Generation Of Satellites Research Articles

Wind wave footprint of tropical cyclones from satellite data

AbstractTropical cyclones are associated with extreme winds, waves, and storm surge, being among most destructive natural phenomena. Developing capability for a rapid impact estimate is crucial for coastal applications and risk preparedness. When predicting waves characteristics associated to tropical cyclones, the traditional approach involves a two‐step procedure (a) a Holland‐type wind vortex model and (b) numerical simulations using a wave generation model, using buoy and satellite measurements for validation. In this work, we take advantage of the increasing amount of remote sensing observational data and propose a new empirical model to estimate the wind wave footprint of tropical cyclones. For this purpose, we construct a dataset with over a million satellite observations of waves triggered by tropical cyclones assuming a circular shape of the TC influence area and defining composites of significant wave height as a function of representative parameters of the track characteristics like the minimum pressure, its forward velocity, and its latitude. The validation against buoy data confirms the usefulness of the model for a first and rapid estimation of the wave footprint, although an underestimation of the most extreme events is observed due to the relatively small number of observations recorded. Due to its efficiency, the model can be applied for rapid estimations of wave footprints in operational systems, reconstruction of historical or synthetic events and risk assessments.

An alternative cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method

Abstract. We develop a new way of retrieving the cloud index from a large variety of satellite instruments sensitive to reflected solar radiation, embedded on geostationary and non-geostationary platforms. The cloud index is a widely used proxy for the effective cloud transmissivity, also called the “clear-sky index”. This study is in the framework of the development of the Heliosat-V method for estimating downwelling solar irradiance at the surface of the Earth (DSSI) from satellite imagery. To reach its versatility, the method uses simulations from a fast radiative transfer model to estimate overcast (cloudy) and clear-sky (cloud-free) satellite scenes of the Earth’s reflectances. Simulations consider the anisotropy of the reflectances caused by both surface and atmosphere and are adapted to the spectral sensitivity of the sensor. The anisotropy of ground reflectances is described by a bidirectional reflectance distribution function model and external satellite-derived data. An implementation of the method is applied to the visible imagery from a Meteosat Second Generation satellite, for 11 locations where high-quality in situ measurements of DSSI are available from the Baseline Surface Radiation Network. For 15 min means of DSSI, results from our preliminary implementation of Heliosat-V and ground-based measurements show a bias of 20 W m−2, a root-mean-square difference of 93 W m−2, and a correlation coefficient of 0.948. The statistics, except for the bias, are similar to operational and corrected satellite-based data products HelioClim3 version 5 and the CAMS Radiation Service.

The influence of vegetation water dynamics on the ASCAT backscatter–incidence angle relationship in the Amazon

Abstract. Microwave observations are sensitive to plant water content and could therefore provide essential information on biomass and plant water status in ecological and agricultural applications. The combined data record of the C-band scatterometers on the European Remote-Sensing Satellites (ERS)-1/2, the Metop (Meteorological Operational satellite) series, and the planned Metop Second Generation satellites will span over 40 years, which would provide a long-term perspective on the role of vegetation in the climate system. Recent research has indicated that the unique viewing geometry of the Advanced SCATterometer (ASCAT) could be exploited to observe vegetation water dynamics. The incidence angle dependence of backscatter can be described with a second order polynomial, the slope and curvature of which are related to vegetation. In a study limited to grasslands, seasonal cycles, spatial patterns, and interannual variability in the slope and curvature were found to vary among grassland types and were attributed to differences in moisture availability, growing season length and phenological changes. To exploit ASCAT slope and curvature for global vegetation monitoring, their dynamics over a wider range of vegetation types needs to be quantified and explained in terms of vegetation water dynamics. Here, we compare ASCAT data with meteorological data and GRACE equivalent water thickness (EWT) to explain the dynamics of ASCAT backscatter, slope, and curvature in terms of moisture availability and demand. We consider differences in the seasonal cycle, diurnal differences, and the response to the 2010 and 2015 droughts across ecoregions in the Amazon basin and surroundings. Results show that spatial and temporal patterns in backscatter reflect moisture availability indicated by GRACE EWT. Slope and curvature dynamics vary considerably among the ecoregions. The evergreen forests, often used as a calibration target, exhibit very stable behavior, even under drought conditions. The limited seasonal variation follows changes in the radiation cycle and may indicate phenological changes such as litterfall. In contrast, the diversity of land cover types within the Cerrado region results in considerable heterogeneity in terms of the seasonal cycle and the influence of drought on both slope and curvature. Seasonal flooding in forest and savanna areas also produced a distinctive signature in terms of the backscatter as a function of incidence angle. This improved understanding of the incidence angle behavior of backscatter increases our ability to interpret and make optimal use of the ASCAT data record and vegetation optical depth products for vegetation monitoring.

