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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. Here this study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Progress in developing GeoSTAR - Microwave Sounder for GOES-R

The Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR)is a new concept for a microwave sounder, intended to be deployed at the Jet Propulsion Laboratory under NASA Instrument Incubator Program sponsorship, and is currently undergoing tests and performance characterization.

aperture synthesis↗
NASA NTRS2009

Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

EXPLICIT PHYSICS ALGORITHMS↗
NASA NTRS2016

Cloud Tolerance of Remote-Sensing Technologies to Measure Land Surface Temperature

Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared(TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave(MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product. This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

polar-orbiting↗
NASA NTRS2018

Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements Over Asia

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be in orbit in 2019 onboard the GEO-KOMPSAT 2B satellite and will continuously monitor air quality over Asia. The GEMS will make measurements in the UV spectrum (300-500 nm) with 0.6 nm resolution. In this study, an algorithm is developed to retrieve aerosol optical properties from UV-visible measurements for the future satellite instrument and is tested using 3 years of existing OMI L1B data. This algorithm provides aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol layer height (ALH) using an optimized estimation method. The retrieved AOD shows good correlation with Aerosol Robotic Network (AERONET) AOD with correlation coefficients of 0.83, 0.73 and 0.80 for heavy-absorbing fine (HAF) particles, dust and non-absorbing (NA) particles, respectively. However, regression tests indicate underestimation and overestimation of HAF and NA AOD, respectively. In comparison with AOD from the OMI/Aura Near-UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 km x 24 km V003 (OMAERUV) algorithm, the retrieved AOD has a correlation coefficient of 0.86 and linear regression equation, AOD(sub GEMS) = 1.18AOD(sub OMAERUV) + 0.09. An uncertainty test based on a reference method, which estimates retrieval error by applying the algorithm to simulated radiance data, revealed that assumptions in the spectral dependency of aerosol absorptivity in the UV cause significant errors in aerosol property retrieval, particularly the SSA retrieval. Consequently, retrieved SSAs did not show good correlation with AERONET values. The ALH results were qualitatively compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products and were found to be well correlated for highly absorbing aerosols. The difference between the attenuated-backscatter-weighted height from CALIOP and retrieved ALH were mostly closed to zero when the retrieved AOD is higher than 0.8 and SSA is lower than 0.93. Although retrieval accuracy was not significantly improved, the simultaneous consistent retrieval of AOD, SSA and ALH alone demonstrates the value of this stand-alone algorithm, given their nature for error using other methods. The use of these properties as input parameters for the air mass factor calculation is expected to improve the retrieval of other trace gases over Asia.

Geostationary Environment Monitoring Spectrometer ↗
NASA NTRSDate not supplied

Short timescale stability monitoring of GOES-16 visible channels based on daily inter-calibration events with VIIRS and MODIS

The CERES project uses geostationary (GEO) imager derived broadband fluxes to estimate the diurnal flux variations between the Terra (10:30 LT) and Aqua (1:30 LT) flux obs. The GEO derived cloud properties are utilized for narrowband to broadband and angular directional model (ADM) algorithms. The GEO imager channels are first inter-calibrated to the Aqua-MODIS C6.1 calibration reference to ensure that the retrieved clouds and fluxes will be consistent. The CERES project has a 2-month latency, all GEO, MODIS and VIIRS calibration anomalies must be identified and mitigated. Convert the monthly GEO ray-matching algorithm into a daily monitoring algorithm to catch any daily calibration anomalies.

geostationary sensor calibration↗
NASA NTRS2021

A Machine Learning Approach to Objective Identification of Dust in Satellite Imagery

Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red-Green-Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision-makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night-time dust identification difficult even for experts. This study applies machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no-dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no-dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night-time hours when visual dust detection is limited by the cooling ground surface characteristics.

dust↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Earth Observations

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary↗
NASA NTRSDate not supplied

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗
NASA NTRSDate not supplied

