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NASA NTRS2016

GOES-16 On-Orbit Dual Isolation Performance Characterization Results

The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the first of the next generation geostationary weather satellites. GOES-R successfully launched on November19, 2016 and renamed GOES-16 upon entering geostationary orbit. Subsequently, GOES-16post-launch testing began. This paper presents the GOES-16 Satellite Dynamic Interaction Characterization results for the Earth Pointed Platform (EPP) stowed, referred to as the Reaction Wheel Assembly (RWA) Isolation Only configuration, and deployed, referred to as the Dual Isolation configuration. GOES-R represents a quantum increase in Earth and solar weather observation capabilities, with 4 times the resolution, 5 times the observation rate, and 3 times the number of spectral bands for Earth observations. With the improved resolution, comes the instrument suites increased sensitivity to disturbances over a broad spectrum 0-512Hz. Sources of disturbance include reaction wheels, thruster firings for station keeping and momentum management, gimbal motion, and internal instrument disturbances. To minimize the impact of these disturbances, the baseline design included an EPP, a stiff optical bench to which the two nadir pointed instruments are collocated together with the Guidance Navigation Control (GNC) star trackers and Inertial Measurement Units (IMUs). The EPP is passively isolated from the spacecraft bus with Honeywell D-Strut isolators providing attenuation for frequencies above 5 Hz in all six degrees-of-freedom. To reduce the risk of wheel disturbances impacting performance, a secondary passive isolation system manufactured by Moog CSA Engineering was incorporated under each of the six 160 Nms reaction wheels, tuned to provide attenuation at frequencies above 50 Hz. Integrated wheel and isolator testing was performed on a Kistler table at NASA Goddard Space Flight Center. Pre-launch Satellite Dynamic Interaction Characterization high-fidelity simulations and ground testing were conducted to evaluate jitter performance for two cases: 1) deployed EPP and reaction wheel (Dual Isolation) and 2) EPP hard mounted (RWA Isolation Only) to the spacecraft. A comparison of pre-launch to post-launch Satellite Dynamic Interaction Characterization results are also presented in this paper.

Carter, Delano↗

NASA NTRS2018

New, Multi-Spectral (RGB) Imagery Products from GOES-16 Usher in a New Era of Satellite Meteorology

Otherwise known as "R-G-B" imagery, these new products provide a paradigm shift in short-term nowcasting and situational awareness via remote sensing. SPoRT used NASA instruments like MODIS and S-NPP VIIRS to demonstrate RGB capabilities prior to GOES-16. Now, with geostationary RGBs in operations, SPoRT has collaborated with select forecasters to assess the impact of this new GOES-16 capability.

Fuell, Kevin K.↗

NASA NTRS2017

In-Flight Guidance, Navigation, and Control Performance Results for the GOES-16 Spacecraft

The Geostationary Operational Environmental Satellite-R Series (GOES-R), which launched in November 2016, is the first of the next generation geostationary weather satellites. GOES-R provides 4 times the resolution, 5 times the observation rate, and 3 times the number of spectral bands for Earth observations compared with its predecessor spacecraft. Additionally, Earth relative and Sun-relative pointing and pointing stability requirements are maintained throughout reaction wheel desaturation events and station keeping activities, allowing GOES-R to provide continuous Earth and sun observations. This paper reviews the pointing control, pointing stability, attitude knowledge, and orbit knowledge requirements necessary to realize the ambitious Image Navigation and Registration (INR) objectives of GOES-R. This paper presents a comparison between low-frequency on-orbit pointing results and simulation predictions for both the Earth Pointed Platform (EPP) and Sun Pointed Platform (SPP). Results indicate excellent agreement between simulation predictions and observed on-orbit performance, and compliance with pointing performance requirements. The EPP instrument suite includes 6 seismic accelerometers sampled at 2 KHz, allowing in-flight verification of jitter responses and comparison back to simulation predictions. This paper presents flight results of acceleration, shock response spectrum (SRS), and instrument line of sight responses for various operational scenarios and instrument observation modes. The results demonstrate the effectiveness of the dual-isolation approach employed on GOES-R. The spacecraft provides attitude and rate data to the primary Earth-observing instrument at 100 Hz, which are used to adjust instrument scanning. The data must meet accuracy and latency numbers defined by the Integrated Rate Error (IRE) requirements. This paper discusses the on-orbit IRE results, showing compliance to these requirements with margin. During the spacecraft checkout period, IRE disturbances were observed and subsequently attributed to thermal control of the Inertial Measurement Unit (IMU) mounting interface. Adjustments of IMU thermal control and the resulting improvements in IRE are presented. Orbit knowledge represents the final element of INR performance. Extremely accurate orbital position is achieved by GPS navigation at Geosynchronous Earth Orbit (GEO). On-orbit performance results are shown demonstrating compliance with the stringent orbit position accuracy requirements of GOES-R, including during station keeping activities and momentum desaturation events. As we show in this paper, the on-orbit performance of the GNC design provides the necessary capabilities to achieve GOES-R mission objectives.

