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All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗
NASA NTRS2011

GOES-R Proving Ground Activities at the NASA Short-Term Prediction Research and Transition (SPoRT) Center

SPoRT is actively involved in GOES-R Proving Ground activities in a number of ways: (1) Applying the paradigm of product development, user training, and interaction to foster interaction with end users at NOAA forecast offices national centers. (2) Providing unique capabilities in collaboration with other GOES-R Proving Ground partners (a) Hybrid GOES-MODIS imagery (b) Pseudo-GLM via regional lightning mapping arrays (c) Developing new RGB imagery from EUMETSAT guidelines

Molthan, Andrew↗
NASA NTRS2012

Comparison Between GOES-12 Overshooting-Top Detections, WSR-88D Radar Reflectivity, and Severe Storm Reports

Studies have found that convective storms with overshooting-top (OT) signatures in weather satellite imagery are often associated with hazardous weather, such as heavy rainfall, tornadoes, damaging winds, and large hail. An objective satellite-based OT detection product has been developed using 11-micrometer infrared window (IRW) channel brightness temperatures (BTs) for the upcoming R series of the Geostationary Operational Environmental Satellite (GOES-R) Advanced Baseline Imager. In this study, this method is applied to GOES-12 IRW data and the OT detections are compared with radar data, severe storm reports, and severe weather warnings over the eastern United States. The goals of this study are to 1) improve forecaster understanding of satellite OT signatures relative to commonly available radar products, 2) assess OT detection product accuracy, and 3) evaluate the utility of an OT detection product for diagnosing hazardous convective storms. The coevolution of radar-derived products and satellite OT signatures indicates that an OT often corresponds with the highest radar echo top and reflectivity maximum aloft. Validation of OT detections relative to composite reflectivity indicates an algorithm false-alarm ratio of 16%, with OTs within the coldest IRW BT range (less than 200 K) being the most accurate. A significant IRW BT minimum typically present with an OT is more often associated with heavy precipitation than a region with a spatially uniform BT. Severe weather was often associated with OT detections during the warm season (April September) and over the southern United States. The severe weather to OT relationship increased by 15% when GOES operated in rapid-scan mode, showing the importance of high temporal resolution for observing and detecting rapidly evolving cloud-top features. Comparison of the earliest OT detection associated with a severe weather report showed that 75% of the cases occur before severe weather and that 42% of collocated severe weather reports had either an OT detected before a severe weather warning or no warning issued at all. The relationships between satellite OT signatures, severe weather, and heavy rainfall shown in this paper suggest that 1) when an OT is detected, the particular storm is likely producing heavy rainfall and/or possibly severe weather; 2) an objective OT detection product can be used to increase situational awareness and forecaster confidence that a given storm is severe; and 3) this product may be particularly useful in regions with insufficient radar coverage.

Dworak, Richard↗
NASA NTRS2013

Phase Change Material for Temperature Control of Imager or Sounder on GOES Type Satellites in GEO

An imager or sounder on satellites, such as the Geostationary Operational Environmental Satellite (GOES), in geostationary orbit (GEO) has a scan mirror and motor in the scan cavity. The GEO orbit is 24 hours long. During part of the orbit, direct sunlight enters the scan aperture and adds heat to components in the scan cavity. Solar heating also increases the scan motor temperature. Overheating of the scan motor could reduce its reliability. For GOES-N to P, a radiator with a thermal louver rejects the solar heat absorbed to keep the scan cavity cool. A sunshield shields the radiator/louver from the Sun. This innovation uses phase change material (PCM) in the scan cavity to maintain the temperature stability of the scan mirror and motor. When sunlight enters the scan aperture, solar heating causes the PCM to melt. When sunlight stops entering the scan aperture, the PCM releases the thermal energy stored to keep the components in the scan cavity warm. It reduces the heater power required to make up the heat lost by radiation to space through the aperture. This is a major advantage when compared to a radiator/ louver. PCM is compact because it has a high solid-to-liquid enthalpy. Also, it could be spread out in the scan cavity. This is another advantage. Paraffin wax is a good PCM candidate, with high solid-to-liquid enthalpy, which is about 225 kJ/kg. For GOES-N to P, a radiator with a louver rejects the solar heat that enters the aperture to keep the scan cavity cool. For the remainder of the orbit, sunlight does not enter the scan aperture. However, the radiator/louver continues radiating heat to space because the louver effective emittance is about 0.12, even if the louver is fully closed. This requires makeup heater power to maintain the temperature within the stability range.