Estimation of aboveground biomass production using an unmanned aerial vehicle (UAV) and VENμS satellite imagery in Mediterranean and semiarid rangelands

Rangeland management requires frequent and accurate estimation of aboveground vegetation biomass (AGB) as a proxy of forage production. However, traditional methods for AGB measurement are time-consuming and only provide a low number of spatiotemporal measurements. Newly developed remote sensing platforms such as UAVs and new generation satellites allow unprecedented large-scale and frequent monitoring of rangeland vegetation that is typically produced in marginal lands characterized by high surface heterogeneity. This study used high-resolution UAV data and the new multi-spectral VENμS satellite to monitor AGB production for two consecutive years in a Mediterranean and semiarid grassland rangeland in Israel. We then studied the effects of grazing intensity and precipitation along the growing season on AGB. Data were collected from two long-term ecological research sites with plots under controlled grazing pressures (i.e., no grazing, moderate, and intensive grazing). We used high-spatial-resolution UAV imagery for land cover classification in order to mask different levels of mixed pixels in the VENμS satellite images along the growing season. A support vector regression model (SVM) for satellite-based AGB estimation was developed using more than 600 ground-truth AGB measurements collected during 2019–2020. The effect of the percentage cover for mixed pixel removal was examined. The results showed an improvement in the prediction model by 35% and reduced the RMSE by half for the support vector machine regression model's best prediction. The best AGB prediction results were achieved by including satellite pixels with over 50% vegetation cover. For the first time in Eastern Mediterranean rangelands, our study illustrates the benefits of high-integrated remote sensing data (i.e., satellite and UAV) for generating more accurate AGB estimations even in highly heterogeneous ecosystems in terms of surface cover. Such an approach can provide crucial and better information for sustainable rangeland management under varying spatiotemporal and climatic conditions.

Ocean Image Formation Algorithm Using Altimeter Data for Next Generation Satellite SAR

Synthetic aperture radar (SAR) obtains two-dimensional images of the Earth’s surface. Spatial resolution is one of the most important factors in evaluating SAR performance. Therefore, high operating frequencies are preferred for obtaining high resolution images. In fact, Ku-band and Ka-band are used in the automobile SAR (Auto-SAR) and the aircraft SAR (Airborne-SAR). However, the operating frequencies of the satellite SAR (Spaceborne-SAR) are limited to the X-band because of the wide Doppler frequency band caused by the satellite’s high velocity. To overcome this frequency limitation in satellite SAR, we propose a low-complexity SAR algorithm that forms images of the ocean using satellite altimeter data with an operating frequency of the Ku-band (13.575 GHz). The algorithm requires the burst mode pulse transmission technique frequently used in SAR altimeters. To validate the proposed algorithm as being suitable for ocean image formation, point target simulation and analysis are performed, and some quantitative results are provided.

VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model

Abstract. After the eruption of volcanoes around the world, monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during the day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called “VADUGS” (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash-contaminated pixels with a probability of detection of 0.84 and a false-alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m−2 for concentrations smaller than 2.0 g m−2 and small overestimations in the range 5 %–50 % for moderate viewing angles 35–65∘, but up to 300 % for satellite viewing zenith angles close to 90 or 0∘. Ash top heights are mainly underestimated, with the smallest underestimation of −9 % for viewing zenith angles between 40 and 50∘. Absolute errors are smaller than 70 % and with high correlation coefficients of up to 0.7 for ash clouds with high mass column concentrations. A comparison with spaceborne lidar observations by CALIPSO/CALIOP confirms these results: For six overpasses over the ash cloud from the Puyehue-Cordón Caulle volcano in June 2011, VADUGS shows similar features as the corresponding lidar data, with a correlation coefficient of 0.49 and an overestimation of ash column concentration by 55 %, although still in the range of uncertainty of CALIOP. A comparison with another ash algorithm shows that both retrievals provide plausible detection results, with VADUGS being able to detect ash further away from the Eyjafjallajökull volcano, but sometimes missing the thick ash clouds close to the vent. VADUGS is run operationally at the German Weather Service and this application is also presented.

Packet Scheduling for Internet of Remote Things (IoRT) Devices in Next Generation Satellite Networks

Background: The massive amount of deployment of Internet of Things (IoT) devices via wireless communications has presented a new paradigm in next-generation mobile networks. The rapid growth in the deployment of IoT devices can be linked to the diverse use of several IoT applications for home automation, smart systems, and other forms of innovations in businesses and industry 4.0. Methods: There is a need for a robust network infrastructure to actualize the huge traffic demand of IoT communications in this new paradigm across the globe, including rural and remote areas. However, due to technical and economic constraints, the terrestrial network infrastructure is not able to fulfill this requirement. Hence, the need for satellite network infrastructure. This solution will be of immense benefit to the provision of remote health care, disaster management, remote sensing, and asset tracking, and environmental monitoring, to name a few. While this remains an interesting solution, packet scheduling, which is one of the key radio resource management functions, is still a challenging issue that remains undefined, especially in a satellite network scenario that has its peculiarities and challenges. Results: Hence, the goal of this research work is to design a new packet scheduling scheme suitable for machine-type communications and also mixed-use case scenarios in satellite network scenarios. The performance evaluation of the proposed packet scheduler is conducted through simulations. Conclusion: The newly proposed packet scheduling scheme provides an improvement of approximately 7 Mbps and 0.5 bps/Hz in terms of throughput and spectral efficiency performances, respectively, in mixed-use case scenarios, when compared to known throughput optimal packet schedulers, without serious compromise to other performance metrics.

Lightning-ignited wildfires and long continuing current lightning in the Mediterranean Basin: preferential meteorological conditions

Abstract. Lightning is the major cause of the natural ignition of wildfires worldwide and produces the largest wildfires in some regions. Lightning strokes produce about 5 % of forest fires in the Mediterranean Basin and are one of the most important precursors of the largest forest fires during the summer. Lightning-ignited wildfires produce significant emissions of aerosols, black carbon, and trace gases, such as CO, SO2, CH4, and O3, affecting air quality. Characterization of the meteorological and cloud conditions of lightning-ignited wildfires in the Mediterranean Basin can serve to improve fire forecasting models and to upgrade the implementation of fire emissions in atmospheric models. This study investigates the meteorological and cloud conditions of lightning-ignited wildfires (LIWs) and long continuing current (LCC) lightning flashes in the Iberian Peninsula and Greece. LCC lightning and lightning in dry thunderstorms with a low precipitation rate have been proposed to be the main precursors of the largest wildfires. We use lightning data provided by the World Wide Lightning Location Network (WWLLN), the Earth Networks Total Lightning Network (ENTLN), and the Lightning Imaging Sensor (LIS) on board the International Space Station (ISS), together with four databases of wildfires produced in Spain, Portugal, southern France, and Greece, respectively, in order to produce a climatology of LIWs and LCC lightning over the Mediterranean Basin. In addition, we use meteorological data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data set and by the Spanish State Meteorological Agency (AEMET), together with the Cloud Top Height product (CTHP) derived from Meteosat Second Generation (MSG) satellites measurements to investigate the meteorological conditions of LIWs and LCC lightning. According to our results, LIWs and a significant amount of LCC lightning flashes tend to occur in dry thunderstorms with weak updrafts. Our results suggest that LIWs tend to occur in clouds with a high base and with a vertical content of moisture lower than the climatological value, as well as with a higher temperature and a lower precipitation rate. Meteorological conditions of LIWs from the Iberian Peninsula and Greece are in agreement, although some differences possibly caused by the highly variable topography in Greece and a more humid environment are observed. These results show the possibility of using the typical meteorological and cloud conditions of LCC lightning flashes as proxy to parameterize the ignition of wildfires in atmospheric or forecasting models.

