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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Mitigation of LEO Satellite Brightness and Trail Effects on the Rubin Observatory LSST

In this work, we report studies on the mitigation of optical effects of bright low-Earth-orbit (LEO) satellites on Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST). These include options for pointing the telescope to avoid satellites, laboratory investigations of bright trails on the Rubin Observatory LSST camera sensors, algorithms for correcting image artifacts caused by bright trails, experiments on darkening SpaceX Starlink satellites, and ground-based follow-up observations. The original Starlink v0.9 satellites are g ~ 4.5 mag, and the initial experiment "DarkSat" is g ~ 6.1 mag. Future Starlink darkening plans may reach g ~ 7 mag, a brightness level that enables nonlinear image artifact correction to well below background noise. However, the satellite trails will still exist at a signal-to-noise ratio ~ 100, generating systematic errors that may impact data analysis and limit some science. For the Rubin Observatory 8.4 m mirror and a satellite at 550 km, the full width at half maximum of the trail is about 3'' as the result of an out-of-focus effect, which helps avoid saturation by decreasing the peak surface brightness of the trail. For 48,000 LEOsats of apparent magnitude 4.5, about 1% of pixels in LSST nautical twilight images would need to be masked.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Galaxy Environment in the 3D-HST Fields: Witnessing the Onset of Satellite Quenching at z ∼ 1–2

We make publicly available a catalog of calibrated environmental measures for galaxies in the five 3D-Hubble Space Telescope (HST)/CANDELS deep fields. Leveraging the spectroscopic and grism redshifts from the 3D-HST survey, multiwavelength photometry from CANDELS, and wider field public data for edge corrections, we derive densities in fixed apertures to characterize the environment of galaxies brighter than JH{sub 140}<24 mag in the redshift range 0.5<z<3.0. By linking observed galaxies to a mock sample, selected to reproduce the 3D-HST sample selection and redshift accuracy, each 3D-HST galaxy is assigned a probability density function of the host halo mass, and a probability that it is a central or a satellite galaxy. The same procedure is applied to a z = 0 sample selected from Sloan Digital Sky Survey. We compute the fraction of passive central and satellite galaxies as a function of stellar and halo mass, and redshift, and then derive the fraction of galaxies that were quenched by environment specific processes. Using the mock sample, we estimate that the timescale for satellite quenching is t{sub quench}∼2--5 Gyr; it is longer at lower stellar mass or lower redshift, but remarkably independent of halo mass. This indicates that, in the range of environments commonly found within the 3D-HST sample (M{sub h}≲10{sup 14} M{sub ⊙}), satellites are quenched by exhaustion of their gas reservoir in the absence of cosmological accretion. We find that the quenching times can be separated into a delay phase, during which satellite galaxies behave similarly to centrals at fixed stellar mass, and a phase where the star formation rate drops rapidly (τ{sub f}∼0.4--0.6 Gyr), as shown previously at z = 0. We conclude that this scenario requires satellite galaxies to retain a large reservoir of multi-phase gas upon accretion, even at high redshift, and that this gas sustains star formation for the long quenching times observed.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2013

Erosion, Transportation, and Deposition on Outer Solar System Satellites: Landform Evolution Modeling Studies

Mass movement and landform degradation reduces topographic relief by moving surface materials to a lower gravitational potential. In addition to the obvious role of gravity, abrasive mechanical erosion plays a role, often in combination with the lowering of cohesion, which allows disaggregation of the relief-forming material. The identification of specific landform types associated with mass movement and landform degradation provides information about local sediment particle size and abundance and transportation processes. Generally, mass movements can be classified in terms of the particle sizes of the transported material and the speed the material moved during transport. Most degradation on outer planet satellites appears consistent with sliding or slumping, impact erosion, and regolith evolution. Some satellites, such as Callisto and perhaps Hyperion and Iapetus, have an appearance that implies that some additional process is at work, most likely sublimation-driven landform modification and mass wasting. A variant on this process is thermally driven frost segregation as seen on all three icy Galilean satellites and perhaps elsewhere. Titan is unique among outer planet satellites in that Aeolian and fluvial processes also operate to erode, transport, and deposit material. We will evaluate the sequence and extent of various landform-modifying erosional and volatile redistribution processes that have shaped these icy satellites using a 3-D model that simulates the following surface and subsurface processes: 1) sublimation and re-condensation of volatiles; 2) development of refractory lag deposits; 3) disaggregation and downward sloughing of surficial material; 4) radiative heating/cooling of the surface (including reflection, emission, and shadowing by other surface elements); 5) thermal diffusion; and 6) vapor diffusion. The model will provide explicit simulations of landform development and thusly predicts the topographic and volatile evolution of the surface and final landscape form as constrained by DEMs. We have also simulated fluvial and lacustrine modification of icy satellites landscapes to evaluate the degree to which fluvial erosion of representative initial landscapes can replicate the present Titan landscape.

