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At least 1,189 records · Page 66
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NASA NTRSDate not supplied

Electric Sail Design Sensitivities

Electric Sails (E-Sails) are a promising propulsion technology that seek to enable high characteristic propellentless acceleration for spacecraft to reach distant and/or difficult to reach orbits such as rapid transit to heliopause. The E-Sail system exchanges momentum by using positively charged electrostatic tethers to repel solar wind photons to push it through space. This concept was first theorized by Pekka Janhunen in 2004 with further developments occurring including a NASA Innovative Advanced Concepts Phase 1 and Phase 2 hosted out of NASA’s Marshall Space Flight Center (MSFC). These developments led to further maturation of the overall system, and this paper is designed to help identify different design sensitivities of an integrated Electric Sail system. To approach this, a small team at MSFC took an in-house developed three degrees of freedom (3DoF) simulation tool and a trajectory modelling tool to look at different design parameters and to determine a potential ideal E-Sail configuration. An E-Sail system has different design architectures including a barbell design, hub and spoke design, and a potential hybrid solution. The barbell design features two equally massed satellites that spin around a central point in the tether system. The hub and spoke design features a large central spacecraft with small end spacecraft to aid in formation control of the overall E-Sail system. Leveraging elements from both the hub and spoke, and barbell design, a hybrid option exists where one could have a larger central mass and one or two tethers extended to a smaller end mass [2]. In summary, the hub and spoke design is the ideal configuration for E-Sail with tethers spanning kilometers to achieve the designed design characteristic acceleration of at least one mm/s2 with this architecture being the focus of this study. Different key design parameters were varied as part of this study. These parameters include the total number of tethers, the length of the tethers, spin rate, relative spacecraft mass, tether voltage, inertia per tether, and impact of slew rate changes. These results started from an internal MSFC technology demonstration mission design and then the parameters were varied with engineering judgement to ensure that the results were consistent with expected results. These were varied in the MSFC 3DoF tool and compared to baseline results to generate a candidate ideal E-Sail design system. These results were then used to help inform the mission analysis design for the system. The 3DoF trajectory optimization was performed in the Astrodynamics and Space Science Enabling Toolbox (ASSET) and utilized collocation optimization of control vector pointing with the objective function being transfer duration. A representative model of the E-Sail force was derived as a function of the Sun-pointing vector direction. This model was initialized with a zero Sun Incidence Angle (SIA) trajectory for the initial guess generation, then with a portion of the mission optimized SIA time history for fast outbound transfers. After the trajectory is hyperbolic relative to a heliocentric frame, the SIA is maintained at 0 degrees except for any needed trajectory correction maneuvers. This trajectory optimization looked at different characteristic acceleration values to analyze the impact it had on mission performance. As a result of this study, the team can best inform mission designers and technology development efforts to mature the E-Sail system. This study will allow mission designers to have defined rules-of-thumb to design an E-Sail system to meet desired mission needs as well as providing several sample mission profiles. These design drivers will inform maturation efforts on which design requirements to consider for an integrated E-Sail design.

E-Sail↗
NASA NTRSDate not supplied

Towards a Marine Stratus Climatology on Drizzle Occurrence from CALIPSO

Marine stratus are a predominant feature of our planet with the annual mean coverage exceeding 20%. They strongly reflect sunlight, yet exert only a modest effect on outgoing infrared radiation, providing a significant net cooling to the Earth’s radiative balance. Their formation is coupled to boundary layer circulations that are driven, in part, by cloud top radiative cooling and evaporative cooling from precipitation in downdrafts. Understanding how these cloud systems evolve as the climate changes is a key question that requires additional information on their lifecycle and microphysical properties to accurately represent their behavior in global circulation models. From a large-scale perspective, insight into the microphysical properties of marine stratus at cloud top can be realized through estimates of the effective radius (Re) of the droplet size distributions derived from MODIS observations. Estimates on the occurrence of rain/drizzle are available from CloudSat. Together these observations indicate that precipitation frequently occurs in clouds with higher cloud top Re. This relationship is consistent with the well documented shift in cloud top droplet size distributions towards fewer, yet larger droplets prior the onset of precipitation. Here we report on a new and complementary set observations from the CALIPSO mission. The approach derives an extinction-to-backscatter ratio (Sc, also known as the cloud lidar ratio) using an established relationship that depends on observations of the lidar attenuated backscatter and volume depolarization ratio within the cloud. Because Sc is strongly and inversely related to Re, a change in the derived Sc from higher to lower values corresponds to a change in the droplet size distribution as seen by MODIS. This change in the lidar signals at cloud top clearly identifies clouds that are capable of precipitation. The presentation provides a brief overview of the approach for deriving Sc and compares CALIOP-derived Sc with observations from other techniques. CALIOP classifications of drizzling clouds, based on the retrieved values Sc, are compared to independent, collocated assessments of drizzle occurrence reported in the standard CloudSat data products. Regional and seasonal comparisons highlight the strengths and weaknesses of the two sensors. A machine learning approach that combines information from both CALIOP and CloudSat showcases possible improvements in the global identification of scenes likely to contain rain-bearing clouds.

