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NASA NTRS2019

GOES-16 and GOES-17 ABI INR Assessment

The first two satellites of the US Geostationary Operational Environmental Satellite R-Series (GOES-R) were launched on November 19, 2016 and March 1, 2018 respectively. GOES-16 officially became GOES East on December 18, 2017, and the designation of GOES-17 as GOES West occurred on February 12 2019. The Advanced Baseline Imager (ABI) is the primary instrument on GOES-16 and GOES-17 for imaging Earth’s surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. The Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) was developed to assess INR performance of GOES-R series ABI images. In this paper, we first describe the assessment of IPATS algorithm accuracy. Next, we present the relationship between view zenith angle (VZA) and the quality of the IPATS measurements. Lastly, we present GOES-16 and GOES-17 navigation (NAV) assessments results from flight data spanning from the start of INR assessment to June 2019. The results show a) IPATS “stair step” measurement error is less or equal to 0.06 ABI pixel with IPATS baseline configuration; b) VZA is an effective filter to exclude outliers of the measurements; and c) ABI INR for both satellites has improved over time as post-launch tests (PLT) were performed and corrections applied. This paper also shows that the post-launch INR tuning of GOES-17 was much shorter than GOES-16.

GOES-17↗
NASA NTRSDate not supplied

Validation of GOES-17 ABI Reflective Channels Performance: Salar De Uyuni 2018 Field Campaign Results

Validation results from a reflectance-based field campaign at the Salar de Uyuni in Bolivia (September 2018) are presented for GOES-17 and GOES-16 Advanced Baseline Imagers (ABI) reflective channels. The in situ measurements were used to characterize the surface reflectance and the atmosphere in order to constrain a radiative transfer model and predict the reflectance at the top of the atmosphere (TOA), which was then compared to the ABI measurements. The field campaign provides TOA reflectance estimates over several days, allowing assessment of the calibration accuracy and stability of channels 1, 2, 3, 5 and 6 for GOES-17 andGOES-16 ABI. Channel 1 of GOES-17 ABI shows -5.5% bias in comparison to the ground-based predicted TOA. Over 6% bias in GOES-17 B2 was confirmed. A comparison to NOAA-20 VIIRS was also carried on during a near nadir overpass.

validation↗
NASA NTRS2019

GOES-17 Magnetometer On-Orbit Calibration Design and Results

The Geostationary Operational Environmental Satellite-R (GOES-R) is a series of four NOAA weather satellites. Each spacecraft flies a Magnetometer instrument which includes two boom mounted fluxgate sensors located 6.3 and 8.5 meters from the spacecraft. Sensor readings are corrupted by both internal (bias and scale factor) and external (spacecraft emitted fields and misalignments) error sources. In order to estimate the bias, an on-orbit calibration is performed, slewing the spacecraft about multiple axes and then performing a fit of the data to determine zero offset and misalignment terms. The first satellite in the series, GOES-16 was launched in November 2016. Its magnetometer calibration maneuver was found to be inadequate providing insufficient rotation about orthogonal axes. The uncertainty in the estimated biases was found to be in the 3-5 nanotesla range, exceeding the magnetometer accuracy requirement of 1.0 nanotesla. Based on lessons learned, the maneuver was redesigned to provide multiple 360 degree rotations about two orthogonal axes, providing optimal visibility into bias and misalignment. Varying ambient fields over the duration of the calibration maneuvers, corrupts the ability to accurately determine parameters of interest. To address this issue, rather than assuming a constant field for the duration of the maneuver, a non-linear least squares filter was employed that uses a series of spline functions to better fit the ambient field. The number of splines can be varied to minimize the least squares filter residuals. Simulations were performed using 360 days of GOES-16 data to bound the uncertainty in the biases for given residuals of the fit to the ambient field. Following the launch of the second satellite in the series, GOES-17 in March 2018, the new calibration maneuver and enhanced bias estimation algorithm were exercised twice. Based on the residuals of the fit, the uncertainty of the estimated zero offset was reduced by an order of magnitude from 3-5 nanotesla to approximately 0.4 nanotesla per axis. This paper provides details of the maneuver design and the innovative algorithm used to estimate the GOES-17 biases and misalignments.