erosion↗
NASA NTRS2013

Absorption Properties of Mediterranean Aerosols Obtained from Multi-year Ground-based and Satellite Remote Sensing Observations

Aerosol absorption properties are of high importance to assess aerosol impact on regional climate. This study presents an analysis of aerosol absorption products obtained over the Mediterranean Basin or land stations in the region from multi-year ground-based AERONET and satellite observations with a focus on the Absorbing Aerosol Optical Depth (AAOD), Single Scattering Albedo (SSA) and their spectral dependence. The AAOD and Absorption Angstrom Exponent (AAE) data set is composed of daily averaged AERONET level 2 data from a total of 22 Mediterranean stations having long time series, mainly under the influence of urban-industrial aerosols and/or soil dust. This data set covers the 17 yr period 1996-2012 with most data being from 2003-2011 (approximately 89 percent of level-2 AAOD data). Since AERONET level-2 absorption products require a high aerosol load (AOD at 440 nm greater than 0.4), which is most often related to the presence of desert dust, we also consider level-1.5 SSA data, despite their higher uncertainty, and filter out data with an Angstrom exponent less than 1.0 in order to study absorption by carbonaceous aerosols. The SSA data set includes both AERONET level-2 and satellite level-3 products. Satellite-derived SSA data considered are monthly level 3 products mapped at the regional scale for the spring and summer seasons that exhibit the largest aerosol loads. The satellite SSA dataset includes the following products: (i) Multi-angle Imaging SpectroRadiometer (MISR) over 2000-2011, (ii) Ozone Monitoring Instrument (OMI) near-UV algorithm over 2004-2010, and (iii) MODerate resolution Imaging Spectroradiometer (MODIS) Deep-Blue algorithm over 2005-2011, derived only over land in dusty conditions. Sun-photometer observations show that values of AAOD at 440 nm vary between 0.024 +/- 0.01 (resp. 0.040 +/- 0.01) and 0.050 +/- 0.01 (0.055 +/- 0.01) for urban (dusty) sites. Analysis shows that the Mediterranean urban-industrial aerosols appear "moderately" absorbing with values of SSA close to approximately 0.94-0.95 +/- 0.04 (at 440 nm) in most cases except over the large cities of Rome and Athens, where aerosol appears more absorbing (SSA approximately 0.89-0.90 +/- 0.04). The aerosol Absorption Angstrom Exponent (AAE, estimated using 440 and 870 nm) is found to be larger than 1 for most sites over the Mediterranean, a manifestation of mineral dust (iron) and/or brown carbon producing the observed absorption. AERONET level-2 sun-photometer data indicate the existence of a moderate East-West gradient, with higher values over the eastern basin (AAEEast. = 1.39/AAEWest. = 1.33) due to the influence of desert dust. The North-South AAE gradient is more pronounced, especially over the western basin. Our additional analysis of AERONET level-1.5 data also shows that organic absorbing aerosols significantly affect some Mediterranean sites. These results indicate that current climate models treating organics as nonabsorbing over the Mediterranean certainly underestimate the warming effect due to carbonaceous aerosols. A~comparative analysis of the regional SSA variability has been attempted using satellite data. OMI and MODIS data show an absorbing zone (SSA approximately 0.90 at 470-500 nm) over Northeastern Africa that does not appear in the MISR retrievals. In contrast, MISR seems able to observe the East-West SSA gradient during summer, as also detected by AERONET. Also, the analysis of SSA provided by satellites indicates that the aerosol over the Mediterranean Sea appears less absorbing during spring (MAM) than summer (JJA).

satellite remote sensing observations↗
NASA NTRS2020

An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation

To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1°×1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1°×1° grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