Chapel, Jim↗

NASA NTRS2017

Characterizing the GOES-R (GOES-16) Geostationary Lightning Mapper (GLM) On-Orbit Performance

Two overlapping efforts help to characterize the GLM performance, the Post Launch Test (PLT) phase to validate the predicted pre-launch instrument performance and the Post Launch Product Test (PLPT) phase to validate the lightning detection product used in forecast and warning decision-making. This paper documents the calibration and validation plans and activities for the first 6 months of GLM on-orbit testing and validation commencing with first light on 4 January 2017. The PLT phase addresses image quality, on-orbit calibration, RTEP threshold tuning, image navigation, noise filtering, and solar intrusion assessment, resulting in a GLM calibration parameter file. The PLPT includes four main activities, the Reference Data Comparisons (RDC), Algorithm Testing (AT), Instrument Navigation and Registration Testing (INRT), and Long Term Baseline Testing (LTBT). Field campaigns are also designed to contribute valuable insights into the GLM performance capabilities. The PLPT tests each contribute to the beta, provisional, and fully validated GLM data.

validation↗

NASA NTRS2018

On-Orbit Validation of the Geolocation Accuracy of GOES-16 Geostationary Lightning Mapper (GLM) Flashes Using Ground-Based Laser Beacons

No abstract available

Geostationary Lightning Mapper (GLM)↗

NASA NTRS2017

GPS Receiver On-Orbit Performance for the GOES-R Spacecraft

This paper evaluates the on-orbit performance of the first civilian operational use of a Global Positioning System Receiver (GPSR) at a geostationary orbit (GEO). The GPSR is on-board the newly launched Geostationary Operational Environmental Satellite (GOES-R). GOES-R is the first of four next generation GEO weather satellites for NOAA, now in orbit GOES-R is formally identified as GOES-16. Among the pioneering technologies required to support its improved spatial, spectral and temporal resolution is a GPSR. The GOES-16 GPSR system is a new design that was mission critical and therefore received appropriate scrutiny. As ground testing of a GPSR for GEO can only be done by simulations with numerous assumptions and approximations regarding the current GPS constellation, this paper reveals what performance can be achieved in using on orbit data. Extremely accurate orbital position is achieved using GPS navigation at GEO. Performance results are shown demonstrating compliance with the1007575 meter and 6 cms radial/in-track/cross-track orbital position and velocity accuracy requirements of GOES-16. The aforementioned compliance includes station-keeping and momentum management maneuvers, contributing to no observational outages. This performance is achieved by a completely new system design consisting of a unique L1 GEOantenna, low-noise amplifier (LNA) assembly and a 12 channel GPSR capable of tracking the edge of the main beam and the side lobes of the GPS L1 signals. This paper presents the definitive answer that the GOES-16 GPSR solution exceeds all performance requirements tracking up to 12 satellites and achieving excellent carrier-to-noise density (C/N0). Additionally, these performance results show the practicality of this approach. This paper makes it clear that all future GEO Satellites should consider the addition of a GPSR in their spacecraft design, otherwise they may be sacrificing spacecraft capabilities and accuracy along with incurring increased and continual demand on ground support.

