of the Weather Research and Forecast (WRF) model coupled with an electromagnetic physical model to reproduce rain attenuation statistics for Earth-space paths at Ka-band. To this purpose, one year of propagation measurements collected at 20 GHz in different places at midlatitudes in Toulouse and Salon de Provence (France), Spino d’Adda (Italy), Aveiro (Portugal), and Madrid (Spain), at high latitudes in Svalbard (Norway) and at low latitudes in Kourou are used to make comparisons between simulations and measurements. Comparisons between the simulated and the experimental annual statistics considered in this paper provide encouraging results, with a similar accuracy as Recommendation ITU-R P.618–13 for midlatitude European locations and with better accuracy for a high latitude area in Svalbard and for an equatorial location in French Guiana.

Reconstruction of Rainfall Field Using Earth–Space Links Network: A Compressed Sensing Approach

High-precision rainfall information is of great importance for the improvement of the accuracy of numerical weather prediction and the monitoring of floods and mudslides that affect human life. With the rapid development of satellite constellation networks, there is great potential for reconstructing high-precision rainfall fields in large areas by using widely distributed Earth–space link (ESL) networks. In this paper, we have carried out research on reconstructing high-precision rainfall fields using an ESL network with the compressed sensing (CS) method in the case of a sparse distribution of the ESLs. Firstly, ESL networks with different densities are designed using the K-means clustering algorithm. The real rainfall fields are then reconstructed using the designed ESL networks with CS, and the reconstructed results are compared with that of the inverse distance weighting (IDW) algorithm. The results show that the root mean square error (RMSE) and correlation coefficient (CC) of the reconstructed rainfall fields using the ESL network with CS are lower than 0.15 mm/h and higher than 0.999, respectively, when the density is 0.05 links per square kilometer, indicating that the ESL network with CS is capable of reconstructing the high-precision rainfall fields under sparse sampling. Additionally, the performance of reconstructing the rainfall fields using the ESL networks with CS is superior compared to the reconstructed results of the IDW algorithm.

Conversion of 15-Minutes to 1-Minute Rainfall Distribution Derived from Tropical Rainfall Distribution Measurement

The radiocommunication sector of the International Telecommunication Union (ITU R) necessitates a 1-min integration time of rainfall rate to predict attenuation at terrestrial links and earth-space links. Since time series of rainfall that satisfies this requirement is not common, the conversion into corresponding 1-min distribution is suggested as an alternative of time series with longer integration time, T (e.g., 10-,30-,60-minutes). This research reviews existing methods for the conversion of cumulative distributions of rainfall and compares the said conversion methods. Several approaches are utilized to convert the 15-minutes rainfall rate to a 1-min rainfall rate in the tropical area of Kampung Paya Jaras. It found that Segal’s method performs the best conversion procedure overall.

Weighted Mean Annual Rain Height Applied to Rain Attenuation Prediction on Earth-Space Links

Rain height is crucial for rain attenuation prediction along slant paths, such as Earth-space links. The global digital map of the mean annual rain height is given in Recommendation ITU-R P.839-4. The disadvantage of this model is that heavy rainfall in most regions of the middle and high latitudes of the world is typically concentrated over a few months, such as summer in North China, and the mean rain height during these months and the mean annual rain height are often significantly different. In this study, a new weighted mean annual rain height is proposed by using the ratio of the mean monthly rainfall to the mean annual rainfall as the weight. The analysis shows that the weighted mean annual rain heights have been closer to the statistical rain heights during heavy rainfall in general. On the other hand, the work of this paper helps to improve the accuracy of the rain attenuation prediction model on Earth-space links in China. However, the weighted mean annual rain heights have different characteristics for different rainfall climate zones. This study will contribute to the research of rain attenuation modelling on Earth-space links.

Research on the Method of Rainfall Field Retrieval Based on the Combination of Earth–Space Links and Horizontal Microwave Links

High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth–space links (ESLs) and horizontal microwave links (HMLs) to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. In this paper, we carry out the research of combining multiple ESLs with HMLs to retrieve rainfall fields. Firstly, a rainfall detection network for retrieving rainfall fields is built based on the atmospheric propagation model of ESL and HML. Then, the ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network are applied to the reconstruction of rainfall fields. Finally, the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. The results show that the joint network of ESLs and HMLs based on OK algorithm and RBF neural network is capable of retrieving the distribution of rain rates in different rain cells with high accuracy, and the root mean square error (RMSE) of retrieving the rain rates of real rainfall fields is lower than 0.56 mm/h, and the correlation coefficient (CC) is higher than 0.996. In addition, the CC for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs is higher than 0.949, indicating that the characteristics of the two different types of rainfall events can be accurately monitored.