Aerosol↗
NASA NTRS2022

Satellite Data Applications for Sustainable Energy Transitions

Transitioning to a sustainable energy system poses a massive challenge to communities, nations, and the global economy in the next decade and beyond. A growing portfolio of satellite data products is available to support this transition. Satellite data complement other information sources to provide a more complete picture of the global energy system, often with continuous spatial coverage over targeted areas or even the entire Earth. We find that satellite data are already being applied to a wide range of energy issues with varying information needs, from planning and operation of renewable energy projects, to tracking changing patterns in energy access and use, to monitoring environmental impacts and verifying the effectiveness of emissions reduction efforts. While satellite data could play a larger role throughout the policy and planning lifecycle, there are technical, social, and structural barriers to their increased use. We conclude with a discussion of opportunities for satellite data applications to energy and recommendations for research to maximize the value of satellite data for sustainable energy transitions.

energy↗
NASA NTRS2025

Validation of Formaldehyde Products From Three Satellite Retrievals (OMI SAO, OMPS-NPP SAO, and OMI BIRA) in the Marine Atmosphere With Four Seasons of Atom Aircraft Observations

Formaldehyde (HCHO) in the atmosphere is an intermediate product from the oxidation of methane and non-methane volatile organic compounds. In remote marine regions, HCHO variability is closely related to atmospheric oxidation capacity and modeled HCHO in these regions is usually added as a global satellite HCHO background. Thus, it is important to understand and validate the levels of satellite HCHO over the remote oceans. Here we intercompare three satellite retrievals of total HCHO columns (OMI-SAO (v004), OMPS-NPP SAO, and OMI BIRA) and validate them against in situ observations from the NASA Atmospheric Tomography Mission (ATom) mission. All retrievals are correlated with ATom integrated columns over remote oceans, with OMI SAO (v004) showing the best agreement. This is also reflected in the mean bias (MB) for OMI SAO (-0.73±0.87) x 10 15 molec cm -2 , OMPS SAO (-0.76±0.88) x 10 15 molec cm -2 , and OMI BIRA (-1.40±1.11) x 10 15 molec cm -2 . We recommend the OMI-SAO (v004) retrieval for remote ocean atmosphere studies. Three satellite HCHO retrievals and in situ ATom columns all generally captured the spatial and seasonal distributions of HCHO in the remote ocean atmosphere. Retrieval bias varies by latitude and season, but a persistent low bias is found in all products at high latitudes and the general low bias is most severe for the OMI BIRA product. Examination of retrieval components reveals slant column corrections have a larger impact on the retrievals over remote marine regions while AMFs play a smaller role. This study informs that the potential latitude-dependent biases in the retrievals require further investigation for improvement and should be considered when using marine HCHO satellite data, and vertical profiles from in situ instruments are crucial for validating satellite retrievals.

Atom↗
NASA NTRS2026

Using Satellite Surface Temperature Data to Monitor Urban Heat Island

Exposure to heat is a growing concern nationwide. Temperatures can be elevated in cities compared to surrounding rural areas, referred to as an “urban heat island” (UHI) effect, which is intensified during heat waves. The lack of dense networks of air temperature measurements results in few studies on urban heat. Now, a vast amount of high spatial and temporal satellite data on land surface temperature is available. We identified satellite datasets with the longest surface temperature records but different spatial and temporal resolutions: Landsat (1985-current, biweekly at 60 m and 100 m spatial resolution) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites (2000-current, daily at 1 km spatial resolution). We investigated how satellites with different spatial and temporal resolutions detect UHI effects differently. We hypothesized that 1) a dataset’s spatial resolution impacts the precision of detected UHIs spatially, since high spatial resolution Landsat data better captures spatial variability in temperature, and 2) daily surface temperature data can detect temporal patterns of UHIs and heatwave frequencies. We analyzed Landsat and MODIS satellite data in the Washington, D.C. and Baltimore region. We found that Landsat describes higher spatial variability of the UHI effect than MODIS data. However, MODIS data shows more consistent seasonal surface temperature patterns than Landsat when compared to in situ air temperature measurements. MODIS data was also able to consistently measure the frequency of heat waves. This study demonstrates the value of NASA satellite data for urban heat and climate change studies.

landsat↗
DOE OSTI2026

Improving coastal water level estimation by merging nadir-only satellite altimetry data into a hydrodynamic model

