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NASA NTRS2018

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

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

Geostationary Environment Monitoring Spectrometer ↗

DOE OSTI2024

Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment’s run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory’s needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1984

Oceanographic biooptical profiling system

A new oceanographic instrument to measure underwater optical, biological, and physical properties has been designed, built, and used extensively at sea. The new instrument system is a significant advance that permits optimum sampling strategies using ship, aircraft, and satellite optical sensors for ocean research. The design criteria for the biooptical profiling system included the rapid acquisition of data to accommodate shipboard synoptic sampling; the measurement of the necessary parameters for providing appropriate contemporaneous surface data for remote sensors; the compatibility of the data acquisition system with conventional conductivity temperature depth-type cables and winches on the oceanographic fleet; the capability of real-time display of data along with rapid preliminary data reduction at sea. The primary instrument package in this profiling system is a new microprocessor controlled multiwavelength spectroradiometer. A description of the instrument and examples of data are presented.

Smith, R. C.↗

DOE OSTI2021

Precise and accurate cosmology with CMB×LSS power spectra and bispectra

With the advent of a new generation of cosmological experiments that will provide high-precision measurements of the cosmic microwave background (CMB) and galaxies in the large-scale structure, it is pertinent to examine the potential of performing a joint analysis of multiple cosmological probes. In this paper, we study the cosmological information content contained in the one-loop power spectra and tree bispectra of galaxies cross-correlated with CMB lensing. We use the FFTLog method to compute angular correlations in spherical harmonic space, applicable for wide angles that can be accessed by forthcoming galaxy surveys. Here, we find that adding the bispectra and cross-correlations with CMB lensing offers a significant improvement in parameter constraints, including those on the total neutrino mass, M ν , and local non-Gaussianity amplitude, ƒ NL . In particular, our results suggest that the combination of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and CMB-S4 will be able to achieve σ(M ν )=42 meV from galaxy and CMB lensing correlations, and σ(M ν )=12 meV when further combined with the CMB temperature and polarization data, without any prior on the optical depth.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗

DOE OSTI2018

Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology (Final Project)

The search for simple and elegant equations to explain and predict observable natural phenomena has been a driving force in scientific theory for several centuries. The great successes of Newton's laws, Maxwell's equations, the Schrodinger equation, and the theory of General Relativity have encouraged this perspective, and perhaps a beautiful equation comprising a “theory of everything” remains to be discovered. But now scientists are grappling with systems of ever-increasing complexity, fraught with nonlinearity, and with the quantities of interest often only indirectly observed. Nature may resist a simple description, and the most important discoveries of the next century may be complex theories with countless variables and parameters. The era of big data opens up a promising new approach to scientific discovery in this setting. The predictions of modern theories, even if complex or nonlinear, can be examined through detailed and computationally-intensive simulations that take days or weeks to run and fill petabytes of disk space. The upcoming generation of scientific instruments will provide petabytes of observations at unprecedented resolution and depth. The challenge is to compare data to simulations so as to test theories and identify those that best match the observations. The data are numerous and rich but are subject to noise and various systematic biases. The simulations are large and detailed but are so costly that the effective sample size is small. Moreover, the mapping from data to simulation to the parameters of interest is typically complicated and ill-conditioned. Although these challenges, and the solutions we propose, apply across the sciences, we will ground our efforts by focusing on cosmology. Here, we are truly entering the era of big data in several senses. Ever larger experiments survey larger and larger cosmological volumes, resulting in enormous amounts of pixel data to process and extract the information of cosmological interest. These include large imaging surveys, which collect all the light in broad wavelength ranges over significant fractions of the sky, and spectroscopic surveys, which measure the light as a function of wavelength to map out 3D distances. These experiments are designed to shed light on the big unanswered questions in cosmology. Distilling the petabytes of data into observed quantities for comparison with theory is a daunting task. But these large experiments do not only result in large amounts of data to analyze: if we wish to compare to theories of physics, then we need increasingly extensive predictions for the observed quantities as well, which requires ever-larger cosmological simulations that take substantial -- and at times prohibitive -- computational resources to produce. Our goal is to develop statistical and machine learning methods for using observed and simulated data to advance machine learning with applications cosmology. In particular, we focus our research efforts on the following tasks: The challenge for automated science is that it is computationally impossible and statistically dangerous to consider every possible model in order to find the best one. We will develop Bayesian Optimization based active-learning methods that accelerate both the execution of the simulations and the search for best-fitting parameters. The key idea is to make the simulations adaptive -- across resolutions, time, and parameters -- using the data to search as the simulation runs; Most ML methods operate on simple finite dimensional feature vectors. However, many cosmology and other science applications require ML methods that can operate on more complex objects as inputs or outputs such as functions, distributions, or sets and point clouds. Our goal is to develop efficient ML methods for this problem and demonstrate their applicability in Cosmology, Astrophysics, and other science problems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Appalachian Basin Play Fairway Analysis: Natural Sedimentary Reservoirs Data 2016 Revision

