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DOE OSTI2021

Size, shade, or shape? The contribution of galaxies of different types to the star formation history of the Universe from SDSS-IV MaNGA

ABSTRACT By fitting stellar populations to the fourth generation of the Sloan Digital Sky Survey (SDSS-IV) Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey observations of ∼7000 suitably weighted individual galaxies, we reconstruct the star formation history of the Universe, which we find to be in reasonable agreement with previous studies. Dividing the galaxies by their present-day stellar mass, we demonstrate the downsizing phenomenon, whereby the more massive galaxies hosted the most star formation at earlier times. Further dividing the galaxy sample by colour and morphology, we find that a galaxy’s present-day colour tells us more about its historical contribution to the cosmic star formation history than its current morphology. We show that downsizing effects are greatest among galaxies currently in the blue cloud, but that the level of downsizing in galaxies of different morphologies depends quite sensitively on the morphological classification used, due largely to the difficulty in classifying the smaller low-mass galaxies from their ground-based images. Nevertheless, we find agreement that among galaxies with stellar masses $M_{\star } \gt 6\times 10^{9}\, \mathrm{ M}_{\odot }$, downsizing is most significant in spirals. However, there are complicating factors. For example, for more massive galaxies, we find that colour and morphology are predictors of the past star formation over a longer time-scale than in less massive systems. Presumably this effect is reflecting the longer period of evolution required to alter these larger galaxies’ physical properties, but shows that conclusions based on any single property do not tell the full story.

Peterken, Thomas↗

DOE OSTI2022

Aging of galaxies along the morphological sequence, marked by bulge growth and disk quenching

Aims: We revisit the color bimodality of galaxies using the extensive EFIGI morphological classification of nearby galaxies. Methods: The galaxy profiles from the Sloan Digital Sky Survey (SDSS) gri images were decomposed as a bulge and a disk by controlled profile modeling with the Euclid SourceXtractor++ software. The spectral energy distributions from our resulting gri SDSS photometry complemented with Galaxy Evolution Explorer (GALEX) NUV photometry were fitted with the ZPEG software and PEGASE.2 templates in order to estimate the stellar masses and specific star formation rates (sSFR) of whole galaxies as well as their bulge and disk components. Results: The absolute NUV-r color versus stellar mass diagram shows a continuous relationship between the present sSFR of galaxies and their stellar mass, which spans all morphological types of the Hubble sequence monotonously. Irregular galaxies to intermediate-type Sab spirals make up the “Blue Cloud” across 4 orders of magnitude in stellar mass but a narrow range of sSFR. This mass build-up of spiral galaxies requires major mergers, in agreement with their frequently perturbed isophotes. At high mass, the Blue Cloud leads to the “Green Plain”, dominated by S0a and Sa early-type spirals. It was formerly called the “Green Valley”, due to its low density, but we rename it because of its wide stretch and nearly flat density over ~2 mag in NUV-r color (hence sSFR), despite a limited range of stellar mass (1 order of magnitude). The Green Plain links up the “Red Sequence”, containing all lenticular and elliptical galaxies with a 2 order of magnitude mass interval, and systematically higher masses for the ellipticals. We confirm that the Green Plain cannot be studied using u - r optical colors because it is overlayed by the Red Sequence, hence NUV data are necessary. Galaxies across the Green Plain undergo a marked growth by a factor 2 to 3 in their bulge-to-total mass ratio and a systematic profile change from pseudo to classical bulges, as well as a significant reddening due to star formation fading in their disks. The Green Plain is also characterized by a maximum stellar mass of 10 11.7 M ⊙ beyond which only elliptical galaxies exist, hence supporting the scenario of ellipticals partly forming by major mergers of massive disk galaxies. Conclusions: The EFIGI attributes indicate that dynamical processes (spiral arms and isophote distortions) contribute to the scatter of the Main Sequence of star-forming galaxies (Blue Cloud), via the enhancement of star formation (flocculence, HII regions). The significant bulge growth across the Green Plain confirms that it is a transition region, and excludes a predominantly quick transit due to rapid quenching. The high frequency of bars for all spirals as well as the stronger spiral arms and flocculence in the knee of the Green Plain suggest that internal dynamics, likely triggered by flybys or (mainly minor) mergers, may be the key to the bulge growth of massive disk galaxies, which is a marker of the aging of galaxies from star forming to quiescence. The Hubble sequence can then be considered as an inverse sequence of galaxy physical evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1994

Amazon Rain Forest Classification Using J-ERS-1 SAR Data

The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.