CALIPSO↗
NASA NTRSDate not supplied

A Golden Era of PSC Observations From the A-Train By CALIOP and MLS

The fundamental role of polar stratospheric clouds (PSCs) in the depletion of stratospheric ozone is well established. Heterogeneous reactions on PSCs convert the stable chlorine reservoirs HCl and ClONO2 to chlorine radicals that destroy ozone catalytically. PSCs also prolong ozone depletion by delaying chlorine deactivation through the removal of gas-phase HNO3 and H2O by sedimentation of large PSC particles. There has been substantial recent progress in our understanding of PSC processes due to the long-term, vortex-wide observations from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite and the MLS (Microwave Limb Sounder) on the Aura satellite. The CALIOP observations provide unprecedented high spatial resolution information on particulate aerosol/PSC morphology and composition, while MLS provides complementary collocated measurements of gas-phase HNO3 and H2O, the major constituents of all PSC particles. The combined analyses of these datasets have enabled better PSC composition discrimination and provided valuable new insight into PSC formation processes and particle growth kinetics. The more than 17 years of CALIOP and MLS data have uniquely captured the primary aspects of the seasonal and multi-year variability of PSCs in the Arctic and Antarctic, enabling the development of a state-of-the-art reference PSC data record and climatology. In this presentation, we highlight the significant advances in our understanding achieved during this golden era of spaceborne PSC observations and identify remaining gaps in our knowledge that future spaceborne lidar missions may help resolve.

polar stratospheric clouds↗
NASA NTRSDate not supplied

Uncertainties in an Observation-Based Estimate of the Global Aerosol Direct Radiative Effect

Aerosols impact Earth’s radiation budget directly through interactions with radiation and indirectly via interactions with clouds. Aerosols have significant radiative impacts on Earth’s energy budget and represent the largest uncertainty in the radiative forcing of the current climate. Although the magnitude of the aerosol direct radiative effect (DRE) is estimated to be less than that of the indirect effect, uncertainties are large. While model-based estimates of aerosol radiative forcing are subject to uncertainties in modeled aerosol properties and uncertainties in simulated cloud cover and albedo, most observation-based studies of global aerosol DRE have their own difficulties with aerosol optical properties and have been limited to cloud-free skies in most cases. Estimates of the uncertainty of the aerosol DRE are themselves uncertain and have varied widely among different published studies. In this study we use the CERES-CALIPSO-CloudSat-MODIS (C3M) product to estimate the global clear-sky and all-sky aerosol DRE. C3M merges collocated data from CERES, CALIPSO, CloudSat, and MODIS with information from reanalysis and an aerosol transport model. With CALIOP observations of aerosol below optically thin clouds and above clouds, co-located with cloud albedo from MODIS, the C3M dataset allows a detailed exploration of observational uncertainties. By perturbing the aerosol properties used and comparing radiative effects in perturbed cases with the base case we characterize uncertainties in estimated aerosol DRE due to uncertainties in the aerosol properties involved. The results also provide a basis for determining measurement requirements to improve the accuracy of DRE estimates. We will describe the approach and present results.

Dave Winker↗
NASA NTRS2023

A Concept of Precise VLBI/GNSS Ties with Micro-VLBI

We present here a concept of measuring local ties between collocated GNSS and VLBI stations using the microwave technique that effectively transforms a GNSS receiver to an element of a VLBI network. This is achieved by modifying the signal chain that allows to transfer voltage of the GNSS antenna to a digitizer via a coaxial cable. We discuss the application of this technique to local tie measurement. We have performed observations with a GNSS antenna and FD-VLBA radiotelescope and detected a strong interferometric signal from both radiogalaxies and GNSS satellites.