Hourly mapping of surface air temperature by blending geostationary datasets from the two-satellite system of GOES-R series

Spatio-temporally continuous surface air temperature (SAT) is a valuable input for many research fields such as environmental studies and hydrological models. Substantial efforts have been focused on mapping daily SAT using land surface temperature (LST) derived from polar-orbiting satellites such as NASA’s Terra and Aqua. However, reconstruction of SAT at very high temporal scales such as sub-daily or hourly was carried out in very few studies, most of which are based on the Meteosat Second Generation (MSG) satellites for small-scale areas with limited stations. In this study, we developed estimation schemes based on random forest for mapping hourly SAT over a large-scale region (CONUS) by blending LST datasets from the two-satellite system of the Geostationary Operational Environmental Satellite-R (GOES-R) Series with the aim of reconstructing the hourly SAT maps with spatially uniform resolutions of about 2 km over CONUS. Estimation schemes with different variables were compared to evaluate the contributions of reanalysis variables and the hour of day (HOD) variable to model predicative performance, and the impacts of station density on model performance were also explored. We found that the model scheme with reanalysis variables and the scheme with HOD achieved higher predicative performance with the mean RMSE of 2.0 K and 2.5 K, respectively. Systematic predictive errors across the synoptic hours of a diurnal cycle are significantly reduced when considering HOD in the modeling of hourly SAT. The model scheme considering both reanalysis variables and HOD achieved the highest performance with a mean RMSE of 1.9 K. We observed that the influence of station density on model performance is related to types of cross-validation and model schemes, and that the uncertainty levels associated with the evaluation of predicative performance for the model schemes decrease with station density, which increases our confidence in the evaluation results for the schemes. Therefore, the mapping accuracy of hourly SAT over the large-scale area CONUS could be conservatively estimated to be 1.9–2.5 K using site-based cross-validation. We expect that this study will facilitate the further mapping of SAT at high-temporal scales over large-scale areas based on abundant and sustained observations from global systems of meteorological geostationary satellites.

Metop First Generation AVHRR FRAC SST Reanalysis Version 1

The first full-mission global AVHRR FRAC sea surface temperature (SST) dataset with a nominal 1.1 km resolution at nadir was produced from three Metop First Generation (FG) satellites: Metop-A (2006-on), -B (2012-on) and -C (2018-on), using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) SST enterprise system. Historical reprocessing (‘Reanalysis-1’, RAN1) starts at the beginning of each mission and continues into near-real time (NRT). ACSPO generates two SST products, one with global regression (GR; highly sensitive to skin SST), and another one with piecewise regression (PWR; proxy for depth SST) algorithms. Small residual effects of orbital and sensor instabilities on SST retrievals are mitigated by retraining the regression coefficients daily, using matchups with drifting and tropical moored buoys within moving time windows. In RAN, the training windows are centered at the processed day. In NRT, the same size windows are employed but delayed in time, ending four to ten days prior to the processed day. Delayed-mode RAN reprocessing follows the NRT with a two-month lag, resulting in a higher quality and a more consistent SST record. In addition to its completeness, the newly created Metop-FG RAN1 SST dataset shows very close agreement with in situ data (including the fully independent Argo floats), well within the NOAA specifications for accuracy (global mean bias; ±0.2 K) and precision (global standard deviation; 0.6 K) in a ~20% clear-sky domain (percent of clear-sky SST pixels to the total of ice-free ocean). All performance statistics are stable in time, and consistent across the three platforms. The Metop-FG RAN1 data set is archived at the NASA JPL PO.DAAC and NOAA NCEI. This paper documents the newly created dataset and evaluates its performance.