Providing robust real time flood warnings is of paramount importance to coastal communities. Although state-of-the-art hydrodynamic models are capable of robustly predicting Coastal Water Levels (CWL), unresolved drivers affecting level fluctuations are often not represented by the model governing equations. This work evaluates a novel method to improve the performance of the ADvanced CIRCulation (ADCIRC) hydrodynamic model by assimilating observations from four nadir-only satellite altimetry missions against a set of National Oceanic and Atmospheric Administration (NOAA) gauge stations located across the entire U.S. East Coast. Two different types of simulations were performed – Open Loop (OL) and Data Assimilation (DA). Five different simulations were performed where four different satellite altimetry observations were assimilated individually and combined with two different scenarios – with and without considering the data quality flags. Results indicate that, despite their limited spatial coverage, merging nadir-only observations into ADCIRC from the newly launched Surface Water and Ocean Topography (SWOT)’s nadir altimeter can improve the model performance at 76% of the gauge locations, whereas Sentinel-6 improves 73% of the total locations, Jason-3 74%, and SARAL 21%. Furthermore, combining observations from SWOT-nadir, Jason-3, and Sentinel-6 can improve the ADCIRC performance at more than 80% of the gauge locations for 107-day simulation. Nadir-only satellite altimetry observations can be useful for improving the model performance even if flagged as “poor quality” near the coast. When the flagged data are disregarded, SWOT can improve ADCIRC at 78%, Sentinel-6 at 73%, Jason-3 at 53%, and SARAL at 21% of the gauge locations. The ability to improve the model simulations largely depends on the availability of a satellite overpass nearby. Therefore, model performance can be further enhanced if satellite observations are available during a storm surge event, stressing the importance of frequent satellite overpasses.

Aafnan Bhuiyan, Soelem↗
DOE OSTI2021

How much information on precipitation is contained in satellite infrared imagery?

Satellite infrared (IR) brightness temperature providing information on the characteristics of cloud tops is one of the primary input data used for estimating precipitation from satellites. Thus, the natural questions to ask are: Does IR imagery contain more information about precipitation in some regions more than others? What are the factors that lead to such differences? Should we expect the amount of information provided by IR imagery to change with respect to temporal or spatial aggregation? Comprehensive analysis of the accuracy of satellite-based precipitation estimates obtained from any given algorithm may provide qualitative answers to the above questions. However, such an approach only offers conclusions within the limited context of the algorithm used to obtain precipitation estimates; that is, patterns caused by the inherent information content of IR imagery are inextricable from those induced by assumptions embedded in the precipitation estimation algorithm. Here, we address the abovementioned questions from a different perspective using an information-theoretic measure, free of structural assumptions and general to a wide range of relationships, to characterize the average and seasonal dependence between IR imagery provided by satellite sensors in the spectral range (10.7–11.5 μm) and precipitation rates in the contiguous United States (CONUS) across distinct temporal and spatial scales. We analyze a total of more than 1.3 billion pairs of IR and precipitation observations over CONUS, and we observe interesting patterns. First, we show that there is a strong inverse relationship (ρ = - 0.73) between the information content of IR data and the number of no-rain observations; however, its robustness varies regionally and seasonally with less significant correlation during the warm season. Second, we demonstrate that the intuitive relationship of increasing dependence between IR and precipitation as a result of temporal or spatial aggregation exhibits a diminishing returns behavior. For instance, temporal aggregation from 1 to 3 h increases the dependence approximately 7 times as much as temporal aggregation from 12 to 24 h. Furthermore, we quantitatively examine and re-confirm several statements previously reported in the literature such as the strong association between IR and precipitation in convective storms and the low association in orographic rainfall. Finally, we conclude by illustrating the potential of the analysis in diagnosis of operational algorithms for estimating precipitation from IR brightness temperature. The findings of this study pinpoint spatial domains and time scales at which IR is not an adequate proxy for estimating precipitation. Thus, they can potentially guide the development of operational algorithms that utilize satellite Infrared imagery for estimating, downscaling and data fusion of precipitation. Finally, the analysis presented here opens up the possibility of developing new methodologies for diagnosis of satellite-based precipitation estimation algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

Highlights: • We assessed how well a year-2006 satellite-based LUR model captures historical NO{sub 2}. • We used three methods to estimate annual mean NO{sub 2} during 1990–2005. • We measured their performance using standard LUR validation techniques. • Back-extrapolated 2006 levels captured up to 76% of spatial variability (90–05). Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006–2011, could capture nitrogen dioxide (NO{sub 2}) concentrations during 1990–2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO{sub 2} predictions: (1) ‘do nothing’ (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable ‘year’ in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006–2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R{sup 2} and mean-square error R{sup 2} (MSE-R{sup 2}), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R{sup 2} = 31%) and 80% (2003; MSE-R{sup 2} = 78%) of spatial variability in NO{sub 2} in a given year, and 76% (MSE-R{sup 2} = 72%) averaged over 1990–2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO{sub 2} estimates for Australia during 1990–2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The Diurnal Dynamics of Gross Primary Productivity Using Observations From the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an Oak Savanna Ecosystem