Tier 3 data for Appalachian Basin sectors of New York, Pennsylvania and West Virginia used in a Geothermal Play Fairway Analysis of opportunities for low-temperature direct-use applications of heat. It accompanies data and materials submitted as Geothermal Data Repository Submission "Natural Reservoir Analysis 2016 GPFA-AB" (linked below). Reservoir information are derived from oil and gas exploration and production data sets, or derived from those data based on further analysis. Data reported here encompass locations (horizontal and depth), geologic formation names, lithology, reservoir volume, porosity and permeability, and derived approximations of the quality of the reservoir. These differ from the linked 2015 data submission in that this file presents data for New York that are comparable to those in the other two states. In contrast, the 2015 data available measured differing attributes across the state boundaries.

15 GEOTHERMAL ENERGY↗

NASA NTRS2004

Aerosol Absorption Effects in the TOMS UV Algorithm

The availability of global long-term estimates of surface UV radiation is very important, not only for preventive medicine considerations, but also as an important tool to monitor the effects of the stratospheric ozone recovery expected to occur in the next few decades as a result of the decline of the stratospheric chlorine levels. In addition to the modulating effects of ozone and clouds, aerosols also affect the levels of UV-A and W-B radiation reaching the surface. Oscillations in surface W associated with the effects of aerosol absorption may be comparable in magnitude to variations associated with the stratospheric ozone recovery. Thus, the accurate calculation of surface W radiation requires that both the scattering and absorption effects of tropospheric aerosols be taken into account. Although absorption effects of dust and elevated carbonaceous aerosols are already accounted for using Aerosol Index technique, this approach does not work for urban/industrial aerosols in the planetary boundary layer. The use of the new TOMS long-term global data record on UV aerosol absorption optical depth, can improve the accuracy of TOMS spectral UV products, by properly including the spectral attenuation effects of carbonaceous, urban/industrial and mineral aerosols. The TOMS data set on aerosol properties will be discussed, and results of its use in the TOMS surface W algorithm will be presented.

Torres, O.↗

DOE OSTI2018

LSST Observing Strategy White Paper: LSST Observations of WFIRST Deep Fields

The Wide-Field Infrared Survey Telescope (WFIRST) is expected to launch in the mid-2020s. With its wide-field near-infrared (NIR) camera, it will survey the sky to unprecedented detail. As part of normal operations and as the result of multiple expected dedicated surveys, WFIRST will produce several relatively wide-field (tens of square degrees) deep (limiting magnitude of 28 or fainter) fields. In particular, a planned supernova survey is expected to image 3 deep fields in the LSST footprint roughly every 5 days over 2 years. Stacking all data, this survey will produce, over all WFIRST supernova fields in the LSST footprint, ~12-25 deg 2 and ~5-15 deg 2 regions to depths of ~28 mag and ~29 mag, respectively. We suggest LSST undertake mini-surveys that will match the WFIRST cadence and simultaneously observe the supernova survey fields during the 2-year WFIRST supernova survey, achieving a stacked depth similar to that of the WFIRST data. We also suggest additional observations of these same regions throughout the LSST survey to get deep images earlier, have long-term monitoring in the fields, and produce deeper images overall. These fields will provide a legacy for cosmology, extragalactic, and transient/variable science.

Foley, RJ↗

DOE OSTI2016

Quantifying the Financial Benefits of Multifamily Retrofits

Increasing the adoption of energy efficient building practices will require the energy sector to increase their understanding of the way that retrofits affect multifamily financial performance as well as how those indicators are interpreted by the lending and appraisal industries. This project analyzed building, energy, and financial program data as well as other public and private data to examine the relationship between energy efficiency retrofits and financial performance on three levels: building, city, and community. The project goals were to increase the data and analysis in the growing body of multifamily financial benefits work as well provide a framework for other geographies to produce similar characterization. The goals are accomplished through three tasks. Task one: A pre- and post-retrofit analysis of thirteen Chicago multifamily buildings. Task two: A comparison of Chicago income and expenses to two national datasets. Task three: An in-depth look at multifamily market sales data and the subsequent impact of buildings that undergo retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Building America Case Study: Quantifying the Financial Benefits of Multifamily Retrofits, Chicago, Illinois