Choi, Michael↗
NASA NTRS2013

Sao Paulo Lightning Mapping Array (SP-LMA): Network Assessment and Analyses for Intercomparison Studies and GOES-R Proxy Activities

A 12 station Lightning Mapping Array (LMA) network was deployed during October 2011in the vicinity of São Paulo, Brazil (SP-LMA) to contribute total lightning measurements to an international field campaign [CHUVA - Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)]. The SP-LMA was operational from November 2011 through March 2012. Sensor spacing was on the order of 15-30 km, with a network diameter on the order of 40-50km. The SP-LMA provides good 3-D lightning mapping out to150 km from the network center, with 2-D coverage considerably farther. In addition to supporting CHUVA science/mission objectives, the SP-LMA is supporting the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), on NOAA's Geostationary Operational Environmental Satellite-R (GOES-R: scheduled for a 2015 launch). These proxy data will be used to develop and validate operational algorithms so that they will be ready to use on "day1" following the GOES-R launch. The SP-LMA data also will be intercompared with lightning observations from other deployed lightning networks to advance our understanding of the capabilities/contributions of each of these networks toward GLM proxy and validation activities. This paper addresses the network assessment and analyses for intercomparison studies and GOES-R proxy activities

Blakeslee, R. J.↗
NASA NTRS2014

Sao Paulo Lightning Mapping Array (SP-LMA): Network Assessment and Analyses for Intercomparison Studies and GOES-R Proxy Activities

A 12 station Lightning Mapping Array (LMA) network was deployed during October 2011 in the vicinity of Sao Paulo, Brazil (SP-LMA) to contribute total lightning measurements to an international field campaign [CHUVA - Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)]. The SP-LMA was operational from November 2011 through March 2012 during the Vale do Paraiba campaign. Sensor spacing was on the order of 15-30 km, with a network diameter on the order of 40-50km. The SP-LMA provides good 3-D lightning mapping out to 150 km from the network center, with 2-D coverage considerably farther. In addition to supporting CHUVA science/mission objectives, the SP-LMA is supporting the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), on NOAA's Geostationary Operational Environmental Satellite-R (GOES-R: scheduled for a 2015 launch). These proxy data will be used to develop and validate operational algorithms so that they will be ready to use on "day1" following the GOES-R launch. As the CHUVA Vale do Paraiba campaign opportunity was formulated, a broad community-based interest developed for a comprehensive Lightning Location System (LLS) intercomparison and assessment study, leading to the participation and/or deployment of eight other ground-based networks and the space-based Lightning Imaging Sensor (LIS). The SP-LMA data is being intercompared with lightning observations from other deployed lightning networks to advance our understanding of the capabilities/contributions of each of these networks toward GLM proxy and validation activities. This paper addresses the network assessment including noise reduction criteria, detection efficiency estimates, and statistical and climatological (both temporal and spatially) analyses for intercomparison studies and GOES-R proxy activities.

Bailey, J. C.↗
NASA NTRS2014

The GOES-R Geostationary Lightning Mapper (GLM) and the Global Observing System for Total Lightning

for the existing GOES system currently operating over the Western Hemisphere. New and improved instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved temporal, spatial, and spectral resolution for the next generation Advanced Baseline Imager (ABI). The GLM will map total lightning continuously day and night with near-uniform spatial resolution of 8 km with a product latency of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. The GLM will help address the National Weather Service requirement for total lightning observations globally to support warning decision-making and forecast services. Science and application development along with pre-operational product demonstrations and evaluations at NWS national centers, forecast offices, and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and check-out of GOES-R in 2016. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.

Goodman, Steven J.↗
NASA NTRS2017

The GOES In-Situ Geomagnetism Experiment Reimagined

The Geostationary Operational Environmental Satellites (GOES) carry several instruments each with its own requirements. Because accommodating all of them is difficult, there is interest in putting some on their own smaller platforms. The simplest instruments are the magnetometers. These are small and have low communication, attitude control and cleanliness requirements. Getting on-station and operating in close proximity with another satellite, however, requires significant propulsion capabilities. This paper describes a CubeSat mission that goes from geo-transfer to geostationary orbit and operates next to a GOES spacecraft. Requirements, a notional design and performance predictions are provided.

geomagnetism↗
NASA NTRS2018

On-Orbit Verification of GLM Navigation on GOES-16

The GOES-R flight project has developed the Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) to perform independent INR evaluations of the optical instruments on the GOES-R series spacecraft. In this presentation, we document the development of navigation (NAV) evaluation capabilities within IPATS for the Geostationary Lightning Mapper (GLM). We also discuss the post-processing quality filtering developed for GLM NAV, and present example results for several GLM background image datasets. Initial results suggest that GOES-16 GLM is compliant with navigation requirements.