NASA Earth Exchange – Current overview of climate and wildfire-oriented works

NASA Earth Exchange (NEX) combines state-of-the-art supercomputing, Earth system modeling, and NASA remote sensing data feeds to deliver a work environment for exploring and analyzing petabyte-scale datasets covering large regions, continents, or the globe. As an accessible platform, NEX can accelerate fundamental research, develop new applications, and reduce overall project costs by providing the research community with data, software, and high-end computing power. Two research thrusts for the NEX community are developing and distributing the NEX-GDDP-CMIP6 downscaled climate dataset and the community-driven wildfire research. NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four “Tier 1” greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The wildfire research thrust leverages geostationary and low earth orbit remote sensing platforms and advanced modeling (WRF). We present a high-level overview of the NEX community, discuss relevant datasets and show several visualization techniques of data used in the wildfire research activity.

NEX↗
NASA NTRS2018

Advancing the Science of Both Coastal Ocean Biophysics and Atmospheric Pollution Chemistry: A Final Report to the Nasa Earth Science Division by the GEO-CAPE Team

The 2007 Decadal Survey (DS) included the recommendation for the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission to launch in 2013–2016 to advance the science of both coastal ocean biophysics and atmospheric-pollution chemistry. In 2009 the NASA Earth Science Division (ESD) initiated study activities for GEO-CAPE and 8 other near- to medium-term missions to help determine the readiness of these conceptual missions to begin the formulation phase. In FY15 the GEO-CAPE mission study team completed a white paper summarizing the results of the pre-formulation work accomplished to date: https://geo-cape.larc.nasa.gov/pdf/GEO-CAPE_2009-2015_SummativeWhitePaper.pdf The team was directed to complete a final report in FY18. This document, prepared as an addendum to the FY15 white paper, serves as the GEO-CAPE final report and focuses only on FY16-18 activities and on summarizing the final state.

Geostationary Coastal and Air Pollution Events↗
NASA NTRS2022

Diverse Responses of Multiple Satellite-Derived Vegetation Greenup Onsets to Dry Periods in the Amazon

Satellite-derived vegetation greenness and seasonal dynamics in the Amazon have generated considerable academic debate over the past two decades. Despite this, the phenological timing of Amazon forests and, in particular their responses to dry periods, remain poorly understood. Here we explicitly identify the diverse timing of vegetation canopy greenup onsets from 10-min geostationary satellite observations, and compute the timing of both the start and end of dry periods from daily precipitation data. We, for the first time, reveal that the Amazon vegetation canopy regularly experiences two cycles of greenup onsets during a year. The occurrence of greenup onset varies diversely from the start to end of the dry periods, but demonstrates regular shifts in local areas, although irregular shifts across the region. The multiple greenup onsets show complex spatial shifts, which closely follow the spatial movement of dry periods. The results provide a new insight into our understanding of the complexity of Amazonian vegetation canopy dynamics during dry periods, which could significantly improve the simulation of carbon and water cycles.

Satellite-Derived Vegetation↗
NASA NTRSDate not supplied

Satellite Tracking of Shallow Cumulus during CAMP2Ex: The Aerosol and Organization Connection

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Shallow clouds come in many forms, from scattered, short-lived cumulus to organized features that include cold pool boundaries and lines reaching O(100 km) scale lasting several hours. In this study, we explore how cloud morphology and environmental aerosol affect shallow cumulus lifecycle properties. Measuring such influences requires a broad, detailed view of these cloud fields as they grow and decay. Geostationary satellites provide a means to follow the clouds with enhanced spatiotemporal sampling from space. We apply a cloud-tracking tool to study lifecycle properties of shallow cumulus sampled during the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign of 2019. The mission conducted airborne and shipborne operations over the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. We segment shallow cumulus on AHI 0.5-km visible reflectance during the daylit hours of several research flights that exhibited ideal atmospheric conditions for tracking. We collect the resulting cloud tracks by regions containing airborne sampling of the cloud environments and by two specific segmentation techniques: one optimized for small, disorganized cumulus and the other for larger, organized clouds using suitable thresholds for reflectance and blurring. We present an analysis of cloud lifecycle properties using our dual segmentation approach for various sampled environments of CAMP2Ex. A total of 7 research flights (RFs) exhibited ideal conditions for generating cloud-track ensembles under similar thermodynamic and kinematic profiles but with varying aerosol load. We consider lifetime, track-achieved metrics, and ensemble-fitted growth rates to evaluate the aerosol effect. Cloud elongation, a factor in cloud morphology, is also considered for any potential influence in observed shifts in growth rates. The results suggest that aerosol affects cloud lifecycles more significantly for clouds with larger areas and independent of the elongation factor.