Increasing the adoption of energy efficient building practices will require the energy sector to increase their understanding of the way that retrofits affect multifamily financial performance as well as how those indicators are interpreted by the lending and appraisal industries. This project analyzed building, energy, and financial program data as well as other public and private data to examine the relationship between energy efficiency retrofits and financial performance on three levels: building, city, and community. The project goals were to increase the data and analysis in the growing body of multifamily financial benefits work as well provide a framework for other geographies to produce similar characterization. The goals are accomplished through three tasks: Task one: A pre- and post-retrofit analysis of thirteen Chicago multifamily buildings. Task two: A comparison of Chicago income and expenses to two national datasets. Task three: An in-depth look at multifamily market sales data and the subsequent impact of buildings that undergo retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

SPRUCE Geochemical Changes in Porewater from Northern Peatlands at Multiple Depths in Field Samples and over Time in Peat Incubations

This data set reports the results of the analysis of dissolved organic matter (DOM) in porewater samples collected from multiple depths in northern peatlands and during laboratory peat incubations to characterize the gain or loss of specific chemical moieties (e.g. CH2, H2, CH2O, etc.) that may be attributed to microbial degradation. During incubations, CO2 and CH4 concentrations and stable isotopes were determined by isotope ratio mass spectrometry. Porewater samples were collected from the S1 Bog (April 2015), a Sphagnum-dominated site at northern Minnesota peatland in the Marcell Experimental Forest, using permanently-installed piezometers, which provided profiles from the watertable surface to 200 cm depth. This is the site of the SPRUCE experiment. Porewater and peat samples were collected in July 2015 from an additional Sphagnum-dominated site within Stordalen Mire, Sweden, a palsa mire underlain by thawing permafrost, using perforated stainless steel tubes that were inserted into the peat to the desired depths (13 cm and 32 cm). Porewater samples were analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS) followed by Kendrick mass transform analysis. The Kendrick mass (i.e., Kendrick 1963) transform analysis identifies the gain or loss of specific chemical moieties (e.g. CH2, H2, CH2O, etc.) via mass differences, and was used to infer potential microbial degradation pathways by which compounds were consumed in these samples. Increasing depth would suggest more degradation of DOM with the greater length of time for peat accumulation and similarly, with increasing length of peat incubation. During incubations, CO2 and CH4 concentrations and stable isotopes were determined by isotope ratio mass spectrometry. There are four files provided in comma separated value (*.csv) format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The Atacama Cosmology Telescope: Probing the baryon content of SDSS DR15 galaxies with the thermal and kinematic Sunyaev-Zel’dovich effects

We present high signal-to-noise measurements (up to 12$\sigma$) of the average thermal Sunyaev Zel'dovich (tSZ) effect from optically selected galaxy groups and clusters and estimate their baryon content within a 2.1$^\prime$ radius aperture. Sources from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS) DR15 catalog overlap with 3,700 sq. deg. of sky observed by the Atacama Cosmology Telescope (ACT) from 2008 to 2018 at 150 and 98 GHz (ACT DR5), and 2,089 sq. deg. of internal linear combination component-separated maps combining ACT and $\it{Planck}$ data (ACT DR4). The corresponding optical depths, $\bar{\tau}$, which depend on the baryon content of the halos, are estimated using results from cosmological hydrodynamic simulations assuming an AGN feedback radiative cooling model. We estimate the mean mass of the halos in multiple luminosity bins, and compare the tSZ-based $\bar{\tau}$ estimates to theoretical predictions of the baryon content for a Navarro-Frenk-White profile. We do the same for $\bar{\tau}$ estimates extracted from fits to pairwise baryon momentum measurements of the kinematic Sunyaev-Zel'dovich effect (kSZ) for the same data set obtained in a companion paper. We find that the $\bar{\tau}$ estimates from the tSZ measurements in this work and the kSZ measurements in the companion paper agree within $1\sigma$ for two out of the three disjoint luminosity bins studied, while they differ by 2-3$\sigma$ in the highest luminosity bin. The optical depth estimates account for one third to all of the theoretically predicted baryon content in the halos across luminosity bins. Potential systematic uncertainties are discussed. The tSZ and kSZ measurements provide a step towards empirical Compton-$\bar{y}$-$\bar{\tau}$ relationships to provide new tests of cluster formation and evolution models.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