J-ERS-1↗

NASA NTRS1992

Measurements of the backscattering phase matrices of crystal clouds with a polarization lidar

A polarization technique based on measurements of intensities of the polarized I(parallel) and crosspolarized I(perpendicular) components of scattered radiation is often used in lidar studies of aerosols. The ratio I(perp)/I(para) is related to nonsphericity of scattering particles and it is often called, though unjustified, depolarization. Correct definition of the term has previously been shown to be the value d = 1 - P, where P is the degree of polarization defined in terms of the Stokes parameters. In fact, measurements of I(para) and I(perp) enable one to determine the Stokes parameter Q = I(para) - I(perp), and under the condition U = V = 0 the depolarization is determined by the relationship d = 2 x I(perp)/(I(para) + I(perp)). In 1988 a new cycle of measuring the Stokes parameters of lidar returns from scattering media irradiated with a linearly polarized light was started. Based on the lidar data obtained during the 1988-1990 period, a classification of scattering ensembles was made which reveals five types of the scattering particle ensembles differing by a combination of the Stokes parameters. In the 1990-1991 period, a cycle of measurements of the lidar returns' Stokes parameters was carried out using sounding radiation.

Kaul, B. V.↗

NASA NTRS2001

Resolution Enhancement of Spaceborne Radiometer Images

Our progress over the last year has been along several dimensions: 1. Exploration and understanding of Earth Observatory System (EOS) mission with available data from NASA. 2. Comprehensive review of state of the art techniques and uncovering of limitations to be investigated (e.g. computational, algorithmic ...). and 3. Preliminary development of resolution enhancement algorithms. With the advent of well-collaborated satellite microwave radiometers, it is now possible to obtain long time series of geophysical parameters that are important for studying the global hydrologic cycle and earth radiation budget. Over the world's ocean, these radiometers simultaneously measure profiles of air temperature and the three phases of atmospheric water (vapor, liquid, and ice). In addition, surface parameters such as the near surface wind speed, the sea surface temperature, and the sea ice type and concentration can be retrieved. The special sensor microwaves imager SSM/I has wide application in atmospheric remote sensing over the ocean and provide essential inputs to numerical weather-prediction models. SSM/I data has also been used for land and ice studies, including snow cover classification measurements of soil and plant moisture contents, atmospheric moisture over land, land surface temperature and mapping polar ice. The brightness temperature observed by SSM/I is function of the effective brightness temperature of the earth's surface and the emission scattering and attenuation of the atmosphere. Advanced Microwave Scanning Radiometer (AMSR) is a new instrument that will measure the earth radiation over the spectral range from 7 to 90 GHz. Over the world's ocean, it will be possible to retrieve the four important geographical parameters SST, wind speed, vertically integrated water vapor, vertically integrated cloud liquid water L.

Krim, Hamid↗

NASA NTRS2006

Percentage Contributions from Atmospheric and Surface Features to Computed Brightness Temperatures

Over the past few years, there has become an increasing interest in the use of millimeter-wave (mm-wave) and sub-millimeter-wave (submm-wave) radiometer observations to investigate the properties of ice particles in clouds. Passive radiometric channels respond to both the integrated particle mass throughout the volume and field of view, and to the amount, location, and size distribution of the frozen (and liquid) particles with the sensitivity varying for different frequencies and hydrometeor types. One methodology used since the 1960's to discern the relationship between the physical state observed and the brightness temperature (TB) is through the temperature weighting function profile. In this research, the temperature weighting function concept is exploited to analyze the sensitivity of various characteristics of the cloud profile, such as relative humidity, ice water path, liquid water path, and surface emissivity. In our numerical analysis, we compute the contribution (in Kelvin) from each of these cloud and surface characteristics, so that the sum of these various parts equals the computed TB. Furthermore, the percentage contribution from each of these characteristics is assessed. There is some intermingling/contamination of the contributions from various components due to the integrated nature of passive observations and the absorption and scattering between the vertical layers, but all in all the knowledge gained is useful. This investigation probes the sensitivity over several cloud classifications, such as cirrus, blizzards, light snow, anvil clouds, and heavy rain. The focus is on mm-wave and submm-wave frequencies, however discussions of the effects of cloud variations to frequencies as low as 10 GHz and up to 874 GHz will also be presented. The results show that nearly 60% of the TB value at 89 GHz comes from the earth's surface for even the heaviest blizzard snow rates. On the other hand, a significant percentage of the TB value comes from the snow in the cloud for 166, and 183 plus or minus 7 GHz for the heavy and medium snow rates. For submm-wave channels, there is no contribution from the surface because these channels cannot probe through clouds, nor normal water vapor amounts in clear air regions. This work is extremely valuable in physically-based retrieval algorithm development research.