Gross primary productivity (GPP) is the largest flux in the global carbon cycle and satellite-based GPP estimates have long been used to study the trends and interannual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar-orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite-R series (GOES-R) to test the ability of subdaily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We compared three methods to estimate GPP: (a) a light-use efficiency model, (b) a linear relationship between the product of near-infrared reflectance of vegetation and photosynthetically active radiation (LIN-NIR v P) and EC tower GPP, and (c) a light response curve (LRC-NIR v P) between NIR v P and EC GPP. The LRC-NIR v P achieved the lowest mean absolute error for winter (2 µmol CO 2 m –2 s –1 ), spring (2.51 µmol CO 2 m –2 s –1 ), summer (1.43 µmol CO 2 m –2 s –1 ), and fall (1.35 µmol CO 2 m –2 s –1 ). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation toward the morning hours during the dry summers. The LRC-NIRvP and the light-use efficiency model were in agreement with these patterns of a shift in peak daily GPP toward the morning hours during summer. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Can We Rely on Satellite Visible/Infrared Microphysical Retrievals of Boundary Layer Clouds in Partially Cloudy Scenes? Implications for Climate Research

This study addresses the longstanding question of the reliability of gridded visible/infrared satellite cloud properties in partially cloudy scenes. By using in-situ cloud probes and airborne Research Scanning Polarimeter (RSP) observations, we analyze bias changes in satellite retrievals from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) geostationary sensor during the ORACLES campaign. Biases in cloud optical depth (τ) and droplet effective radius (r e ) modestly change for cloud area fraction greater than 35%. The agreement between SEVIRI and RSP r e substantially improves when the retrievals are averaged after removing pixels with τ < 3.0, yielding biases indistinguishable from overcast scenes. In addition, satellite and RSP show an excellent agreement for closed- and open-cell stratocumulus clouds, showing that the satellite retrievals capture spatial changes of r e , and confirming that satellites can faithfully reproduce real physical features for optically thick and partially cloudy scenes. We demonstrate that a simple methodology can minimize uncertainties in satellite-based climate studies.

Painemal, David [NASA Langley Research Center, Ham↗
DOE OSTI2015

RESONANCES, CHAOS, AND SHORT-TERM INTERACTIONS AMONG THE INNER URANIAN SATELLITES

The Portia group of Uranian satellites, representing 9 of the planet’s 13 tiny innermost moons, form a densely packed dynamical system. Hubble Space Telescope observations indicate that their orbits have changed significantly over two decades, and long-term numerical integrations show that their orbits are unstable over millions of years. To investigate the dynamical interactions of the Portia group satellites on the decade timescale over which orbital changes have been observed, we have performed a suite of 100–1000 yr N-body numerical integrations for a range of assumed satellite masses, which are at present not tightly constrained by observations. As first noted by Dawson et al. and recently investigated independently by Quillen and (Robert) French, the moons are configured in chains of interlinked first- and second-order eccentric resonances that contribute to chaotic behavior. We explore in detail several of the strongest of these interlinked resonances. The first such chain is a quintet of orbital resonances: Bianca is near a resonance with Cressida, which is itself near a resonance with Desdemona. Desdemona, in turn, is near a resonance with Portia, which is itself near a resonance with Juliet. The five participating resonances are: Cressida and Bianca (16:15), Desdemona and Cressida (47:46), Portia and Desdemona (13:12), and Portia and Juliet (51:49). A second such chain is a set of two interlinked resonances: Cupid and Belinda (58:57) and Belinda and Perdita (44:43). We also report the new identification of a companion set of second-order inclination-type resonances (Cressida and Bianca (32:30), Desdemona and Cressida (94:92), Portia and Desdemona (26:24), Portia and Juliet (51:49), Cupid and Belinda (116:114), and Belinda and Perdita (88:86)), some of which result in quite strongly coupled variations in the inclinations of the interacting satellites. Using a robust formulation of orbital elements that accounts for the oblateness of Uranus, we probe the dynamical interactions among the moons in the time and frequency domains, and also in phase space, using numerical integrations of subsets of the inner moons, for a range of assumed masses. Several of the satellites are near mean-motion resonance with more than one neighbor, and undergo orbital variations at two nearly equal resonant frequencies. Such configurations of two interlinked resonances can result in chaotic behavior, associated with the transition of one resonant argument from circulation to libration. We demonstrate that, even on the short timescales investigated here, the dynamical interactions, onset of chaos, and associated Lyapunov times are highly sensitive to the masses of the interacting satellites.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