Cloud Tracking↗
DOE OSTI2021

Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products

Here we assess the accuracy of estimating daily grass reference evapotranspiration (PM-ET o ) using daily shortwave radiation (R s ) and reference evapotranspiration (ET REF ) products provided by the Meteosat Second Generation (MSG) geostationary satellite delivered by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF) framework. The accuracy of using reanalysis ERA5 shortwave radiation data (R s ERA5 ) provided by the European Center for Medium-Range Weather Forecasts (ECMWF) is also evaluated. The assessments were performed using observed weather variables at 37 weather stations distributed across continental Portugal, where climate conditions range from semi-arid to humid, and 12 weather stations located in Azores islands, characterized by humid, windy and often cloudy conditions. This study&#x27;s use of data from a variety of climate conditions contributed to a unique and innovative assessment of the usability of LSA-SAF and ERA5 products for ET o estimation. The first assessment focused on comparing LSA-SAF estimates of R s (R s LSA-SAF ) against ground stations (R s ground ). The results showed a good matching between the two R s data sets for continental Portugal but a tendency for R s LSA-SAF to under-estimate R s ground in the cloudy islands of Azores. ET o values computed using R s LSA-SAF data and observed temperature, humidity and wind speed (ET o LSA-SAF ) were then compared with PM-ET o estimates with ground-based data, which were used as benchmark; input data of temperature and humidity needed for PM-ET o were quality checked for surface aridity effects. It was observed that ET o LSA-SAF is strongly correlated with PM-ET o (R 2 > 0.97) for most locations in continental Portugal, with regression coefficient of a linear regression forced to the origin ranging between 0.95 and 1.05, mean root mean square error (RMSE) of 0.13 mm d –1 , and Nash and Sutcliff efficiency of modeling (EF) above 0.95. For most Azores locations, ET o LSA-SAF over-estimated PM-ET o . This is likely a consequence of the high spatio-temporal heterogeneity of weather conditions that occur in these oceanic islands together with the different footprints of satellite (averaged over the pixel) and station observations. Reanalysis ERA5 shortwave radiation data presented similar behavior to the LSA-SAF products, however with slightly lower accuracy. The daily LSA-SAF ET REF product (ET REF LSA-SAF ) was assessed and results have shown a good accuracy of this product, with acceptable RMSE and high EF values, for continental Portugal but a low accuracy for the Azores islands. A simplified bias correction approach was shown to improve both ET o derived from the LSA-SAF products, namely for Azores stations, which seem to be representative of smaller areas. The use of the FAO-PM temperature approach (PMT) was also assessed using the R s LSA-SAF and R s ERA5 data, which showed a superiority of the LSA-SAF product for ET o estimations (ET o PMT LSA-SAF ). No significant differences (p < 0.05) were observed in terms of the median value of the RMSE when adopting ET o PMT and ET REF LSA-SAF . Differently, results showed that using the R s LSA-SAF in the PMT approach (ET o PMT LSA-SAF ) produces significantly better RMSE results than ET o PMT and ET REF LSA-SAF . Overall, the performed assessment allows concluding that the use of R s LSA-SAF, and to a lesser extent the use of the R s ERA5 , highly improves the accuracy of computation of ET o when R s observations are not available, including when only temperature data are accessible. The use of the ET REF LSA-SAF product is a good alternative when observed weather data are not available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Radio Frequency Transients Correlated with Electron Flux Measured On-Board the STP-Sat6

