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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

MODIS Retrieval of Aerosol Optical Depth over Turbid Coastal Water

We present a new approach to retrieve Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently does not conduct AOD retrieval in shallow waters that have visible sediments or sea-floor (i.e., Class 2 waters). Over the global coastal water regions in cloud-free conditions, coastal screening leads to approx. 20% unavailability of AOD retrievals. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 microns to be negligible regardless of water turbidity, and therefore the 2.1 microns reflectance at the top of the atmosphere is sensitive to both change of fine-mode and coarse-mode AODs. By assuming that the aerosol single scattering properties over coastal turbid water are similar to those over the adjacent open-ocean pixels, the new algorithm can derive AOD over these shallow waters. The test algorithm yields approx. 18% more MODIS-AERONET collocated pairs for six AERONET stations in the coastal water regions. Furthermore, comparison of the new retrieval with these AERONET observations show that the new AOD retrievals have equivalent or better accuracy than those retrieved by the MODIS operational algorithm's over coastal land and non-turbid coastal water product. Combining the new retrievals with the existing MODIS operational retrievals yields an overall improvement of AOD over those coastal water regions. Most importantly, this refinement extends the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides. This expanded coverage is crucial for better understanding of impact of anthropogenic aerosol particles on coastal air quality and climate.

Retrieval↗
NASA NTRS2018

Correcting for Trace Gas Absorption When Retrieving Aerosol Optical Depth from Satellite Observations of Reflected Shortwave Radiation

Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon Moderate Resolution Imaging Spectroradiometer (MODIS, onboard Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, onboard Suomi-NPP) sensors, use wavelength bands in "window" regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper we use High-resolution TRANsmission (HITRAN) database and Line-by-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are "similar", they are different enough that applying MODIS specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases up to 0.07. As recent studies are attempting to create a long-term data record by joining multiple satellite datasets, including MODIS and VIIRS, the consistency of gas correction becomes even more crucial.

Satellite Remote Sensing↗
NASA NTRS2019

Assessing Uncertainties of a Geophysical Approach to Estimate Surface Fine Particulate Matter Distributions from Satellite-Observed Aerosol Optical Depth

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2(exp) resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2(exp) horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3(exp) in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3(exp). Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

aerosol optical depth↗
NASA NTRSDate not supplied

Delamination Depth Imaging for Quasi-Static and Fatigue Load Testing Using Passive Thermography

Passive thermography is used to monitor small increases in temperature resulting from delamination damage formation in composite hat-stiffened panels during both quasi-static and fatigue loading. It has been observed that rapid damage formation produces heating. This is composed of two heat generation components. The first component is an instantaneous response due to an irreversible thermoelastic strain release. The second component is mechanical heating, at the interface of failure, due to fracture damage. This produces a transient rise in temperature as the heat diffuses to the surface. The first component defines the thermal start time for the transient response. A one-dimensional thermal model is fitted to the data pixel by pixel, to produce imagery of the damage depth. The results are compared to ultrasonic and X-ray CT data.

Passive thermography↗
NASA NTRS2020

Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa

Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are simply absent in many major cities across sub-Saharan Africa (SSA). Satellites provide snapshots of regional air pollution but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We investigate the utility of such a multi-source data integration approach using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system, and a linear conversion factor is developed to convert AOD to surface fine particulate matter mass concentration (as PM2.5). With 10 or more ground monitors in Pittsburgh, there is a 2-fold reduction in surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, we assess the ability of combined regional-scale satellite retrievals and local-scale low-cost sensor measurements to improve surface PM2.5 estimation at several urban sites in SSA. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement in surface PM2.5 estimation accuracy with respect to using low-cost ground monitoring data alone. A linear AOD-to-surface-PM2.5 conversion factor developed in Kigali, Rwanda, did not generalize well to other parts of SSA and varied seasonally for the same location, emphasizing the need for ongoing and localized ground-based monitoring, which can be facilitated by low-cost sensors. Overall, we find that combining ground-based low-cost sensor and satellite data, even without including additional meteorological or land use information, can improve and expand spatiotemporal air quality data coverage, especially in data-sparse regions.