Gail Skofronick-Jackson↗

NASA NTRS2014

The 183-WSL Fast Rain Rate Retrieval Algorithm. Part II: Validation Using Ground Radar Measurements

The Water vapour Strong Lines at 183 GHz (183-WSL) algorithm is a method for the retrieval of rain rates and precipitation type classification (convectivestratiform), that makes use of the water vapor absorption lines centered at 183.31 GHz of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15-18 and NOAA-19Metop-A satellite series, respectively. The characteristics of this algorithm were described in Part I of this paper together with comparisons against analogous precipitation products. The focus of Part II is the analysis of the performance of the 183-WSL technique based on surface radar measurements. The ground truth dataset consists of 2.5 years of rainfall intensity fields from the NIMROD European radar network which covers North-Western Europe. The investigation of the 183-WSL retrieval performance is based on a twofold approach: 1) the dichotomous statistic is used to evaluate the capabilities of the method to identify rain and no-rain clouds; 2) the accuracy statistic is applied to quantify the errors in the estimation of rain rates.The results reveal that the 183-WSL technique shows good skills in the detection of rainno-rain areas and in the quantification of rain rate intensities. The categorical analysis shows annual values of the POD, FAR and HK indices varying in the range 0.80-0.82, 0.330.36 and 0.39-0.46, respectively. The RMSE value is 2.8 millimeters per hour for the whole period despite an overestimation in the retrieved rain rates. Of note is the distribution of the 183-WSL monthly mean rain rate with respect to radar: the seasonal fluctuations of the average rainfalls measured by radar are reproduced by the 183-WSL. However, the retrieval method appears to suffer for the winter seasonal conditions especially when the soil is partially frozen and the surface emissivity drastically changes. This fact is verified observing the discrepancy distribution diagrams where2the 183-WSL performs better during the warm months, while during the winter time the discrepancies with radar measurements tends to maximum values. A stable behavior of the 183-WSL algorithm is demonstrated over the whole study period with an overall overestimation for rain rates intensities lower than 1 millimeter per hour. This threshold is crucial especially in wintertime where the low precipitation regime is difficult to be classified.

validationradar↗

DOE OSTI2022

ShupeTurner cloud microphysics

This product is the ShupeTurner cloud microphysics product derived for the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. The product includes time-height derivations of the cloud type (phase type) and the condensed water content and characteristic effective particle size for liquid and ice hydrometeor populations. Additionally, it includes the vertical integral of the condensed water content for liquid and ice, i.e., the liquid water path and ice water path. These are derived using a combination of sensors, including: cloud radar, depolarization lidar, microwave radiometer, ceilometer, and radiosondes. During MOSAiC, these sensors were installed and operated onboard the Polarstern icebreaker while within the Arctic sea ice. Most data were obtained while Polarstern was moored to the MOSAiC sea ice floe, passively drifting through the Arctic. However, the data from 16 May 2020 - 17 June 2020 and 31 July 2020 - 21 August 2020 were obtained while Polarstern was underway transiting through the sea ice during relocations of the expedition. General details of the retrieval algorithm, its application, and uncertainties are provided in Shupe et al. (2015), with the cloud classification being described in more detail in Shupe (2007). General details about the MOSAiC expedition including instruments, locations, and meteorological context is provided in Shupe et al. (2022).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Characteristic Atmospheric Radiative Heating Rate Profiles in Arctic Clouds as Observed at Barrow, Alaska

Abstract A 2-yr cloud microphysical property dataset derived from ground-based remote sensors at the Atmospheric Radiation Measurement site near Barrow, Alaska, was used as input into a radiative transfer model to compute radiative heating rate (RHR) profiles in the atmosphere. Both the longwave (LW; 5–100 μm) and shortwave (SW; 0.2–5 μm) RHR profiles show significant month-to-month variability because of seasonal dependence in the vertical profiles of cloud liquid and ice water contents, with additional contributions from the seasonal dependencies of solar zenith angle, water vapor amount, and temperature. The LW and SW RHR profiles were binned to provide characteristic profiles as a function of cloud type and liquid water path (LWP). Single-layer liquid-only clouds are shown to have larger (10–30 K day−1) LW radiative cooling rates at the top of the cloud layer than single-layer mixed-phase clouds; this is due primarily to differences in the vertical distribution of liquid water between the two classes. However, differences in SW RHR profiles at the top of these two classes of clouds are less than 3 K day−1. The absolute value of the RHR in single-layer ice-only clouds is an order of magnitude smaller than in liquid-bearing clouds. Furthermore, for double-layer cloud systems, the phase and condensed water path of the upper cloud strongly modulate the radiative cooling both at the top and within the lower-level cloud. While sensitivity to cloud overlap and phase has been shown previously, the characteristic RHR profiles are markedly different between the different cloud classifications.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