Geodesy↗
NASA NTRSDate not supplied

Noise Reduction Trajectory Analysis of a Supersonic Business Jet using Novel Optimization Tools

Proposals to reduce airport noise during takeoff and landing for supersonic aircraft using methods such as variable noise reduction systems add complexity to conceptual flight models. To better optimize these takeoff and landing profiles for noise certification, new modeling methods are explored in this paper with the end goal of more effectively determining the sensitivities of airframe, propulsion, and mission design variables on overall airport noise. Takeoff and landing trajectories are modeled for a notional supersonic business jet concept developed by NASA for use in environmental impact studies conducted by the International Civil Aviation Organization. Optimization tools capable of gradient-based optimal control and collocation solving methods are examined with the aim of achieving faster solutions in a more comprehensive design space. The benefits and limitations of these new methods are compared with existing methods used in previous studies of the airplane concept. It is found that the new modeling methods match closely when compared with existing tools for a standard takeoff and landing case, with a cumulative effective perceived noise difference of 0.1 EPNdB. Comparisons between takeoff trajectories with a variable noise reduction system match within 0.8 EPNdB and 1.3 EPNdB, due to highlighted differences in the modeling approaches.

supersonic↗
NASA NTRS2023

High-Resolution Snowstorm Measurements and Retrievals Using Cross-Platform Multi-Frequency and Polarimetric Radars

Many studies have provided microphysical retrievals using radars of different frequencies, platforms, and methodologies. However, little is known about the consistency of retrievals derived from different radar platforms (i.e., airborne or spaceborne vs. groundbased) and their methodologies. This study is the first to directly compare snow mean volume diameter (Dm) retrievals from both nadir-pointing airborne multi-frequency radars and ground-based polarimetric range-height indicator (RHI) radar scans along a coincident airborne flight track. Compared to typical airborne/spaceborne/ground validation studies which at best provide O(1 km) resolution, these two platforms together provide directly collocated, high-resolution O(10m) retrievals. Dm retrieval values were over all both largest and smallest when using two separate dual-wavelength methods from aircraft measurements. A Triple-frequency analysis suggests the possibility that snow aggregates were generally composed of needles. The results shown here can be used as a benchmark for comparing retrieval methodologies.

Edwin L. Dunnavan↗
NASA NTRS2024

Validation and Application of Satellite-Derived Sea Surface Temperature Gradients in the Bering Strait and Bering Sea

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift.

Arctic↗
NASA NTRS2024

Merged and Gridded GPM and Atmospheric River Data Product

The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual-frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis-derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high-latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user-friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation.

Marian E Mateling↗
NASA NTRS2024

Mapping Cloud Microphysical Properties to Triple-Wavelength Airborne Lidar Measurements in Winter Storms: Results from the IMPACTS Field Campaign

Ice production and growth inmixed-phase cloudsgive a complicated picture of the radiative and mass characteristics due to the heterogeneity of particle shapes and sizes. Collocated airborne lidar and microphysics datasets from a recent winter storm field study are used to evaluate the effect of ice and liquid hydrometeors,observed in situ,on the multiple-wavelength backscatter coefficientvalues measured by the NASA Cloud Physics Lidarover the course of three winter deployments in the Midwestand Northeast United States.

Joseph A. Finlon↗
NASA NTRSDate not supplied

Deep Convective Cloud Calibration Sensitivity Studies in Support of Radiometrically Scaling GEO Imagers With VIIRS

The NASA CERES SYN1deg product provides the scientific community regional hourly TOA and surface broadband fluxes and clouds. For consistent geostationary (GEO) derived fluxes and clouds the GEO imagers are radiometrically scaled to the Aqua-MODIS calibration reference. The CERES project utilizes GEO and MODIS analogous channel coincident, collocated, and co-angled radiance pairs as the primary method to inter-calibrate the GEO imagers. Deep convective clouds (DCC) are bright tropical, near Lambertian, top of atmosphere pseudo invariant Earth targets that do not rely on coincident radiance pairs to radiometrically scale sensors to a common calibration reference. DCC pixels are identified by cold IR window channel brightness temperatures (BT). Successful DCC inter-calibration relies on sufficient sampling of comparable identified DCC pixels between sensors. The DCC identification thresholds, pixel resolution, local time, and geographical sampling should be consistent between sensors. Over 20 GEO imagers during the span of the CERES record have various visible and IR pixel resolutions. Unlike the MODIS and VIIRS imagers, where the visible and IR pixel resolutions are similar, the GEO visible pixel resolution is much finer than the IR pixel resolution. The study will examine the pixel resolution impact on the DCC calibration methodology. The resulting DCC calibration coefficients can be validated against the sensor pair calibration methodology. Accounting for the DCC calibration imager resolution differences will allow the CERES project to utilize the future CLARREO observations as the calibration reference for all GEO, MODIS, and VIIRS imagers across the CERES record.