PERFORMANCE OF RAIN- INDUCED ATTENUATION PREDICTION MODELS FOR TERRESTRIAL LINK IN CAMEROON

Weather, notably rain, has a negative impact on radio wave propagation between terrestrial and earthspace links at frequencies above 10 GHz. Therefore raininduced attenuation is a significant propagation impact that must be considered when designing satellite communication systems. Analysis of rain attenuation for earth-space links in three locations in Cameroon at V, Ku and Ka bands is investigated using four rain attenuation models: The ITU-R P.618 model, Svjatogor model, GarciaLopez model and Bryant model at 42.5° elevation angles. The major goal is to figure out which rain attenuation prediction models are best for satellite communication in this area. Five years (2013–2017) daily rainfall data obtained from the Tropical Rain Measuring MissionPrecipitation Radar (TRMM-PR) and Global Precipitation Measurement (GPM) mission's Core Observatory were used for this study. The results showed that the ITUR P.618, Garcia-Lopez, and Svjatogor models performed best in this region. Attenuation ranged from 15 dB to 16 at 42.5° elevation angle for time exceedance of 0.01% at Kuband in all the study locations. For the Ka-band, attenuation varied between 32 dB and 38 dB. Signal availability at Ku-band is possible based on predicted rain attenuation values for 0.01 %-time exceedance. At Ka and V-band, the predicted rain attenuation values for 0.01%- time exceedance have shown that availability of signal is impossible, which infers losses of the signal during such rainfall events across the selected locations in Cameroon

Evaluation of Satellite\u2019s Point-Ahead Angle Derived from TLE for Laser Communication

Advances in lasers, optics and electronics for Satellite’s optical communication are opening the possibility of very high performance near Earth space links with data rate up to several Gbps. Being the divergence of the laser beam typically of tens of murad, an extremely high precision pointing is needed to correctly establish and maintain data optical link. In particular, the relative motion between the satellite and the ground station shall be accurately evaluated to estimate how to correct pointing angles for future orbital locations. This correction is made via a point-ahead mirror (PAM) mechanism, which deviates the laser beam by an angle called point-ahead angle (PAA). The purpose of this paper is evaluate the possibility of accurately estimate the point-ahead angle in advance using the two-line elements sets for the orbiting satellite, which are available before the ground station overpass. The study evaluated TLE-based orbital evolution of Sentinel-6 satellite, comparing the results with the high precision data obtained by laser ranging from the crustal dynamics data information system (CDDIS). The maximum error observed between the estimated and measured point-ahead angles was less than 1murad, demonstrating the possibility of this point-ahead correction technique for LEO orbiting satellites.

Prediction of Tropospheric Amplitude Scintillation for Accurate Design of Earth-Space Communication Link over Akure, Nigeria

Prediction of Tropospheric Amplitude Scintillation for Accurate Design of Earth-Space Communication Link over Akure, Nigeria

Distribution of tropospheric amplitude scintillation on Ku-Band earth-space link over a tropical station

Distribution of tropospheric amplitude scintillation on Ku-Band earth-space link over a tropical station

Calculation and analysis of cloud attenuation and other cloud parameters in India for earth-space links

• The cloud attenuation is higher during the SW monsoon season followed by NE monsoon, pre-monsoon and winter seasons at the places considered. • The maximum cloud attenuation of 1.67 dB/km is measured in Hyderabad by WVP-NTU cloud detection model at 24 GHz. • The WVP-NTU cloud detection model is more suitable for the five places considered as compared to the SU model. An effort is made to examine the cloud parameters over few stations in India for one year from October 2016 to September 2017. This study includes the investigation of cloud base height, cloud top height, zero degree isotherm height, number of cloud layers, cloud thickness, number of days cloud detected, cloud liquid water content and cloud attenuation at 6 GHz, 12.5 GHz and 24 GHz. Salonen Uppala (SU) model and Water Vapor Pressure-Nanyang Technological University (WVP-NTU) model are used for cloud detection using the radiosonde data at Bangalore, Hyderabad, Mumbai, Thiruvananthapuram and Chennai. The cloud parameters found using both the models are compared. WVP-NTU model shows enhanced values for most of the cloud parameters over SU model. Cloud base height calculated using these models are compared with METAR data and found that Cloud base height from WVP-NTU model matches well with METAR data. Cloud liquid water content and cloud attenuation values are calculated using the ITU-R model. The cumulative distributions (CDF) of cloud attenuation for one year are compared for the places considered. The seasonal CDFs of cloud attenuation using the WVP model are compared with the rain statistics. In this study, the maximum cloud attenuation of 1.67 dB/km is measured in Hyderabad by WVP-NTU combined ITU-R model at 24 GHz.