AOD↗
NASA NTRS1993

Estimation of Aerosol Optical Depth, Pressure Elevation, Water Vapor and Calculation of Apparent Surface Reflectance from Radiance Measured by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Using a Radiative Transfer Code

The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is an imaging spectrometer that measures spatial images of the total up welling spectral radiance from 400 to 2500 nm at 10 nm spectral intervals. Quantitiative research and application objectives for surface investigations require conversion of the measured radiance to surface reflectance or surface leaving radiance. In this paper we describe a set of algorithms to estimate aerosol optical depth, atmospheric water vapor, and surface pressure height from the AVIRIS measured radiance.

infrared↗
NASA NTRS2022

Lithium-ion Battery Strain Gauge Monitoring and Depth of Discharge Estimation

Lithium-ion battery diagnostics and prognostics rely on measurements of electrical impedance, capacity, and voltage to infer the internal state of the battery. Mechanical changes to the cell structure represent an additional measure of the battery’s state because these changes are related to the overall battery health. As lithium-ion batteries are charged and discharged, lithium ions are inserted or removed from the anode and cathode, a process called intercalation and de-intercalation. As lithium ions intercalate and de-intercalate, they can cause changes to the lattice of the electrode particles, resulting in volumetric changes. These volumetric changes cause mechanical stresses and strains on the lithium-ion battery electrodes, and subsequently, the whole cell’s thickness varies as it is charged and discharged. This paper presents a study on the use of surface-mounted strain gauges for in-situ measurement of structural changes to lithium-ion batteries, along with a characterization of the unit-to-unit differences in strain response. A neural network modeling structure is then used to predict the battery’s depth of discharge under dynamic discharge conditions.

Lithium-ion batteries, depth of discharge, strain ↗
NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗
NASA NTRS2021

Airborne and Ground-Based Measurements of Aerosol Optical Depth of Freshly Emitted Anthropogenic Plumes in the Athabasca Oil Sands Region

In this work we report the airborne aerosol optical depth (AOD) from measurements within freshly emitted anthropogenic plumes arising from mining and processing operations in the Athabasca Oil Sands Region (AOSR) in the context of ground-based AERONET climatological daily averaged AODs at Fort McMurray (Alberta, Canada). During two flights on 9 and 18 June 2018, the NASA airborne 4STAR (Spectrometers for Sky-Scanning, Sun-Tracking Atmospheric Research) Sun photometer registered high fine-mode (FM, <1 µm) in-plume AODs of up to 0.4 and 0.9, respectively, in the vicinity of the plume source (<20 km). Particle composition shows that the plumes were associated with elevated concentrations of sulfates and ammonium. These high AODs significantly exceed climatological averages for June and were not captured by the nearby AERONET instrument (mean daily AODs of 0.10±0.01 and 0.07±0.02, maximum AOD of 0.12) due possibly to horizontal inhomogeneity of the plumes, plume dilution and winds which in certain cases were carrying the plume away from the ground-based instrument. The average 4STAR out-of-plume (background) AODs deviated only marginally from AERONET daily averaged values. While 4STAR AOD peaks were generally well correlated in time with peaks in the in situ-measured particle concentrations, we show that differences in particle size are the dominant factor in determining the 4STAR-derived AOD. During the two flights of 24 June and 5 July 2018 when plumes likely travelled distances of 60 km or more, the average 4STAR FM AOD increased by 0.01–0.02 over ∼50 km of downwind particle evolution, which was supported by the increases in layer AODs calculated from the in situ extinction measurements. Based on these observations as well as the increases in organic mass, we attribute the observed AOD increase, at least in part, to secondary organic aerosol formation. The in-plume and out-of-plume AODs for this second pair of flights, in contrast to clear differences in in situ optical and other measurements, were practically indistinguishable and compared favourably to AERONET within 0.01–0.02 AOD. This means that AERONET was generally successful in capturing the background AODs, but missed some of the spatially constrained high-AOD plumes with sources as close as 30–50 km, which is important to note since the AERONET measurements are generally thought to be representative of the regional AOD loading. The fact that industrial plumes can be associated with significantly higher AODs in the vicinity of the emission sources than previously reported from AERONET can potentially have an effect on estimating the AOSR radiative impact.