GLM↗
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 NTRS2019

GOES-16 ABI Solar Reflective Channel Validation for Earth Science Application

This paper presents the validation results of GOES-16 Satellite's Advanced Baseline Imager (ABI) obtained from a reflectance-based field campaign undertaken at the Salar de Uyuni in Bolivia in June 2017. In situ ground measurements are used to characterize the surface reflectance and the atmosphere in order to constrain the radiative transfer code and predict at-sensor reflectance (also referred to as top-of-atmosphere (TOA) reflectance) to compare with concurrent GOES-16 ABI measurements. The five-day field campaign provided repeated TOA reflectance estimates, allowing assessment not only of the calibration accuracy of the ABI reflective channels 1, 2, 3, 5 and 6, but also of its stability over the duration of the campaign. The results show that the accuracy of the ABI reflective channels calibration is within specification for channels 1, 3, 5, and 6 - average biases within 2%; for channel 2 the bias is 5%. The estimated uncertainty on the derived biases is 2–2.4%. Some calibration stability issues were present in the ABI calibration at the time of the campaign: (i) a jump on the order of 2% in channels 1 and 6, coincident with an ABI solar calibration event, reflects an instability of the ABI gains in these channels, and (ii) short-term variability in channels 1 and 2 is due to striping (ABI detector-todetector calibration differences). Continued validation and subsequent reprocessing of ABI reflectance imagery would allow Earth scientists to fully benefit from the high spatial and spectral fidelity of the GOES-16 ABI diurnal measurements at the continental scale.

Joel McCorkel↗
NASA NTRS2018

Evaluation of a High-Resolution Numerical Weather Prediction Model’s Simulated Clouds Using Observations from CloudSat, GOES-13 and in Situ Aircraft

This study aimed to assess tropical cloud properties predicted by Environment and Climate Change Canada’s Global Environmental Multiscale (GEM)modelwhen run with the Milbrandt–Yau double-moment cloud microphysical scheme and one-way nesting that culminated at a (∼300 km)2 inner domain with 0.25 km horizontal grid spacing. The assessment utilized satellite and in situ data collected during the High Ice Water Content (HIWC) and High Altitude Ice Crystals (HAIC) projects for a mesoscale convective system on 16May 2015 over French Guiana.Data fromCloud- Sat’s cloud-profiling radar and GOES-13’s imager were compared to data either simulated directly by GEM or produced by operating on GEM’s cloud data with both the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package (COSP) instrument simulator and a three-dimensional Monte Carlo solar radiative transfer model. In situ observations were made from research aircraft – Canada’s National Research Council Convair-580 and the French SAFIRE Falcon-20 – whose flight paths were aligned with CloudSat’s ground-track. Spatial and temporal shifts of clouds simulated by GEM compared well to GOES-13 imagery. There are, however, differences between simulated and observed amounts of high and low cloud. While GEM did well at predicting ranges of ice-water content (IWC) near 11 km altitude (Falcon-20), it produces too much graupel and snow near 7 km (Convair-580). This produced large differences between CloudSat’s and COSP-generated radar reflectivities and two-way attenuations. On the other hand, CloudSat’s inferred values of IWC agree well with in situ samples at both altitudes. Generally, GEM’s visible reflectances exceeded GOES-13’s on account of having produced too much low-level liquid cloud. It is expected that GEM’s disproportioning of cloud hydrometeors will improve once it includes a better representation of secondary ice production.

Qu, Zhipeng↗
NASA NTRS2020

Reflective Solar Band Striping Mitigation Method for the GOES-R Series Advanced Baseline Imager Using Special Scans

The large focal plane arrays used in the new generation of the Geostationary Operational Environmental Satellites (GOES) advanced baseline imager (ABI) introduce new calibration challenges compared with the heritage GOES imagers. The increased number of detectors allows for increased spatial, spectral, and temporal performance, but as a trade-off, it has an increased risk of image striping. We detail the development of a new postlaunch relative calibration capability for ABI reflective solar bands that utilizes ABI special scans to generate a set of relative gains that can be applied to improve image quality and reduce image striping. Results demonstrate that the method reduces image striping in the ABI solar reflective bands over varying scene content and time, both diurnally and over an extended period. This methodology ensures a calibration strategy that is consistent with heritage approaches yet adapts to the new postlaunch validation challenges presented by the new class of operational imagers in the GOES-R series. The developed approach is ready for operational use, as needed, and can be easily implemented into operations to support the operational production of geostationary imagery of the Earth.