Conor Haney↗
NASA NTRSDate not supplied

Optimal Control using Composite Bernstein Approximants

In this work, we present composite Bernstein polynomials as a direct collocation method for approximating optimal control problems. An analysis of the convergence properties of composite Bernstein polynomials is provided, and beneficial properties of composite Bernstein polynomials for the solution of optimal control problems are discussed. The efficacy of the proposed approximation method is demonstrated through a bang-bang example. Lastly, we apply this method to a motion planning problem, offering a practical solution that emphasizes the ability of this method to solve complex optimal control problems.

Gage MacLin↗
NASA NTRSDate not supplied

Deep Convective Cloud Calibration Sensitivity Studies in Support of Radiometrically Scaling GEO Imagers With VIIRS

The NASA CERES SYN1deg product provides the scientific community regional hourly TOA and surface broadband fluxes and clouds. For consistent geostationary (GEO) derived fluxes and clouds the GEO imagers are radiometrically scaled to the Aqua-MODIS calibration reference. The CERES project utilizes GEO and MODIS or VIIRS analogous channel coincident, collocated, and co-angled radiance pairs as the primary method to inter-calibrate the GEO imagers. Tropical deep convective clouds (DCC) are bright, near Lambertian, top of atmosphere pseudo invariant Earth targets that do not rely on coincident ray-matched radiance pairs to radiometrically scale sensors to a common calibration reference. The DCC invariant target (DCC-IT) methodology collectively analyzes all tropical DCC identified pixel radiances by way of probability density function (PDF) distributions. Perfectly inter-calibrated sensor pairs should reveal nearly identical PDF distributions given the same DCC identification criterion. The PDF median, mean, mode, and inflection point statistics were tested as a function of DCC identification criterion using SNPP-VIIRS and Himawari-8 AHI 0.65μm channel radiances during January 2019. It was found that the PDF inflection point provided inter-calibration factors within 0.25% that were nearly independent of DCC identification criterion. The PDF median provided inter-calibration factors within 0.25% for the coldest BT and most stringent homogeneity factors. The PDF mean and mode statistics were inadequate under any DCC conditions. It is critical for the DCC pixel radiances to be anisotropically corrected. The DCC-IT methodology will also be tested for other visible and SWIR bands.

DCC↗
NASA NTRSDate not supplied

Geodetic Reference Instrument Transponder for Small Satellites (GRITTS)

The accuracy of the Terrestrial Reference Frame is limited by systematic errors in tying the contributions from the different geodetic techniques. Local survey uncertainties are limited due to the inaccessibility of the instrument reference points. NASA is implementing a technology demonstration mission called Geodetic Reference Instrument Transponder for Small Satellites(GRITSS) that uses a small satellite as a space-based ref-refence point for tying together collocated geodetic stations. The technology being demonstrated uses a novel idea of upconverting the Global Positioning System (GPS) signals received at the satellite and transponding them to a VLBI Global Observing System (VGOS) antenna ground station. This approach does not require the satellite to be in view of more than one VLBI station at a time, allowing the use of Low Earth Orbits and an inexpensive CubeSat. The demonstration will initially involve only the NASA VGOS stations in Maryland, Hawaii, and Texas, but may be expanded later in the mission to include other international VGOS stations. This presentation will provide an overview of the GRITSS mission and the measurement concept.

S M Merkowitz↗
NASA NTRSDate not supplied

Desensitized Aerocapture Guidance

Mission design flexibility can be increased using an aerocapture maneuver to capture into an orbit around a planet from a hyperbolic trajectory. In past work, bang-bang control has been shown as an optimal guidance solution that minimizes the $\Delta{V}$ required to get into a desired orbit using bank modulation. This work revisits aerocapture using a bank modulation problem to search for an optimal solution using a direct collocation method. Previous studies have shown that aerocapture performance is sensitive to atmospheric density. In the literature, several studies have used a desensitized control approach to make the optimal guidance more robust to uncertainties in the model. To this end, this work aims to develop a desensitized aerocapture guidance robust to uncertainty in atmospheric density. Furthermore, this work compares the robustness of the open-loop desensitized aerocapture guidance with optimal aerocapture guidance with an aerocapture application at Earth.