Effects of weather on the retrieval of sea ice concentration and ice type from passive microwave data

Effects of wind, water vapor, and cloud liquid water on ice concentration and ice type calculated from passive microwave data are assessed through radiative transfer calculations and observations. These weather effects can cause overestimates in ice concentration and more substantial underestimates in multi-year ice percentage by decreasing polarization and by decreasing the gradient between frequencies. The effect of surface temperature and air temperature on the magnitudes of weather-related errors is small for ice concentration and substantial for multiyear ice percentage. The existing weather filter in the NASA Team Algorithm addresses only weather effects over open ocean; the additional use of local open-ocean tie points and an alternative weather correction for the marginal ice zone can further reduce errors due to weather. Ice concentrations calculated using 37 versus 18 GHz data show little difference in total ice covered area, but greater differences in intermediate concentration classes. Given the magnitude of weather-related errors in ice classification from passive microwave data, corrections for weather effects may be necessary to detect small trends in ice covered area and ice type for climate studies.

Maslanik, J. A.↗

DOE OSTI2022

Three-Dimensional Convective–Stratiform Echo-Type Classification and Convectivity Retrieval from Radar Reflectivity

The Echo Classification from COnvectivity (ECCO) algorithm identifies convective and stratiform types of radar echo in three dimensions. It is based on the calculation of reflectivity texture—a combination of the intensity and the heterogeneity of the radar echoes on each horizontal plane in a 3D Cartesian volume. Reflectivity texture is translated into convectivity, which is designed to be a quantitative measure of the convective nature of each 3D radar grid point. It ranges from 0 (100% stratiform) to 1 (100% convective). By thresholding convectivity, a more traditional qualitative categorization is obtained, which classifies radar echoes as convective, mixed, or stratiform. In contrast to previous algorithms, these echo-type classifications are provided on the full 3D grid of the reflectivity field. The vertically resolved classifications, in combination with temperature data, allow for subclassifications into shallow, mid-, deep, and elevated convective features, and low, mid-, and high stratiform regions—again in three dimensions. The algorithm was validated using datasets collected over the U.S. Great Plains during the PECAN field campaign. An analysis of lightning counts shows ~90% of lightning occurring in regions classified as convective by ECCO. A statistical comparison of ECCO echo types with the well-established GPM radar precipitation-type categories show 84% (88%) of GPM stratiform (convective) echo being classified as stratiform (convective) or mixed by ECCO. ECCO was applied to radar grids for the continental United States, the United Arab Emirates, Australia, and Europe, illustrating its robustness and adaptability to different radar grid characteristics and climatic regions.

Radars/Radar observations↗

DOE OSTI2022

eROSITA study of the globular cluster 47 Tucanae

Here, we present the results of the analysis of five observations of the globular clutser 47 Tucanae (47 Tuc) with the extended Roentgen Survey with an Imaging Telescope Array (eROSITA) on board the Spektrum-Roentgen-Gamma (Spektr-RG, SRG). We study the X-ray population in the field of one of the most massive globular clusters in our Milky Way. We focused on the classification of point-like sources in the field of 47 Tuc. The unresolved dense core of 47 Tuc (1.7 radius) and also sources that show extended emission are excluded from this study. We applied different methods of X-ray spectral and timing analysis together with multi-wavelength studies to classify the X-rays sources in the field of 47 Tuc. We detected 888 point-like sources in the energy range of 0.2–5.0 keV. We identified 126 background active galactic nuclei and 25 foreground stars. One of the foreground stars is classified as a variable M dwarf. We also classified 14 X-ray sources as members of 47 Tuc, including one symbiotic star, two quiescent low-mass X-ray binaries, and four cataclysmic variables. There are also five X-ray sources that can either be a cataclysmic variable or a contact binary, and also one X-ray source can be an active binary (type RS CVn). We identified one X-ray binary that belongs to the Small Magellanic Cloud. Moreover, we calculated the X-ray luminosity function of 47 Tuc. No significant population that seems to belong to the globular cluster is observed in the energy range of 0.5–2.0 keV using eROSITA observations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