Monica Cook↗
NASA NTRSDate not supplied

An Alternative Explanation for the Great Oxygenation Event (GOE): Weathering of Rocks Containing Minerals With Peroxy Bonds

During the Great Oxygenation Event (GOE) 2.42.35 Gyrsago, Earth experienced a notable shift in its oxidation state. This has been widely attributed to cyanobacteria having “invented” oxygenic photosynthesis. We present a very different view based on the fact thatoxygen is released from rocks during weathering. If so, the GOE must have been caused by an influx of abiogenicO2 into Earth’s surface environment. Minerals forming Earth’s crust contain impurity hydroxyls such as O3SiOH. Pairs of those are known to undergo a common redox conversion, ubiquitously forming peroxydefects plus H 2 : O 3 SiOH-HOSiO 3 ⇌O 3 Si/ OO \SiO 3 + H 2 Being diffusively mobile, most H 2 will be lost, leaving behind bound O as peroxy. The peroxy defects release O 2 during weathering: O 3 Si/ OO \SiO 3 + H 2 O → O 3 SiOH-HOSiO 3 + 1 ⁄ 2 O 2 We suggest that this abiogenically generated O 2 was responsible for the progressive oxidation of the early Earth and that this abiotic process drove the Great Oxygenation Event (GOE).

Great Oxygenation Event↗
DOE OSTI2024

Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama

Abstract Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian et al., 2021, https://doi.org/10.3390/rs13122309 , 2022, https://doi.org/10.1029/2021gl097070 ). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

GOES-16 Data for LASSO-CACTI Overview Paper

GOES-16 L1b satellite radiances have been obtained for the LASSO-CACTI case dates. Specifically, the period in the ARM subset is for select days in the period October 26, 2018 through March 15, 2019. These files were downloaded from Amazon Web Services using the GOES-2-Go library, https://blaylockbk.github.io/goes2go/_build/html/.

{"GOES-16 band 13",radiance,LASSO-CACTI}↗
DOE OSTI2026

Integration of GOES Data for Solar Resource Assessment of the Contiguous United States

The National Solar Radiation Database (NSRDB), produced by the National Laboratory of the Rockies (NLR), provides high-resolution solar resource data for the contiguous United States (CONUS) using Geostationary Operational Environmental Satellite (GOES) East and West observations. This study evaluates the integration of multi-satellite data within the GOES-East/West overlap regions, where conventional longitude-based selection methods often produce an artificial boundary seam. Our results demonstrate that an advanced blending algorithm, which incorporates sun-satellite scattering angles and satellite viewing zenith angles, improves NSRDB accuracy and creates a spatially continuous dataset. Validation against ground-based irradiance measurements reveals reductions in both percentage error (PE) and normalized Root Mean Square Error (nRMSE), particularly in the central United States. The dynamical integration of multi-satellite data provides a robust foundation for more precise modeling of solar resource and improved spatiotemporal analysis of solar ramp across the CONUS.

14 SOLAR ENERGY↗
NASA NTRS2015

GOES-R Dual Isolation

The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the first of the next generation geostationary weather satellites, scheduled for delivery in late 2015. 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 suite's increased sensitive to disturbances over a broad spectrum 0-512 Hz. 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 includes an Earth Pointed Platform (EPP), a stiff optical bench to which the two nadir pointed instruments are collocated together with the Guidance Navigation & Control (GN&C) 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 approximately 5 Hz in all six degrees-of-freedom. A change in Reaction Wheel Assembly (RWA) vendors occurred very late in the program. To reduce the risk of RWA disturbances impacting performance, a secondary passive isolation system manufactured by Moog CSA Engineering was incorporated under each of the six 160 Nms RWAs, tuned to provide attenuation at frequencies above approximately 50 Hz. Integrated wheel and isolator testing was performed on a Kistler table at NASA Goddard Space Flight Center. High fidelity simulations were conducted to evaluate jitter performance for four topologies: 1) hard mounted no isolation, 2) EPP isolation only, 2) RWA isolation only, and 4) dual isolation. Simulation results demonstrate excellent performance relative to the pointing stability requirements, with dual isolated Line of Sight (LOS) jitter less than 1 micron rad.

GN&C Engineering↗