Pardha Sai Chadalavada↗
NASA NTRSDate not supplied

Reconstruction of Thermal Protection System Aeroheating using a Green’s Function Approach

Inverse heat transfer (IHT) techniques are often used to reconstruct the surface heating conditions on spacecraft thermal protection systems (TPS) during atmospheric entry. Current IHT techniques for entry spacecraft applications, however, demand substantial computational resources, and are impractical for analyses such as uncertainty quantification and real-time health monitoring. In this paper, a Green’s function sensor fusion approach is used to reconstruct the TPS surface aeroheating conditions on experimental spaceflight and ground test systems from collocated temperature and heat flux sensors embedded in the TPS. The algorithm leverages Green’s functions to model the heat conduction within the spacecraft TPS and stabilizes the recovery of the surface heating condition using the direct heat flux sensor measurement. The algorithm is validated using arc-jet ground test data and applied to the reconstruction of the Mars 2020 backshell heating during Martian atmospheric entry. The performance of the algorithm is benchmarked against a current state-of-the-art IHT framework, FIAT_Opt. The Green’s function-based reconstruction algorithm recovers the net hot-wall heat flux absorbed by the TPS and the incident heat flux from the atmospheric entry environment in close agreement with FIAT_Opt. Notably, computation of the surface heating condition is completed in three orders of magnitude less time with the Green’s function sensor fusion approach using a consumer-grade PC, versus with FIAT_Opt running on a high performance computer cluster. The efficiency of the algorithm is leveraged to compute the uncertainty contributions of input parameters to the total uncertainty in reconstructed Mars 2020 backshell heating for the full atmospheric entry heat pulse. The sensitivity analysis uncovers that, at different times throughout the entry heat pulse, uncertainties in the TPS specific heat, thermal conductivity, and emissivity are all dominant drivers of the reconstruction uncertainty. These results demonstrate Green’s functions and sensor-fusion techniques as promising IHT approaches to reconstruct atmospheric entry environments from TPS-embedded measurements, and highlight how these techniques may give access to post-flight analyses previously hindered by the prohibitive cost of current methods.

Kenneth McAfee↗
NASA NTRSDate not supplied

Firesense: Supporting Operational Partners Through Co-Development

Fire is a natural disturbance and fundamental to many ecosystems, but, across the United States and many areas globally, fires have become more common, larger and more likely to occur at the same time. This has the potential to challenge resource allocation and response and requires coordinated integration of technology and tools at the spatial and temporal scales required by practitioners who make wildland fire management decisions. In the US wildfire is currently managed across state, federal, and tribal agencies who leverage various datasets to guide management decisions. FireSense, a NASA Science Mission Directorate project, aims to develop and deliver trailblazing technology and tools for use before, during, and after wildland fires by working together with land managers and practitioners. FireSense is focused on turning information into solutions by expanding partnerships and collaborations with operational agencies to inform and deliver data and technology to support decisions for wildland fire management. Through implementation across the US and with campaign activities in a variety of landscapes, coordinated research and development work will build upon adaptive and use-inspired approaches that can be put into operation. By leveraging and complementing partner activities through coordinated investment, we support a more comprehensive and cohesive response that can advance fire science to better understand fire behavior and effects through integrated measurement, monitoring and modeling. We present updates from in-progress technology development and field campaigns within the US which include sampling and application across scales with information from multiple sensors collocated with field measurements and highlighting the importance of cross-scale observations during the fire lifecycle. Coordinated investment in new science and technology for the complete fire lifecycle supports a comprehensive and cohesive fire response that help to overcome barriers and anticipate and manage the new reality of extreme fires in a warming world.

Jacquelyn Shuman↗
NASA NTRSDate not supplied

Machine Learning Application for Improving Cloud Detection and Phase Determination Over Sunglint Regions for Geostationary Satellites

Cloud detection and phase determination over sunglint regions has been a challenge, especially for geostationary (GEO) satellites. Sunglint is observed when the sunlight specular reflection is at the same viewing angle of the satellite sensor. This intense reflection in the visible channels (VIS) is often comparable to that from optically thick clouds. It also contaminates the shortwave infrared channels (SWIR). Consequently, VIS and SWIR channels become less useful - or not useful- when they are saturated, hampering the detection of cloudy and clear-sky pixels. Sunglint contamination happens frequently and exists nearly in every daytime GEO full disk satellite images. However, sunglint intensity and region are difficult to model due to variable viewing geometry and ocean surface conditions. Moreover, existing physical models do not meet the accuracy required for operational GEO satellite cloud detection. We developed a machine learning algorithm to improve cloud detection in sunglint conditions for the NASA Langley’s Satellite ClOud and radiation Property retrieval System (SatCORPS). This poster presents our recent progress in the algorithm development, validation and applications. The algorithm is validated using collocated SatCORPS GOES-East and GOES-West cloud products. We demonstrate that the machine learning cloud detection in sunglint regions is superior to the traditional approach by improving temporal consistency between sunglint and non-sunglint conditions.

Machine Learning, Cloud detection, Sunglint, SatCO↗