GEO GPS↗

NASA NTRS2017

GOES-R Active Vibration Damping Controller Design, Implementation, and On-Orbit Performance

GOES-R series spacecraft feature a number of flexible appendages with modal frequencies below 3.0 Hz which, if excited by spacecraft disturbances, can be sources of undesirable jitter perturbing spacecraft pointing. In order to meet GOES-R pointing stability requirements, the spacecraft flight software implements an Active Vibration Damping (AVD) rate control law which acts in parallel with the nadir point attitude control law. The AVD controller commands spacecraft reaction wheel actuators based upon Inertial Measurement Unit (IMU) inputs to provide additional damping for spacecraft structural modes below 3.0 Hz which vary with solar wing angle. A GOES-R spacecraft dynamics and attitude control system identified model is constructed from pseudo-random reaction wheel torque commands and IMU angular rate response measurements occurring over a single orbit during spacecraft post-deployment activities. The identified Fourier model is computed on the ground, uplinked to the spacecraft flight computer, and the AVD controller filter coefficients are periodically computed on-board from the Fourier model. Consequently, the AVD controller formulation is based not upon pre-launch simulation model estimates but upon on-orbit nadir point attitude control and time-varying spacecraft dynamics. GOES-R high-fidelity time domain simulation results herein demonstrate the accuracy of the AVD identified Fourier model relative to the pre-launch spacecraft dynamics and control truth model. The AVD controller on-board the GOES-16 spacecraft achieves more than a ten-fold increase in structural mode damping of the fundamental solar wing mode while maintaining controller stability margins and ensuring that the nadir point attitude control bandwidth does not fall below 0.02 Hz. On-orbit GOES-16 spacecraft appendage modal frequencies and damping ratios are quantified based upon the AVD system identification, and the increase in modal damping provided by the AVD controller for each structural mode is presented. The GOES-16 spacecraft AVD controller frequency domain stability margins and nadir point attitude control bandwidth are presented along with on-orbit time domain disturbance response performance.

Active Vibration Damping↗

NASA NTRS2017

GEONEX: Land Monitoring From a New Generation of Geostationary Satellite Sensors

The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval algorithms (e.g., LAI and FPAR, GPP, etc.) for subsequent science product generation. Initial evaluation of Himawari AHI products against standard MODIS products indicate general agreement, suggesting that data from geostationary sensors can augment low earth orbit (LEO) satellite observations.

geostationary↗

NASA NTRS2017

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

GOES-1↗

NASA NTRS2018

Coronal Imaging with the Solar Ultraviolet Imager

Coronal imaging capabilities of the Solar UltraViolet Imager (SUVI) on the Geostationary Operational Environmental Satellite-16 (GOES-16) are investigated. Launched in 2016, GOES-16 is stationed at 75.2 deg W longitude and accommodates both terrestrial and space weather instruments. SUVI is one of two instruments observing the Sun, providing solar images in six extreme ultraviolet (EUV) wavelengths: 94A, 131A, 171A, 195A, 284A and 304A. Mounted on a two axis gimballed Sun Pointed Platform (SPP), its 53 arc minute square field of view is nominally Sun-centered to provide high dynamic range images in all six wavelengths every four minutes. Analyses of on-orbit data indicated that SUVI had sufficient dynamic range and sensitivity in the 171A and 195A wavelengths to image the corona to the largest heights above the Sun to date while simultaneously imaging the Sun. The 1.6 megapixel Charge Coupled Device (CCD) detector enables the capture of the fine features of the corona. On-orbit calibration tests requiring movement of the SUVI line-of-sight off the Sun, demonstrated that the resulting dynamic disturbance didn't adversely impact the earth-pointing instrument performance. Combining these factors, we conducted a test to assess the performance of SUVI as an EUV extremely wide-field coronal imager by off-pointing the SPP around the Sun and synthesizing an image that's about 4 times the diameter of the Sun. The capability to perform coronal imaging at these two wavelengths is demonstrated by off-pointing the SUVI boresight to nine locations around the Sun and generating a composite coronal image that extends to approx. 4 solar radii. For this purpose, we exploited the satellite's ability to provide stable, fine pointing, even during the slews. Various exposure durations were considered during the first trial and a composite coronal image is obtained for each wavelength. The results are promising. The off-pointing locations and the exposure duration are being refined for the subsequent trials. The routine use of an ultraviolet solar imager as both an imager and a EUV coronagraph would be the first of its kind and is an exciting development. The on-orbit solar coronagraphs to date provide white light images with fields of view extending from 4-15 solar radii. Earlier, Seaton, et al investigated the evolution of the corona in EUV near the solar surface with the data from the Sun Watcher with Active Pixels and Image Processing (SWAP) EUV solar telescope on board the Project for On-Board Autonomy 2 (PROBA2). The presence of corona in EUV even to 4 solar radii is not known. The results of these experiments will begin to fill this void.