GOES-17↗
NASA NTRS2019

In-Flight Line-Of-Sight Pointing Performance for the GOES-16 and GOES-17 Spacecraft

The Geostationary Operational Environmental Satellite-R program (GOES-R) has launched two of the next generation geostationary weather satellites, both of which are now fully operational. GOES-16 launched in November 2016, and GOES-17 launched in March 2017. In this paper, we present the pointing and pointing stability results of the two spacecraft, with specific focus on aspects of the design related to mitigating jitter. The flight instrument suite includes 6 seismic accelerometers sampled at approximately 2 kilohertz, allowing in-flight verification of pointing stability and comparison back to simulation predictions. This paper compares the observed flight results with the simulation predictions for acceleration and shock response spectrum (SRS) for various operational scenarios and instrument observation modes. Passive isolation of both the reaction wheels and the payload deck have proved to be effective in reducing jitter responses. Active Vibration Damping (AVD) of flexible-body modes attenuates the low frequency motion of the vehicle appendages, improving the low-frequency pointing performance. Knowledge of the instrument scan mirror motion is fed forward to the reaction wheel control, reducing disturbances on the spacecraft bus. Attitude knowledge and rate data are provided to the primary Earth-observing instrument with an accuracy defined by the Integrated Rate Error (IRE) requirements. The data are used to adjust instrument scanning. As we show in this paper, the in-flight performance of the GN&C (Guidance, Navigation and Control) design provides the necessary capabilities to achieve the demanding GOES-R mission objectives while its robustness enabled the simultaneous operation of the Advanced Baseline Imager (ABI) prime and redundant cryocoolers (CCs) to resolve an in-flight cooling anomaly on GOES-17.

Line-of-Sight↗
NASA NTRS2019

GOES-17 Advanced Baseline Imager Performance Recovery Summary

The 17th Geostationary Operational Environmental Satellite (GOES-17) was launched on 1 March 2018. The Advanced Baseline Imager (ABI) is the primary instrument on the GOES-R series for weather and environmental monitoring. The GOES-17 ABI (flight model 2) experienced a degradation in its thermal system that limits ABI's ability to shed solar heat load. This limitation resulted in significant reduction in performance after initial turn on with only 3 of 16 spectral channels expected to be available for much of the year. A combined government/vendor team was tasked with optimizing the operation of ABI to recapture as much performance as possible. By modifying the operational configuration and sensor parameters, the team was able to regain over 97% imaging capability.This was accomplished by taking advantage of the considerably flexible nature of ABI's design to adapt its configuration to the new reality and improve capabilities for many of ABI's subsystems. The significant differences in operational configuration, sensor parameter optimization, and algorithm optimization will be discussed as well as their impact on performance and data availability.

Advanced Baseline Imager↗
NASA NTRS2021

Improving the CERES SYN Cloud and Flux Products by Identifying GOES-17 Scan Anomalies Using a Convolutional Neural Network

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project relies on top-of-atmosphere (TOA) broadband fluxes derived from geostationary (GEO) satellite imagery to account for the diurnal flux variations between the CERES observation intervals, and thereby produce a synoptic gridded (SYN1deg) product based on continuous temporal observations. Consistent broadband flux derivation depends on accurate radiative property measurements and cloud retrievals, which largely determine the radiance-to-flux conversion process. Therefore, it is important to ensure a high quality of cloud property input in order to maintain a reliable broadband flux record. In Edition 4 of the CERES SYN1deg product, a robust automated image anomaly detection algorithm based on inter-line and inter-pixel differences, spatial variance, and 2-D Fourier analysis has been successful in identifying imagery with linear artifacts, but the line-by-line inspection and cleaning process must still be performed by a human. Therefore, further automation of this quality assurance process is warranted, especially considering the excessive amount of additional cleaning necessitated by the GOES-17 Advance Baseline Imager (ABI) cooling system anomaly. As such, this article highlights advancement of the CERES GEO image artifact cleaning approach based on a convolutional neural network (CNN) for classification of bad scanlines. Once trained, the CNN approach is a computationally inexpensive means to ensure greater consistency in cloud retrievals, and therefore broadband flux derivation, based on GOES-17 measurements.

Benjamin Scarino↗
NASA NTRS2019

GOES-16 and GOES-17 ABI INR Assessment

The Image navigation and registration (INR) Performance Assessment Tool Set (IPATS) measurement accuracy, within 0.06 pixels, is sufficient for INR assessment. IPATS is not a static system. Additional filters and/or sub-procedures were developed when the demand emerged, e.g. the development of Short Term AbNormal Dectection (STAND) and View Zenith Angle (VZA) filters in post-launch test (PLT) of GOES-16 and GOES-17 respectively. NAV INR accuracy improved with updates and tuning in PLT. Currently, NAV errors are about 1-2 urad for all assessed channels of both ABIs. IPATS NAV assessments will continue to provide feedback for tuning the navigation algorithms and parameters in future updates and future GOES-R series ABIs.