THE COSMIC EVOLUTION OF FAINT SATELLITE GALAXIES AS A TEST OF GALAXY FORMATION AND THE NATURE OF DARK MATTER

The standard cosmological model based on cold dark matter (CDM) predicts a large number of subhalos for each galaxy-size halo. Matching the subhalos to the observed properties of luminous satellites of galaxies in the local universe poses a significant challenge to our understanding of the astrophysics of galaxy formation. We show that the cosmic evolution and host mass dependence of the luminosity function of satellites provide a powerful new diagnostic to disentangle astrophysical effects from variations in the underlying dark matter mass function. We illustrate this by comparing recent observations of satellites between redshifts 0.1 < z < 0.8 based on Hubble Space Telescope images, with predictions from three different state-of-the-art semi-analytic models applied to CDM power spectra, with one model also applied to a warm dark matter (WDM) spectrum. We find that even though CDM models provide a reasonable fit to the local luminosity function of satellites around galaxies comparable to the Milky Way, they do not reproduce the data as well for different redshifts and host galaxy stellar masses, indicating that further improvements in the description of star formation are likely needed. The WDM model matches the observed mass dependence and redshift evolution of satellite galaxies more closely, indicating that a modification of the underlying power spectrum may offer an alternative solution to this tension. We conclude by presenting predictions for the color distribution of satellite galaxies to demonstrate how future observations will be able to further distinguish between these models and to help constrain baryonic and non-baryonic physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

THE ZURICH ENVIRONMENTAL STUDY (ZENS) OF GALAXIES IN GROUPS ALONG THE COSMIC WEB. II. GALAXY STRUCTURAL MEASUREMENTS AND THE CONCENTRATION OF MORPHOLOGICALLY CLASSIFIED SATELLITES IN DIVERSE ENVIRONMENTS

We present structural measurements for the galaxies in the 0.05 < z < 0.0585 groups of the Zurich Environmental Study, aimed at establishing how galaxy properties depend on four environmental parameters: group halo mass (M{sub GROUP}), group-centric distance (R/R{sub 200}), ranking into central or satellite, and large-scale structure density (δ{sub LSS}). Global galaxy structure is quantified both parametrically and non-parametrically. We correct all these measurements for observational biases due to point-spread function blurring and surface brightness effects as a function of galaxy size, magnitude, steepness of light profile, and ellipticity. Structural parameters are derived also for bulges, disks, and bars. We use the galaxy bulge-to-total ratios (B/T) together with the calibrated non-parametric structural estimators to implement a quantitative morphological classification that maximizes purity in the resulting morphological samples. We investigate how the concentration C of satellite galaxies depends on galaxy mass for each Hubble type and on M{sub GROUP}, R/R{sub 200}, and δ{sub LSS}. At galaxy masses M ≥ 10{sup 10} M{sub ☉}, the concentration of disk satellites increases with increasing stellar mass separately within each morphological bin of B/T. The known increase in concentration with stellar mass for disk satellites is thus due, at least in part, to an increase in galaxy central stellar density at constant B/T. The correlation between concentration and galaxy stellar mass becomes progressively steeper for later morphological types. The concentration of disk satellites shows a barely significant dependence on δ{sub LSS} or R/R{sub 200}. The strongest environmental effect is found with group mass for >10{sup 10} M{sub ☉} disk-dominated satellites, which are ∼10% more concentrated in high mass groups than in lower mass groups.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Capture of irregular satellites at Jupiter

The irregular satellites of outer planets are thought to have been captured from heliocentric orbits. The exact nature of the capture process, however, remains uncertain. We examine the possibility that irregular satellites were captured from the planetesimal disk during the early solar system instability when encounters between the outer planets occurred. Nesvorný et al. already showed that the irregular satellites of Saturn, Uranus, and Neptune were plausibly captured during planetary encounters. Here we find that the current instability models present favorable conditions for capture of irregular satellites at Jupiter as well, mainly because Jupiter undergoes a phase of close encounters with an ice giant. We show that the orbital distribution of bodies captured during planetary encounters provides a good match to the observed distribution of irregular satellites at Jupiter. The capture efficiency for each particle in the original transplanetary disk is found to be (1.3-3.6) × 10{sup –8}. This is roughly enough to explain the observed population of jovian irregular moons. We also confirm Nesvorný et al.'s results for the irregular satellites of Saturn, Uranus, and Neptune.

79 ASTRONOMY AND ASTROPHYSICS↗