Orthogonal Versus Zero-Forced Beamforming in Multibeam Antenna Systems: Review and Challenges for Future Wireless Networks

Orthogonality in multibeam antennas is revisited. The difference between orthogonal beamforming and zero-forced beamforming is highlighted. The intriguing relation between orthogonality, reciprocity and losses is recapitulated. Different approaches on the design of orthogonal beamforming networks and implementation of zero-forced beamforming strategies are shown with various examples from the antenna-research-oriented literature. The use of orthogonal and zero-forced beamforming is discussed from the communication system perspective with relevant studies from diverse disciplines. Some of the future research challenges and potential benefits are outlined for the next generation satellite and cellular communication applications.

Massive Access in Space-Based Internet of Things: Challenges, Opportunities, and Future Directions

The integration of fifth generation (5G) mobile network and satellite communication promises a promotion on the interconnection of everything, and has become one of the key development directions of beyond 5G and even 6G. As an inevitable development trend of mobile communication, the space-based Internet of Things (S-IoT) is expected to be commercially available in building an integrated information infrastructure network of the space, air, ground and oceans, and so on. However, the tremendous growth in the number of connected user equipments poses a fundamental rethinking of conventional multiple access technologies, which is considered one of the open challenges in future S-IoT networks. In this article, we first review the main challenges of massive access in S-IoT. Then, we present and discuss how to develop a scalable massive access S–IoT network by leveraging introduced technologies, including intelligent hybrid non-orthogonal multiple access (NOMA) transmission, grant-free superimposed pilot NOMA random access and multi-slot pilot allocation random access protocol. Finally, some exciting future directions of massive access in S-IoT are revealed.

The HPDPU—High-Performance Data Processing Unit for Future Satellite Communication Systems

Efficient data processing is a key enabler to compete with the excessive requirements of next generation satellite systems. In particular, connecting LEO satellite-constellations to existing terrestrial mobile communication services becomes increasingly important as this measure allows truly ubiquitous connectivity, but comes at the cost of significantly higher processing and inter satellite communication effort. To address this issue, we propose the High-Performance Data Processing Unit (HPDPU), possessing a powerful processing core that is available as radiation-hardened and COTS version in order to operate in traditional space and NewSpace scenarios, respectively. To achieve high re-usability, a modular cPCI-SS connector is used, that enable an easy a low-cost assembly, integration and testing of hardware units also during the design time. For a further increase of processing power, application-specific extension boards can be integrated by mezzanine connectors. To demonstrate the performance, an innovative transceiver signal processing chain is proposed that allows a reliable and low-latency data transmission. For evaluation, a real field test analysis of the transmission quality, a hardware performance comparison and a power estimation is presented. The results highlight the HPDPU to be a powerful solution in order to challenge the demanding requirements of high-end satellite communication systems of the near future.

The MSG Technique: Improving Commercial Microwave Link Rainfall Intensity by Using Rain Area Detection from Meteosat Second Generation

Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation (MSG) satellite data to improve MWL rainfall estimates. The investigation, conducted during daytime, used MSG optical (VIS0.6) and near IR (NIR1.6) data to estimate rain areas along a 15 GHz, 9.88 km MWL for classifying the MWL signal into wet–dry periods and estimate the baseline level. Additionally, the MSG technique estimated a new parameter, wet path length, representing the length of the MWL that was wet during wet periods. Finally, MWL rainfall intensity estimates from this new MSG and conventional techniques were compared to rain gauge estimates. The results show that the MSG technique is robust and can estimate gauge comparable rainfall estimates. The evaluation scores every three hours of RMSD, relative bias, and r2 based on the entire evaluation period results of the MSG technique were 2.61 mm h−1, 0.47, and 0.81, compared to 2.09 mm h−1, 0.04, and 0.84 of the conventional technique, respectively. For convective rain events with high intensity spatially varying rainfall, the results show that the MSG technique may approximate the actual mean rainfall estimates better than the conventional technique.