High Semi-Volatile Organic Aerosol Contributions Associated with Ammonium Nitrate in the Coastal Urban Environment

Organic components often contribute 50% or more of the submicron aerosol mass in coastal urban environments, but their partitioning between the gas and particle phases is controlled by a complex mixture of unidentified organic compounds that are poorly constrained by observations. This study compares daily filter organic functional groups (OFG) with online organic mass fragments from La Jolla, California, as part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), quantifying the contributions of four types of non-volatile (NV) organic emission sources to the submicron composition. Daily filters retained only 0.79–0.98 µg/m3 NV submicron organic mass concentration, even though 1.8–1.9 µg/m3 non-refractory (NR) submicron organic mass concentration was measured online. The 62–64% of measured NR submicron organic mass concentration that exceeded what remained on the filters after 23-hr sampling is interpreted as semi-volatile, consistent with the moderate correlation of the NR NV difference to NR ammonium, NR nitrate, and biomass burning-related NV and NR organic factors. The association between semi-volatile organic components and ammonium nitrate likely results from both co-emission and co-evaporation. Size-resolved filter analysis showed that NV organic mass concentration accounts for 68% of NR organic mass concentration for 0.5–1 µm dry diameter but account for 9.0% for 0.18–0.5 µm dry diameter showing the differences in volatility between particle modes. Importantly, volatility of organic components was size-dependent Information Classification: General and associated with ammonium nitrate and biomass burning, providing guidance for constraining atmospheric aerosol properties in global models.

Pelayo, Christian↗

NASA NTRS2018

Integrating Cloud-Based Workflows in Continental-Scale Cropland Extent Classification

Accurate information on cropland spatial distribution is required for global-scale assessments and agricultural land use policies. Cloud computing platforms such as Google Earth Engine (GEE) provide unprecedented opportunities for large-scale classifications of Landsat data. We developed a novel method to fuse pixel-based random forest classification of continental-scale Landsat data on GEE and an object-based segmentation approach known as recursive hierarchical segmentation (RHSeg). Using our fusion method, we produced a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010. The total cropland area for North America was estimated at 275.18 million hectares (Mha). The overall accuracies of the map are>90% across the continent. This map also compares well with the United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-food Canada (AAFC) annual crop inventory (ACI), and the Mexican government agency Servicio de Informacion Agroalimentaria y Pesquera (SIAP)'s agricultural boundaries. Furthermore, our map compared well with sub-country statistics including state-wise and county-wise cropland statistics in regression models resulting in R2 > 0.84. This key contribution paves the way for more detailed products such as crop intensity, crop type, and crop irrigation, and provides a method for creating high-resolution cropland extent maps for other countries where spatial information about croplands are not as prevalent.

Massey, Richard↗

DOE OSTI2019

Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ~343 km 2 area, and a high-resolution (5 m) vegetation classification map was generated. An unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters. We employed a quantitative method to add supervision to the unlabeled clusters, producing a fully labeled vegetation map. We then developed convolutional neural networks (CNNs) using the multi-sensor fusion datasets to map vegetation distributions using the original classes and the classes produced by the unsupervised classification method. To validate the resulting CNN maps, vegetation observations were collected at 30 field plots during the summer of 2016, and the resulting vegetation products developed were evaluated against them for accuracy. Our analysis indicates the CNN models based on the labels produced by the unsupervised classification method provided the most accurate mapping of vegetation types, increasing the validation score (i.e., precision) from 0.53 to 0.83 when evaluated against field vegetation observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

The Earth Observing System (EOS)