GOES-17↗
NASA NTRS2020

GOES-17 Independent Review Team Investigation Results

The GOES-17 (Geostationary Operational Environmental Satellite) was launched March 1, 2018. It was the second of the GOES R class satellites to be launched - following GOES 16 in November 2016. Both satellites contain Advanced Baseline Imagers (ABIs) that provide high spatial and temporal resolution imagery of the Earth at visible and infrared wavelengths. The Japanese Himawari 8 (launched in October 2014), Himawari 9 (launched in November 2016) and South Korean GEO-KOMPSAT-2A (launched in December 2018) have imager payloads and thermal control systems identical to the GOES R class. All these satellites serve as geostationary weather satellites.

Eugene Ungar↗
NASA NTRS2020

California & Oregon Ecological Forecasting: Detecting and Forecasting Fog Occurrence, Frequency, and Change to Support Coast Redwood (Sequoia sempervirens) Habitat Assessments

Fog and low clouds play an important role in providing moisture to coastal ecosystems. Coast redwood (Sequoia sempervirens) forests are currently distributed along a narrow strip of coastline in California and Oregon and rely on the presence of marine fog for moisture availability during the dry season (June-October). Recent time series analyses presented an uncertain future of fog frequency; however, a decline in fog presence may have adverse effects on the coast redwood habitat. To support Save the Redwoods League, a non-profit organization dedicated to coast redwood forest management, the team analyzed hourly fog data from the Geospatial Operational Environmental Satellite 17 (GOES-17) Advanced Baseline Imager (ABI) and daily cloud cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. To explore present day fog longevity, GOES-17 was utilized to map the number of fog hours per day for the 2019 and 2020 dry seasons. The MODIS cloud flag was used to map the presence or absence of daily fog, which was summarized to create a monthly fog frequency dataset and identify trends in fog presence between 2000-2020. Both datasets were used as inputs into the random forest machine learning algorithm to identify climatic drivers of fog presence and longevity over the landscape. The present-day models suggested that daily temperature difference is a driving force behind fog presence and longevity. Trends in fog presence from 2000-2020 indicated great interannual variability. Finally, fog presence was modeled under a 2080 climate projection to shed light on the future of fog presence under a projected warmer climate. Model results projected an overall decline in fog presence during the dry season in the 2080s. Decreased fog presence as a result of increased temperature difference under a warmer climate remains to be a topic of investigation as to the impact on future redwood habitat suitability.

DEVELOP Project Summary↗
NASA NTRS2020

California & Oregon Ecological Forecasting: Detecting and Forecasting Fog Occurrence, Frequency, and Change to Inform Coast Redwood (Sequoia sempervirens) Habitat Assessments

Fog and low clouds play an important role in providing moisture to coastal ecosystems. Coast redwood (Sequoia sempervirens) forests are currently distributed along a narrow strip of coastline in California and Oregon and rely on the presence of marine fog for moisture availability during the dry season (June-October). Recent time series analyses presented an uncertain future of fog frequency; however, a decline in fog presence may have adverse effects on the coast redwood habitat. To complement ongoing work by Save the Redwoods League, a non-profit organization dedicated to coast redwood forest management, the team analyzed hourly fog data from the Geospatial Operational Environmental Satellite 17 (GOES-17) Advanced Baseline Imager (ABI) and daily cloud cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. To explore present day fog longevity, GOES-17 was utilized to map the number of fog hours per day for the 2019 and 2020 dry seasons. The MODIS cloud flag was used to map the presence or absence of daily fog, which was summarized to create a monthly fog frequency dataset and identify trends in fog presence between 2000-2020. Both datasets were used as inputs into the random forest machine learning algorithm to identify climatic drivers of fog presence and longevity over the landscape. The present-day models suggested that daily temperature difference is a driving force behind fog presence and longevity. Trends in fog presence from 2000-2020 indicated great interannual variability. Finally, fog presence was modeled under a 2080 climate projection to shed light on the future of fog presence under a projected warmer climate. Model results projected an overall decline in fog presence during the dry season in the 2080s. Decreased fog presence as a result of increased temperature difference under a warmer climate remains to be a topic of investigation as to the impact on future redwood habitat suitability.