Tadikonda, Sivakumara K.↗

NASA NTRS2017

Identifying Stratospheric Air Intrusions and Associated Hurricane-Force Wind Events over the North Pacific Ocean

Motivation: Ocean data is sparse: reliance on satellite imagery for marine forecasting; Ocean Prediction Center (OPC) –“mariner’s weather lifeline”. Responsible for: Pacific, Atlantic, Pacific Alaska surface analyses –24, 48, 96 hrs.; Wind & wave analyses –24, 48, 96 hrs.; Issue warnings, make decisions, Geostationary Operational Environmental Satellite –R Series (now GOES-16), Compared to the old GOES: 3 times spectral resolution, 4 times spatial resolution, 5 times faster coverage; Comparable to Japanese Meteorological Agency’s Himawari-8, used a lot throughout this research. Research Question: How can integrating satellite data imagery and derived products help forecasters improve prognosis of rapid cyclogenesis and hurricane-force wind events? Phase I –Identifying stratospheric air intrusions: Water Vapor –6.2, 6.9, 7.3 micron channels; Airmass RGB Product; AIRS, IASI, NUCAPS total column ozone and ozone anomaly; ASCAT (A/B) and AMSR-2 wind data.

Ozone↗

NASA NTRS2017

The Extratropical Transition of Tropical Storm Cindy From a GLM, ISS LIS and GPM Perspective

The distribution of lightning with respect to tropical convective precipitation systems has been well established in previous studies and more recently by the successful Tropical Rainfall Measuring Mission (TRMM). However, TRMM did not provide information about precipitation features poleward of +/-38 deg latitude. Hence we focus on the evolution of lightning within extra-tropical cyclones traversing the mid-latitudes, especially its oceans. To facilitate such studies, lightning data from the Geostationary Lightning Mapper (GLM) onboard GOES-16 was combined with precipitation features obtained from the Global Precipitation Measurement (GPM) mission constellation of satellites.

Precipitation↗

NASA NTRS2018

Flash Optical Energy from the Geostationary Lightning Mapper

The Geostationary Operational Environmental Satellite -16 (GOES-16) Geostationary Lightning Mapper (GLM) is evaluated for many months during the Post Launch Product Test (PLPT) phase in order to ensure that optimal products are available for both the operational forecasting and broader scientific research communities. The emphasis of the PLPT phase is to validate the GLM performance (i.e., lightning flash detection efficiency, geolocation and time-stamp accuracy) using an extensive network of independent ground-based, in-situ, and space-based reference lightning detection systems. However, another essential aspect of the PLPT phase is to obtain benchmarks of the GLM lightning optical amplitude, so that any long-term degradation in the nadir-staring GLM camera system can be realized and quantitatively assessed. This is accomplished in a straight-forward manner by collecting a very large sample of lightning flashes across many geographical regions in the GLM field-of-view so that statistically meaningful benchmarks of lightning optical amplitude (i.e., optical energy in units of femto-joules per flash) are obtained. The benchmarking is particularly important to follow-on studies that will attempt to incorporate the flash optical energy product into new derived products (e.g., energy-weighted lightning "jump" warning algorithms, and lightning nitrogen oxides production estimates).

Lightning↗

NASA NTRS2017

GEO-LEO Reflective Band Inter-Comparison with BRDF and Atmospheric Scattering Corrections