On the geometric accuracy and stability of MSG SEVIRI images

Monitoring the geometric quality and temporal stability of Earth Observation satellite sensors is a major task of satellite vendors. As an owner and operator of widely used meteorological satellites, EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) carries out operational activities and scientific studies on the radiometric and geometric quality assessments of geostationary Meteosat satellites among others. As part of Level 1 Product Monitoring - Evolution Studies initiated by EUMETSAT, a new methodology has been developed and implemented as a prototype software, namely the Geometric Quality Assessment (GQA) Tool; which enhances image local texture, extracts prominent features and matches them via area-based least squares, and eliminates possible outliers using a set of rules. This article presents the results of algorithm validation activities carried out by using time series images of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor aboard Meteosat Second Generation (MSG) satellites and MEdium Resolution Imaging Spectrometer (MERIS) global mosaics as reference to evaluate short- and long-term geometric temporal stability. Meteosat-11 SEVIRI time series data from a full-day and one year were employed for this purpose. In addition, the geometric quality of Meteosat-8 over Indian Ocean and Meteosat-10 in rapid scan mode was assessed for single acquisitions. The results show that the geometric accuracy of the evaluated datasets yield to the mean shifts up to one km in East-West (x) and North-South (y) directions, with larger errors in the y direction. The image inner geometric accuracy assessments revealed displacement errors with strip pattern in the three spectral bands (i.e. high-resolution visible, infra-red 10.8 and visible 0.8) evaluated within the framework.

Estimating Ocean Surface Currents With Machine Learning

Global surface currents are usually inferred from directly observed quantities like sea-surface height, wind stress by applying diagnostic balance relations (like geostrophy and Ekman flow), which provide a good approximation of the dynamics of slow, large-scale currents at large scales and low Rossby numbers. However, newer generation satellite altimeters (like the upcoming SWOT mission) will capture more of the high wavenumber variability associated with the unbalanced components, but the low temporal sampling can potentially lead to aliasing. Applying these balances directly may lead to an incorrect un-physical estimate of the surface flow. In this study we explore Machine Learning (ML) algorithms as an alternate route to infer surface currents from satellite observable quantities. We train our ML models with SSH, SST, and wind stress from available primitive equation ocean GCM simulation outputs as the inputs and make predictions of surface currents (u,v), which are then compared against the true GCM output. As a baseline example, we demonstrate that a linear regression model is ineffective at predicting velocities accurately beyond localized regions. In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman. Using a local stencil of neighboring grid points as additional input features, we can train the deep learning models to effectively “learn” spatial gradients and the physics of surface currents. By passing the stenciled variables through convolutional filters we can help the model learn spatial gradients much faster. Various training strategies are explored using systematic feature hold out and multiple combinations of point and stenciled input data fed through convolutional filters (2D/3D), to understand the effect of each input feature on the NN's ability to accurately represent surface flow. A model sensitivity analysis reveals that besides SSH, geographic information in some form is an essential ingredient required for making accurate predictions of surface currents with deep learning models.

Analysis of Digital Video Broadcast Second Generation Satellite (DVB-S2) for Simultaneous Voice Communication & TV Broadcast

The growing rivalry mainly in search of the audience in today’s TV broadcasting industry has made the real-time gathering of latest national and international developments through contribution link, the most critical part. The communication among field staff/reporter and head office is through the cellular system. In areas with no mobile coverage, it is almost impossible to stay in touch and very difficult to share the latest updates. In this paper, a detailed study of Digital Video Broadcast- second generation Satellite (DVB-S2) standard used in DSNG (Digital Satellite News Gathering) link is carried out for the feasibility of two ways communication with the digital television broadcast. We have used communication system toolbox in MATLAB Simulink to implement our model. We created the system according to the DVB-S2 standard and also used RF Satellite channel instead of AWGN (Adaptive White Gaussian Noise) channel. Simulation results show that communication signal can be transmitted with digital video television broadcast in DVB-S2 baseband frame.