DEVELOP Technical Paper↗
NASA NTRS2019

GOES-R Spacecraft Verification and Validation Compared with Flight Results

The Geostationary Operational Environmental Satellite, R-Series (GOES-R) represents a dramatic improvement in GEO (Geostationary Earth Orbit) weather observation capabilities over the previous generation. To provide these new capabilities, GOES-R incorporates a number of new technologies flying for the first time. As with any new spacecraft design, extensive ground testing was performed to validate the vehicle performance. In this paper, we present several successes and several lessons-learned from the GOES-R verification and validation (V&V) efforts. Included are the Dynamic Interaction Test (DIT) results for jitter assessment, and comparison to flight results. Also included are the effects of thermally-induced alignment perturbations, along with post-launch mitigations. Finally, we discuss unexpected GOES-17 gyro performance, which caused a Safe Mode entry shortly after launch. V&V mitigations are presented, which will be used for the next two GOES-R vehicles.

GOES-17↗
NASA NTRS2018

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

As part of the geolocation accuracy assessment of lightning flashes detected by the Geostationary Lightning Mapper (GLM) on the GOES-16 and GOES-17 satellites (Geostationary Operational Environmental Satellite), two satellite laser ranging stations employed laser beacon systems to generate transient light pulses that simulate natural lightning around 777.4 nm to validate the pre-launch spec of 5 km. The pulse width, repetition rate, wavelength, and power of the laser-pulses were configured to produce sufficient instrument response to be detected as synthetic lightning events by the GLM instrument. During the testing period from April 2017 to January 2018, the laser systems illuminated the GOES-16 satellite to observe diurnal variation of the GLM system response, with particular emphasis on geolocation accuracy. The final GOES-16 laser beacon tests, which used the latest updates of the geolocation algorithms implemented by the GOES-R Ground Segment, showed the offsets between the GLM geolocated location and the known laser locations were within 5 km.

Lightning↗
NASA NTRS2019

Analysis and Applications of Water Vapor-Derived Multispectral Composites for Geostationary Satellites

Analysis of multispectral (red-green-blue, RGB) satellite image composites can be used to improve understanding of thermodynamic and / or dynamic features associated with the development of significant weather events (cyclones, hurricanes, intense convection, turbulence, etc.) The enhanced water vapor imaging capabilities of the Advanced Baseline Imager on GOES-16 and GOES-17 satellites provide a unique opportunity to demonstrate this capability through a comparison of the Air Mass (AM) and Differential Water Vapor (DWV) RGB image products for several case studies.

Berndt, Emily↗
NASA NTRS2019

GOES-R Solar UltraViolet Imager Extended Coronal Imaging

GOES-16 and GOES-17 each hosts a Solar UltraViolet Imager (SUVI) that images the Sun in six extreme ultraviolet (EUV) wavelengths: 9.4 nm, 13.1nm, 17.1nm, 19.5nm, 28.4nm, and 30.4nm. The SUVI is nominally Sun-pointed and has a four-minute imaging sequence covering all the channels and meeting the dynamic range requirements. Based on the SUVI capabilities observed on-orbit, a campaign to image the extended solar corona was undertaken in 2018. This was performed by off-pointing the SUVI line-of-sight to the left and right of the Sun and producing a composite image by stitching together the off-pointed and the Sun-centered images. The imaging area in the composite is about three times the nominal image area in the East-West direction (about 5 RSun versus 1.8 RSun for nominal images). The campaign was conducted in February (4 hours), June (72 hours), and August-September of 2018 (5 weeks). Results from the campaign indicated the presence of solar corona to three solar radii, even in the quiet Sun part of the solar cycle. NOAA can operationalize this concept by tasking the SUVI on one of GOES-E and GOES-W. Such long-term operation will provide data that is needed to establish the much needed connectivity between the EUV and white light coronagraph data for the CME events.

Extended Coronal↗
NASA NTRS2019

Observations from the GOES-R Solar UltraViolet Imager Extended Coronal Imaging Campaign

Solar corona in 17.1nm and 19.5nmwavelengths up to three solar radii from Sun center was observed by the Solar UltraViolet Imager (SUVI) on the Geostationary Operational Environmental Satellite (GOES) –16 and GOES-17. The nominally Sun-pointed SUVI was off-pointed to the left and to the right of the Sun center at a regular cadence and a composite Extended Coronal Imaging (ECI) frame was created. The imaging area in the composite is about three times the nominal image area in the East-West direction (about 5*R(sub Sun) versus 1.6*R(sub Sun) for nominal images). The campaign was conducted in February (4 hours), June (72 hours), and August-September of 2018 (5 weeks). Limited solar CME activity during the 5-week campaign was observed in both the SUVI and LASCO C2 imagers. Some of the observations during this campaign include structures up to a few solar radii off the solar limb, and interesting coronal activity –both on and off the solar disk. They are presented here.