We analyzed transient electromagnetic signatures, referred to as spacecraft environment discharges (SEDs) or on-board discharges (OBDs), and the in-situ particle environment simultaneously measured by the Radio Frequency Sensor (RFS) and the Space and Atmospheric Burst Reporting System Z-plasma spectrometer (SABRS-ZPS), respectively. These sensors are on-board the U.S. Department of Defense Space Test Program Satellite 6 (STP-Sat6) in geostationary orbit. Radio frequency signatures of SEDs on STP-Sat6 have relatively high peak amplitudes; they often caused amplitude-saturation in the so-called high band (120–140 MHz frequency range) of the RFS likely due to the proximity of their source to the RFS’s dipole antennas. We examined the occurrence rate of SEDs reported by the RFS and its temporal relationship to electron flux in the 7.9–12.2 keV range. Peaks in SED count rates were time-correlated with peaks in electron flux. The electron-flux peaks appeared to lead the SED count rate peaks by 24–45 min for the 7.9–12.2 keV energies. This indicates that incidence of electrons at this relatively low-energy range likely resulted in ramping up of the charging of the spacecraft leading to periods of high occurrence rates of discharges in and around the spacecraft.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The evolution of soil microstructure and micromineralogy along a soil age gradient on the Galápagos Islands (Ecuador)

The islands of the Galápagos archipelago are of different ages due to the combination of a geostationary volcanic hotspot and the eastward movement of the Nazca tectonic plate. Taking advantage of this, a soil chronosequence (1.5–1070 ka) has recently been established covering four of the islands. This was used to investigate the temporal development of soil microstructure and micromineralogy using micromorphological methods in combination with synchrotron-based mineralogical and geochemical microanalyses. Along the chronosequence, the microstructure changed from intergrain microaggregates in the young soils to angular blocky microaggregates in the older soils. In the young soils, a high amount of vitric scoria and weatherable minerals occur, but they quickly disappear with age, and micromass increased fast. The micromorphological studies as well as micro-XRD and micro-XRF revealed increasing heterogeneity of soil microstructure and micromineralogy due to weathering and pedogenic processes until intermediate soil age. In the oldest soils, the microstructure became more uniform again, de-mixing pedofeatures like clay coatings have completely disappeared, and kaolinite, hematite and gibbsite dominate the mineralogical composition. Our study shows that the development of soil microstructure mirrors macroscopic soil profile development showing increasing differentiation until intermediate developmental stages and receding heterogeneity in highly weathered soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning

Fengyun-4A (FY-4A), which is the latest-generation Chinese geostationary meteorological satellite, measures solar reflection and thermal emission with high temporal, spatial, and spectral resolutions. It is expected to be highly beneficial for solar resource assessment and forecasting in China. This study is the first to estimate, using FY-4A and a random forest model, the global horizontal irradiance (GHI) at a 4-km–15-min spatio-temporal resolution over China, as a means to arrive at a solar photovoltaic (PV) resource map. In terms of GHI estimates, the root mean square error and mean bias error between hourly measured and retrieved values are 147.02 (35.2%), –5.64 W/m 2 (–1.4%), respectively, whereas the values of daily estimates are 29.20 (18.0%), –2.97 W/m 2 (–1.3%). The retrieval accuracy is found much better for instances with solar zenith angles smaller than 60°. Relatively larger errors are found at locations in the Sichuan Basin and northeastern China, which can be attributed to bright surfaces and/or strong cloud transients. With the retrieved irradiance, PV resource is derived through a physical model chain. The annual mean PV resource map suggests that, over most of the west areas, the annual mean effective irradiance exceeds 1700 kWh/m 2 , with the highest value found in Tibet (around 2000 kWh/m 2 per annum). Eastern China has an annual effective irradiance of only 1300–1500 kWh/m 2 . In conclusion, the region with poorest solar resource is the Sichuan Basin (less than 1100 kWh/m 2 per annum).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