A Survey of Rain Fade Models for Earth–Space Telecommunication Links—Taxonomy, Methods, and Comparative Study

Satellite communication is a promising transmission technique to implement 5G and beyond networks. Attenuation due to rain begins at a frequency of 10 GHz in temperate regions. However, some research indicates that such attenuation effects start from 5–7 GHz, especially in tropical regions. Therefore, modeling rain attenuation is significant for propagating electromagnetic waves to achieve the required quality of service. In this survey, different slant link rain attenuation prediction models have been examined, classified, and analyzed, and various features like improvements, drawbacks, and particular aspects of these models have been tabulated. This survey provides various techniques for obtaining input data sets, including rain height, efficient trajectory length measurement techniques, and rainfall rate conversion procedures. No survey of the Earth–space link models for rain attenuation is available to the best of our knowledge. In this study, 23 rain attenuation models have been investigated. For easy readability and conciseness, the details of each model have not been included. The comparative analysis will assist in propagation modeling and planning the link budget of slant links.

Rain height dynamics over some coastal cities of South Africa for earth-space links design

One of the major parameters required for the prediction of rain-induced attenuation is the rain height. This paper presents some dynamic features of rain height measurement from TRMM-PR over two coastal cities in the Republic of South Africa. The analysis of 5-year data on the variation of bright band height, atmospheric pressure, relative humidity, and temperature reveal seasonal dependence over the studied locations. The result shows the existence of stratiform-convective interaction within the season, especially during the summer months. The observed exceedance in the probability of the bright band also shows the presence of two rain type (stratiform and convective). Comparison of rain height measurements with the recommended ITU-R reveals above 1 km variations with the percentage difference of about 30%, which could create a serious negative effect on the link design. This study suggests the adaptation of local rain height data for better accuracy of link design for the region.

Statistical Study of Rainfall Inversion Using the Earth-Space Link at the Ku Band: Optimization and Validation for 1 Year of Data

The large-scale monitoring of rainfall is of great significance in the research of meteorology, hydrology, and atmospheric measurement science. In recent years, with the quick development of communication satellite constellation, the use of earth-space link (ESL) to measure rainfall in the atmosphere is expected to be a potential approach for the large-scale monitoring of global rainfall. In this article, to verify the long-term performance of rainfall measurement using ESL, the data of an ESL at the Ku band and a Thies Laser Precipitation Monitor (LPM) in Nanjing were collected. The rainfall inversion model using ESL was optimized according to the height of 0 °C-layer from the radiosonde data of ten years, and the inversion results in the different types of rainfall were discussed. The results show that the rainfall inversed by the optimized ESL model are in good agreement with the rainfall measured by LPM (correlative coefficient is 0.985), the relative errors of rain intensity inversed by ESL in light rain, moderate rain, heavy rain, and extreme rain are 20%, 15.17%, 8.93%, and 8.99%, respectively. The average relative errors of rain intensity measured by the ESL in convective rainfall and stratiform rainfall are 16.01% and 26.59%, respectively.

PREDICTION OF RAIN-INDUCED ATTENUATION ALONG EARTH-SPACE LINKS AT MILLIMETER WAVE BANDS OVER WEST AFRICAN REGION

PREDICTION OF RAIN-INDUCED ATTENUATION ALONG EARTH-SPACE LINKS AT MILLIMETER WAVE BANDS OVER WEST AFRICAN REGION

Reconstruction and Nowcasting of Rainfall Field by Oblique Earth-Space Links Network: Preliminary Results from Numerical Simulation

High-precision rainfall field reconstruction and nowcasting play an important role in many aspects of social life. In recent years, the rain-induced signal attenuation of oblique earth-space links (OELs) has been presented to monitor regional rainfall. In this paper, we set up the first OEL in Nanjing, China, for the estimation of rain intensity. A year of observations from this link are also compared with the measurements from laser disdrometer OTT-Parsivel (OTT), between which the correlation is 0.86 and the determination coefficient is 0.73. Then, the simulation experiment is carried out: an OELs network is built, and the Kriging interpolation algorithm is employed to perform rainfall field reconstruction. The rainfall fields of plum rain season from 2016 to 2019 have been reconstructed by this network, which shows a good agreement with satellite remote sensing data. The resulting root-mean-square errors are lower than 3.46 mm/h and spatial correlations are higher than 0.80. Finally, we have achieved the nowcasting of rainfall field based on a machine-learning approach, especially deep learning. It can be seen from experiment results that the motion of rain cell and the position of peak rain intensity are predicted successfully, which is of great significant for taking concerted actions in case of emergency. Our experiment demonstrates that the densely distributed OELs are expected to become a futuristic rainfall monitoring system complementing existing weather radar and rain gauge observation networks.