Tadikonda, Sivakumar K.↗
NASA NTRS2020

Using GLM Flash Density, Flash Area, and Flash Energy to Diagnose Tropical Cyclone Structure and Intensification

Increased lightning in tropical cyclones (TCs) is typically associated with intensification, but significant lightning outbreaks are also observed in weakening storms. The total number of lightning flashes in a TC is not always a reliable indicator of TC intensity evolution. Issues with the range and detection efficiency of ground-based networks, particularly for intracloud lightning. Physical processes such as vertical wind shear can intensify asymmetric convection while also weakening the TC. The commissioning of the Geostationary Lightning Mapper (GLM) aboard GOES-16 and GOES-17 marked, for the first time, the presence of an operational lightning detector in geostationary orbit. In addition to flash density (the number of flashes per unit area per unit time), GLM also provides continuous observations of flash area and total optical energy.

Duran, Patrick↗
NASA NTRSDate not supplied

Global Positioning System Constellation Modernization Impact on Sidelobe Capable GPS Receivers in Geostationary Orbit

This paper provides on-orbit insight into Global Positioning System Receiver (GPSR) performance at a geostationary orbit (GEO) and contrasts that performance regarding the modernized and heritage GPS constellation currently operational. The subject matter of this paper falls under the following topics a) heritage and modern GPS transmit pattern comparison, b) GOES-R GPSR acquisition and tracking characterization regarding the first four operational GPS III vehicles, and c)relevant signal requirements as it pertains to GEO GPSR facilitation. The GPSR described herein is onboard the GOES-R series satellites. GOES-R (Geostationary Operational Environmental Satellite-R Series) is the first in a 4-part series of new weather satellites set to replace and upgrade the older GOES constellation. Two GOES-R have been launched to date, GOES-16 and GOES-17, the data presented in this paper are from both vehicles over common time spans. The GPSR on board this geostationary weather satellite is a mission critical, enabling technology which has been both tested on the ground and evaluated on-orbit to verify its effectivity[1]. This is a completely new system design consisting of a unique L1 GEO antenna, low-noise amplifier (LNA) assembly and a 12-channel GPSR capable of tracking the edge of the main beam and the sidelobes of the GPS L1 signal. Any satellite intending to maximize GPS navigation performance at GEO will need to implement a GPSR system that tracks sidelobes. GOES-R is the first civilian operational satellite to utilize GPS sidelobes for navigation at GEO, which is the key factor in the systems highly accurate, robust and continuous navigation solution. However, this also renders a distinct sensitivity to changes in the GPS transmit signal pattern in the sidelobe regime as a result of the new GPS III constellation modernization. This paper presents results showing that the GOES-RGPSR solution, given GPS constellation modernization to GPS III, although impacted slightly in received C/N0 at certain geometries, will continue to meet all performance requirements tracking up to 12 satellites and achieving excellent carrier-to-noise spectral density (C/N0).

GPS↗
NASA NTRSDate not supplied

NASA GLOBE CLOUD GAZE: Creating Data Quality Flags for Citizen Science Cloud Observations Matched to NASA Satellite Data

The GLOBE Program, NASA’s largest and longest lasting citizen science program about the Earth, has been collecting cloud observations matched to multiple satellite data daily. The program’s cloud protocol is historically the most popular protocol as your eyes are the only instruments you need to collect observations of the sky. This dataset includes over 3,300,000 cloud observations with variables like total cloud cover, cloud type and opacity that are collocated to the nearest overpass times of geostationary satellites (GOES-15, GOES-16, GOES-17, Meteosat-8, Meteosat-11, or Himawari-8), or to Clouds and the Earth’s Radiant Energy System (CERES) instruments onboard Aqua and Terra, or the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. In order to increase the usability of this dataset, the Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement (CLOUD GAZE) has been developed to generate data quality flags of these ground-up and top-down perspectives of sky and clouds. Recently funded through NASA’s Citizen Science for Earth Systems Program, CLOUD GAZE has partnered with the Zooniverse online platform to obtain reference data and image tagging of sky photographs collected through The GLOBE Program’s clouds protocol. This paper will present the GLOBE Clouds dataset matched to NASA satellite data, integration of CLOUD GAZE to develop data quality flags, and research applications of the dataset (includes ground-up and top-down perspective comparisons, ground observations of dust storms and smoke plumes, and cloud observations in polar regions). The paper will also present on techniques and recommendations for classroom use and for community engagement, particularly for those looking to online resources.

Marilé Colón Robles↗