THE EFFECTS OF DARK MATTER ANNIHILATION ON COSMIC REIONIZATION

We revisit the possibility of constraining the properties of dark matter (DM) by studying the epoch of cosmic reionization. Previous studies have shown that DM annihilation was unlikely to have provided a large fraction of the photons which ionized the universe, but instead played a subdominant role relative to stars and quasars. The DM might, however, have begun to efficiently annihilate with the formation of primordial microhalos at z ∼ 100–200, much earlier than the formation of the first stars. Therefore, if DM annihilation ionized the universe at even the percent level over the interval z ∼ 20–100, it could leave a significant imprint on the global optical depth, τ . Moreover, we show that cosmic microwave background polarization data and future 21 cm measurements will enable us to more directly probe the DM contribution to the optical depth. In order to compute the annihilation rate throughout the epoch of reionization, we adopt the latest results from structure formation studies and explore the impact of various free parameters on our results. We show that future measurements could make it possible to place constraints on the DM’s annihilation cross-sections, which are at a level comparable to those obtained from the observations of dwarf galaxies, cosmic-ray measurements, and studies of recombination.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2016

Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China 2004-2013

Three decades of rapid economic development is causing severe and widespread PM2.5(particulate matter (is) less than 2.5 ) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data. We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1 deg resolution using the most recent satellite data and evaluated model performance with available ground observations. We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model predicted PM2.5 concentrations from 2004 to early 2014 using ground observations. The overall model cross-validation R(sup 2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM(sub 2.5) concentrations with little bias at the monthly (R(sup 2) = 0.73), regression slope = 0.91) and seasonal (R(sup 2) = 0.79), regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 micro-g/cu cm between 2004 and 2007 and a decrease of 0.46 micro-g/cu cm between 2008 and 2013. Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

satellite remote sensing↗

DOE OSTI2016

SU-F-E-06: Dosimetric Characterization of Small Photons Beams of a Novel Linear Accelerator

Purpose: The aim of the present contribution was to measure the main dosimetric quantities of small fields produced by UNIQUE and evaluate its matching with the corresponding dosimetric data of one 21EX conventional linear accelerator (Varian) in operation at the same center. The second step was to evaluate comparative performance of the EDGE diode detector and the PinPoint micro-ionization chamber for dosimetry of small fields. Methods: UNIQUE is configured with MLC (120 leaves with 0.5 cm leaf width) and a single low photon energy of 6 MV. Beam data were measured with scanning EDGE diode detector (volume of 0.019 mm{sup 3}), a PinPoint micro-ionization chamber (PTW) and for larger fields (≥ 4×4cm{sup 2}) a PTW Semi flex chamber (0.125 cm{sup 3}) was used. The scanning system used was the 3D cylindrical tank manufactured by Sun Nuclear, Inc. The measurement of PDD and profiles were done at 100 cm SSD and 1.5 depth; the relative output factors were measured at 10 cm depth. Results: PDD and the profile data showed less than 1% variation between the two linear accelerators for fields size between 2×2 cm{sup 2} and 5×5cm{sup 2}. Output factor differences was less than 1% for field sizes between 3×3 cm{sup 2} and 10×10 cm{sup 2} and less of 1.5 % for fields of 1.5×1.5 cm{sup 2} and 2×2 cm{sup 2} respectively. The dmax value of the EDGE diode detector, measured from the PDD, was 8.347 mm for 0.5×0,5cm{sup 2} for UNIQUE. The performance of EDGE diode detector was comparable for all measurements in small fields. Conclusion: UNIQUE linear accelerator show similar dosimetrics characteristics as conventional 21EX Varian linear accelerator for small, medium and large field sizes.EDGE detector show good performance by measuring dosimetrics quantities in small fields typically used in IMRT and radiosurgery treatments.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1989

The Mars climate for a photovoltaic system operation

Detailed information on the climatic conditions on Mars are very desirable for the design of photovoltaic systems for establishing outposts on the Martian surface. The distribution of solar insolation (global, direct and diffuse) and ambient temperature is addressed. This data are given at the Viking lander's locations and can also be used, to a first approximation, for other latitudes. The insolation data is based on measured optical depth of the Martian atmosphere derived from images taken of the sun with a special diode on the Viking cameras; and computation based on multiple wavelength and multiple scattering of the solar radiation. The ambient temperature (diurnal and yearly distribution) is based on direct measurements with a thermocouple at 1.6 m above the ground at the Viking lander locations. The insolation and ambient temperature information are short term data. New information about Mars may be forthcoming in the future from new analysis of previously collected data or from future flight missions. The Mars climate data for photovoltaic system operation will thus be updated accordingly.

Appelbaum, Joseph↗

NASA NTRS1989

The Mars climate for a photovoltaic system operation

Appelbaum, Joseph↗