The inter-comparison of the reflective solar bands (RSB) between the instruments onboard a geostationary orbit satellite and a low Earth orbit satellite is very helpful in assessing their calibration consistency. Himawari-8 was launched 7 October 2014 and GOES-R was launched on 19 November 2016. Unlike previous GOES instruments, the Advanced Himawari Imager (AHI) on Himawari-8 and the Advanced Baseline Imager (ABI) on GOES-R have onboard calibrators for the RSB. Independent assessment of calibration is nonetheless important to enhance their product quality. MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) can provide good references for sensor calibration. In this work, the inter-comparison between AHI and VIIRS is performed over a pseudo-invariant target. The use of stable and uniform calibration sites provides comparison with accurate adjustment for band spectral difference, reduction of impact from pixel mismatching, and consistency of BRDF (Bidirectional Reflectance Distribution Function) and atmospheric correction. The site used is the Strzelecki Desert in Australia. Due to the difference in solar and view angles, two corrections must be applied in order to compare the measurements. The first is the atmospheric scattering correction applied to the top of atmosphere reflectance measurements. The second correction is applied to correct the BRDF effect. The atmospheric correction is performed using a vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) model and the BRDF correction is performed using a semi-empirical model. Our results show that AHI band 1 (0.47 microns) has a good agreement with VIIRS band M3 within 0.15 percent. AHI band 5 (1.61 microns) shows the largest difference (5.09 percent) with VIIRS band M10, while AHI band 5 shows the least difference (1.87 percent) in comparison with VIIRS band I3. The methods developed in this work can also be directly applied to assess GOES-16/ABI (Geostationary Operational Environment Satellite16 / Advanced Baseline Imager) calibration consistency, a topic we will address in the future.

The inter-comparison of the reflective solar bands↗

NASA NTRS2017

Generating Land Surface Reflectance for the New Generation of Geostationary Satellite Sensors with the MAIAC Algorithm

The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor land surface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of- atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked (“remembered”) over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance products. Furthermore, the geostationary results satisfactorily capture the movement of clouds and variations in atmospheric dust/aerosol concentrations, suggesting that high quality land surface and vegetation datasets from the advanced geostationary sensors can help complement and improve the corresponding EOS products.

geostationary satellite sensors↗

NASA NTRSDate not supplied

Advancements in Blowing Dust Detection at Night via Machine Learning

This presentation introduces operational users to a machine-learning based Dust Probability product developed by the NASA SPoRT program for the application of detecting and monitoring blowing dust plumes at night. Advances in earth observing satellites has improved monitoring and detection of dust both day and night through derived imagery such as the Dust RGB. However, limitations of the RGB at night result in less contrast between dust and land surface features, as seen by the user. A Machine Learning (ML) model has been developed and applied to GOES-16 ABI to overcome this limitation and improve nighttime dust detection. The ML capability is a subset of Artificial Intelligence methods. In this case the Dust ML model was developed using a simple Random Forest (RF) model, typically used to solve classification challenges (or to provide regression type output). The goal was to leverage the strengths of the RF model to learn how to identify blowing dust, and hence, overcome the limitation of a user trying to detect blowing dust within the satellite imagery by eye alone. A brief description of the ML model development will be provided. However, the focus of the presentation will be on the initial user feedback from the assessment of this tool for the 2022 blowing dust events of March through April. During this time several users across the U.S. Southwest collaborated to apply this Dust ML product at night as a complement to the existing Dust RGB in order to determine if it provided greater operational efficiency and value.

Machine Learning↗

NASA NTRS2017

An Overview of the Design and Development of the Geostationary Operational Environmental Satellite R-Series (GOES-R) Space Segment

The first of the National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite R-series (GOES-R) satellites was launched in November 2016. GOES-R has been developed by NOAA in partnership with the National Aeronautics and Space Administration (NASA). The satellite represents a quantum leap in the state of the art for geostationary weather satellites by providing data from a suite of six new instruments. All instruments were developed expressly for this mission, and include two Earth-observing instruments (the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM)), two solar-viewing instruments (Solar Ultraviolet Imager (SUVI) and Extreme ultraviolet and X-ray Irradiance Sensors (EXIS)) and two in situ instruments (Space Environment In-Situ Suite (SEISS) and a magnetometer pair). In addition to hosting the instruments, GOES-R also accommodates several communication packages designed to collect and relay data for weather forecasting and emergency management. Accommodating the six instruments and four communication payloads imposed challenging and competing constraints on the satellite, including requirements for extremely stable earth and solar pointing, high-speed and nearly error-free instrument data transmission, and a very quiet electromagnetic background. To meet mission needs, GOES-R employed several technological innovations, including low-thrust rocket engines that allow instrument observations to continue during maneuvers, and the first civilian use of Global Positioning System-based orbit determination in geostationary orbit. This paper will provide a brief overview of the GOES-R satellite and its instruments as well as the developmental challenges involved in accommodating the instruments and communications payloads.

SUVI↗

NASA NTRS2017

An Overview of the Design and Development of the GOES R-Series Space Segment

GOES-16↗