AERONET↗
NASA NTRSDate not supplied

Evaluation of Variable-Depth Liners with Slotted Cores

An initial evaluation of variable-depth, slotted-core liners fabricated with conventional, flightworthy com-ponents is presented. Liners with two distinct chamber lengths provide significant broadband absorption and their impedances are, to first order, predictable with the NASA impedance prediction code. These liners employ film caps embedded in alternating chambers to provide a near-rigid boundary condition. This enables improved control of the liner design process. A number of additively-manufactured, bent-chamber liners are also investigated to explore the e˙ects of the bend on the liner impedance. The liner impedance is negligibly a˙ected by changes to the partition width, but is significantly a˙ected by changes to the slot height. This o˙ers an option for achieving low-frequency sound absorption with relatively thin liners.

acoustic↗
NASA NTRSDate not supplied

Evaluation of Variable-Depth Liners with Slotted Cores

An initial evaluation of variable-depth, slotted-core liners fabricated with conventional, flightworthy components is presented. Liners with two distinct chamber lengths provide significant broadband absorption and their impedances are, to first order, predictable with the NASA impedance prediction code. These liners employ film caps embedded in alternating chambers to provide a near-rigid boundary condition. This enables improved control of the liner design process. A number of additively-manufactured, bent-chamber liners are also investigated to explore the effects of the bend on the liner impedance. The liner impedance is negligibly affected by changes to the partition width, but is significantly affected by changes to the slot height. This offers an option for achieving low-frequency sound absorption with relatively thin liners.

acoustic↗
NASA NTRS2022

Quantifying and Reducing Uncertainty in Microwave Vegetation Optical Depth and Soil Moisture Retrievals

Soil moisture and vegetation optical depth (VOD; related to vegetation water content) retrieved from SMAP and SMOS satellites are widely used for a range of hydrosphere and biosphere applications. However, while soil moisture has been globally well-validated, VOD validation has been sparse. Furthermore, simultaneously retrieval of these parameters results in uncertainties both individually in soil moisture and VOD retrievals as well as in compensation between the parameters. Here, we show global locations where soil moisture and VOD retrievals will have lower uncertainty, based on complementary brightness temperature information content and signal-to-noise ratio metrics. In these same locations, we show that error still propagates more into VOD. However, using VOD regularization algorithms, this error is greatly reduced, especially at sub-weekly timescales where algorithmic error can be most apparent. Despite these regularization approaches that reduce errors, there are yet vast differences in available global regularized retrievals originating from different algorithmic choices.

Passive microwave radiometry↗
NASA NTRS2021

Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals

Satellite soil moisture and vegetation optical depth (VOD; related to the total vegetation water mass per unit area) are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in-situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on sub-weekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal.

Vegetation optical depth↗
NASA NTRS2023

Thermal Infrared Dust Optical Depth and Coarse-Mode Effective Diameter Over Oceans Retrieved From Collocated MODIS and CALIOP Observations

In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 µm (DAOD10μm) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing the dust lognormal volume particle size distribution (PSD) corresponding to retrieved Deff with the in-situ measured dust PSDs from the AER-D, SAMUM-2 and SALTRACE field campaigns through case studies. The new DAOD10μm retrievals were evaluated first through comparisons with the collocated DAOD10.6μm retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al. 2022). The pixel-to-pixel comparison of the two DAOD retrievals indicates a good agreement (R~0.7) and a significant reduction of (~50%) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (5°×2°) mean DAOD10um retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R = 0.9), Indian Ocean (IO; R = 0.8) and North Pacific (NP; R = 0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse mode dust Deff over global oceans. We found that dust Deff over IO and NP are up to 20% smaller than that over NA. Over NA in summer, we found a ~50% reduction of the number of retrievals with Deff > 5 μm from 15°W to 35°W and a stable trend of Deff average at 4.4 μm from 35°W throughout the Caribbean Sea (90°W). Over NP in spring, only ~5% of retrieved pixels with Deff > 5 μm are found from 150°E to 180°, while the mean Deff remains stable at 4.0 μm throughout eastern NP. To our best knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust long-wave radiative effects and coarse mode dust particle size in models.

dust optical depth↗
NASA NTRS2024

Retrievals of Aerosol Optical Depth Over the Western North Atlantic Ocean During ACTIVATE