Analysis of Digital Video Broadcast Second Generation Satellite (DVB-S2) for Simultaneous Voice Communication & TV Broadcast

The growing rivalry mainly in search of the audience in today’s TV broadcasting industry has made the real-time gathering of latest national and international developments through contribution link, the most critical part. The communication among field staff/reporter and head office is through the cellular system. In areas with no mobile coverage, it is almost impossible to stay in touch and very difficult to share the latest updates. In this paper, a detailed study of Digital Video Broadcast- second generation Satellite (DVB-S2) standard used in DSNG (Digital Satellite News Gathering) link is carried out for the feasibility of two ways communication with the digital television broadcast. We have used communication system toolbox in MATLAB Simulink to implement our model. We created the system according to the DVB-S2 standard and also used RF Satellite channel instead of AWGN (Adaptive White Gaussian Noise) channel. Simulation results show that communication signal can be transmitted with digital video television broadcast in DVB-S2 baseband frame.

Rain Area Detection in South-Western Kenya by Using Multispectral Satellite Data from Meteosat Second Generation.

This study presents a rain area detection scheme that uses a gradient based adaptive technique for daytime and nighttime rain area detection and correction from reflectance and infrared (IR) brightness temperatures data of the Meteosat Second Generation (MSG) satellite. First, multiple parametric rain detection models developed from MSG’s reflectance and IR data were calibrated and validated with rainfall data from a dense network of rain gauge stations and investigated to determine the best model parameters. The models were based on a conceptual assumption that clouds characterised by the top properties, e.g., high optical thickness and effective radius, have high rain probabilities and intensities. Next, a gradient based adaptive correction technique that relies on rain area-specific parameters was developed to reduce the number and sizes of the detected rain areas. The daytime detection with optical (VIS0.6) and near IR (NIR1.6) reflectance data achieved the best detection skill. For nighttime, detection with thermal IR brightness temperature differences of IR3.9-IR10.8, IR3.9-WV73 and IR108-WV62 showed the best detection skill based on general categorical statistics. Compared to the Global Precipitation Measurement (GPM) Integrated Mult-isatellitE Retrievals for GPM (IMERG) and the gauge station data from the southwest of Kenya, the model showed good agreement in the spatial dynamics of the detected rain area and rain rate.

Dual View on Clear-Sky Top-of-Atmosphere Albedos from Meteosat Second Generation Satellites

Geostationary observations offer the unique opportunity to resolve the diurnal cycle of the Earth’s Radiation Budget at the top of the atmosphere (TOA), crucial for climate-change studies. However, a drawback of the continuous temporal coverage of the geostationary orbit is the fixed viewing geometry. As a consequence, imperfections in the angular distribution models (ADMs) used in the radiance-to-flux conversion process or residual angular-dependent narrowband-to-broadband conversion errors can result in systematic errors of the estimated radiative fluxes. In this work, focusing on clear-sky reflected TOA observations, we compare the overlapping views from Meteosat Second Generation satellites at 0° and 41.5°E longitude which enable a quantification of viewing-angle-dependent differences. Using data derived from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), we identify some of the main sources of discrepancies, and show that they can be significantly reduced at the level of one month. This is achieved, separately for each satellite, via a masking procedure followed by an empirical fit at the pixel-level that takes into account all the clear-sky data from that satellite, calculated separately per timeslot of the day, over the month of November 2016. The method is then applied to each month of 2017, and gives a quadratic mean of the albedo root-mean squared difference over the dual-view region which is comparable from month to month, with a 2017 average value of 0.01. Sources of discrepancies include the difficulty to estimate the flux over the sunglint ocean region close to the limbs, the fact that the data processing does not include dedicated angular distribution models for the aerosol-over-ocean case, and the existence of an observer-dependent diurnal-asymmetry artefact affecting the clear-sky-albedo dependence on the solar zenith angle particularly over land areas.