The Earth Observing System is a US National Aeronautics and Space Administration (NASA) program consisting of a science segment, a data system, and a space segment made up of a series of polar-orbiting and mid-inclination satellites for long-term monitoring of the Earth as an integrated system, including observations of the land surface, biosphere, atmosphere, cryosphere, and oceans. Initially conceived in the mid-late 1980s, it was implemented as a series of large "flagship" missions and smaller focused satellites, often in partnership with instruments and sometimes spacecraft from other nations. In 1984 NASA formed several instrument panels to develop concepts for “facility instruments” that would provide comprehensive support to the scientific and applications’ communities, each with a supporting science team competitively selected via peer-review. An Announcement of Opportunity was released in 1988 and proposals were solicited for three types of proposals: (i) instrument investigations that would include the provision of instrumentation for flight on a polar platform, including non-Earth science payloads requiring polar orbits (PI instruments), (ii) research facility instrument team member and team leader investigations for facility instruments, and (iii) interdisciplinary science investigations to provide data analysis and modeling, preparing to use data acquired from the space platforms. The initial selections made in 1989 included support for 24 instrument investigations, 6 research facility instrument investigations, and 28 interdisciplinary investigations. Although considerable changes were made over the years in the structure and orbital configuration of the various instruments and platforms, a total of 10 different missions were eventually flown, as shown in Fig. 1. These included three multiinstrument flagship platforms known as Terra (launched in 1999), Aqua (launched in 2002), and Aura (launched in 2004), where the Terra mission emphasis was primarily on land processes, Aqua on the hydrological cycle, and Aura on atmospheric chemistry. In addition to these flagship missions the EOS missions included Landsat 7 (land cover classification and change), QuikScat (wind speed and direction over the oceans), Jason-1 (ocean surface topography and sea level), ACRIMSAT (total solar irradiance (TSI)), SAGE III (stratospheric aerosol and ozone levels), ICESat (ice, cloud, and land elevation), and SORCE (Solar Radiation and Climate Experiment; total and spectral solar irradiance). In addition EOS supported CERES (an Earth radiation budget sensor) and LIS (a lightning sensor), that flew as part of the NASA/JAXA Tropical Rainfall Measuring Mission (1997), and SeaWinds (wind speed and direction), that flew as part of the short-lived ADEOS II (Midori II, 2002) mission of JAXA (Japan Aerospace Exploration Agency). The EOS contributions are too numerous to present in detail in this article, but can be viewed for convenience as a means of obtaining global measurements (with appropriate characteristics) of the 24 variables and variable groups listed in Table 1. This table was created by the EOS community in the late 1990s as a way to succinctly indicate which instruments on the various EOS platforms were expected to contribute to each of the specified variables in primary (bold), secondary (bold italics), or contributing (roman) roles. All of the science data products produced by these space missions are available without charge throughout the world, and are thus highly valued for their many contributions to applications such as air quality, fires, land cover change, and the state of the oceans, as well as for science and monitoring of the state of the Earth system and its changes over time. These and other international satellites have played a crucial role in understanding and documenting global change, including (i) global surface temperature and atmospheric temperature and water vapor profiles, (ii) glacial retreat, (iii) sea ice extent and change, (iv) solar radiation into and out of the Earth-atmosphere system, (v) atmospheric aerosol and cloud properties, (vi) sources and sinks of

ICESat↗

NASA NTRS2019

Neutron Stars and Black Holes in the Small Magellanic Cloud: The SMC NuSTAR Legacy Survey

We present a source catalog from the first deep hard X-ray (E > 10 keV) survey of the Small Magellanic Cloud (SMC), the Nuclear Spectroscopic Telescope Array (NuSTAR) Legacy Survey of the SMC. We observed three fields, for a total exposure time of 1 Ms, along the bar of this nearby star-forming galaxy. Fields were chosen for their young stellar and accreting binary populations. We detected 10 sources above a 3σ significance level (4–25 keV) and obtained upper limits on an additional 40 sources. We reached a 3σ limiting luminosity in the 4–25 keV band of ∼10(exp 35) erg s(exp −1), allowing us to probe fainter X-ray binary (XRB) populations than has been possible with other extragalactic NuSTAR surveys. We used hard X-ray colors and luminosities to constrain the compact-object type, exploiting the spectral differences between accreting black holes and neutron stars at E > 10 keV. Several of our sources demonstrate variability consistent with previously observed behavior. We confirmed pulsations for seven pulsars in our 3σ sample. We present the first detection of pulsations from a BeXRB, SXP 305 (CXO J005215.4–73191), with an X-ray pulse period of 305.69 ± 0.16 s and a likely orbital period of ∼1160–1180 days. Bright sources (≳5 × 10(exp 36) erg s(exp −1) ) in our sample have compact-object classifications consistent with their previously reported types in the literature. Lower-luminosity sources (≲5 × 10(exp 36) erg s(exp −1) ) have X-ray colors and luminosities consistent with multiple classifications. We raise questions about possible spectral differences at low luminosity between SMC pulsars and the Galactic pulsars used to create the diagnostic diagrams

Lazzarini, M.↗