Aerosol optical depth was retrieved from two airborne remote sensing instruments, the Research Scanning Polarimeter (RSP) and Second Generation High Spectral Resolution Lidar (HSRL-2), during the National Aeronautics and Space Administration (NASA) Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). The field campaign offers a unique opportunity to evaluate an extensive 3-year dataset under a wide range of meteorological conditions from two instruments on the same platform. However, a long-standing issue in atmospheric field studies is that there is a lack of reference datasets for properly validating field measurements and estimating their uncertainties. Here we address this issue by using the triple collocation method, in which a third collocated satellite dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) is introduced for comparison. HSRL-2 is found to provide a more accurate retrieval than RSP over the study region. The error standard deviation of HSRL-2 with respect to the ground truth is 0.027. Moreover, this approach enables us to develop a simple, yet efficient, quality control criterion for RSP data. The physical reasons for the differences in two retrievals are determined to be cloud contamination, aerosols near the surface, multiple aerosol layers, absorbing aerosols, non-spherical aerosols, and simplified retrieval assumptions. These results demonstrate the pathway for optimal aerosol retrievals by combining information from both lidars and polarimeters for future airborne and satellite missions.

Aerosol optical depth↗
DOE OSTI2021

A Sodium–Antimony–Telluride Intermetallic Allows Sodium-Metal Cycling at 100% Depth of Discharge and as an Anode-Free Metal Battery

In this work, repeated cold rolling and folding is employed to fabricate a metallurgical composite of sodium–antimony–telluride Na 2 (Sb 2/6 Te 3/6 Vac 1/6 ) dispersed in electrochemically active sodium metal, termed “NST-Na.” This new intermetallic has a vacancy-rich thermodynamically stable face-centered-cubic structure and enables state-of-the-art electrochemical performance in widely employed carbonate and ether electrolytes. NST-Na achieves 100% depth-of-discharge (DOD) in 1 m NaPF 6 in G2, with 15 mAh cm –2 at 1 mA cm –2 and Coulombic efficiency (CE) of 99.4%, for 1000 h of plating/stripping. Sodium-metal batteries (SMBs) with NST-Na and Na 3 V 2 (PO 4 ) 3 (NVP) or sulfur cathodes give significantly improved energy, cycling, and CE (>99%). An anode-free battery with NST collector and NVP obtains 0.23% capacity decay per cycle. Imaging and tomography using conventional and cryogenic microscopy (Cryo-EM) indicate that the sodium metal fills the open space inside the self-supporting sodiophilic NST skeleton, resulting in dense (pore-free and solid electrolyte interphase (SEI)-free) metal deposits with flat surfaces. The baseline Na deposit consists of filament-like dendrites and “dead metal”, intermixed with pores and SEI. Density functional theory calculations show that the uniqueness of NST lies in the thermodynamic stability of the Na atoms (rather than clusters) on its surface that leads to planar wetting, and in its own stability that prevents decomposition during cycling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High Depth‐of‐Discharge Zinc Rechargeability Enabled by a Self‐Assembled Polymeric Coating

Abstract Zinc has the potential for widespread use as an environmentally friendly and cost‐effective anode material pending the resolution of rechargeability issues caused by active material loss and shape change. Here, a self‐assembled Nafion‐coated Celgard 3501 (NC‐Celgard) separator is shown to enable unprecedented cycle life of a Zn anode in alkaline electrolyte at high depth‐of‐discharge (DOD Zn ). Using commercially relevant energy‐dense electrodes with high areal capacities of 60 mAh cm –2 , Zn–Ni cells tested at 20% DOD Zn cells achieve over 200 cycles while 50% DOD Zn cells achieve over 100 cycles before failure. The 20% and 50% DOD cells deliver an average of 132 and 180 Wh L –1 per cycle over their lifetime respectively. Rechargeability is attributed to the highly selective diffusion properties of the 300 nm thick negatively charged Nafion coating on the separator which prevents shorting by dendrites and inhibits redistribution of the active material. Crossover experiments show that the NC‐Celgard separator is practically impermeable to zincate ([Zn(OH) 4 ] 2– ), outperforming commercial Celgard, cellophane, Nafion 211 and 212 separators while still allowing hydroxide transport. This work demonstrates the efficacy of selective separators for increasing the cycle life of energy‐dense Zn electrodes without adding significant volume or complexity to the system.

Arnot, David J.↗