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At least 631 records · Page 35

STARLESS CLOUD CORE L1517B IN ENVELOPE EXPANSION WITH CORE COLLAPSE

Various spectral emission lines from the star-forming molecular cloud core L1517B manifest red asymmetric double-peaked profiles with stronger red peaks and weaker blue peaks, in contrast to the oft-observed blue-skewed molecular spectral line profiles with blue peaks stronger than red peaks. Invoking a spherically symmetric general polytropic hydrodynamic shock model for the envelope expansion with a core collapse (EECC) phase, we show the radial flow velocity, mass density, and temperature structures of a self-similar evolution for L1517B in a dynamically consistent manner. By prescribing simple radial profiles of abundance distribution for pertinent molecules, we perform molecular excitation and radiative transfer calculations using the publicly available RATRAN code set for the spherically symmetric case. Emphatically, the spectral profiles of line emissions from the same molecules but for different line transitions as well as spectra of closely pertinent isotopologues strongly constrain the self-similar hydrodynamics of a cloud core with prescribed abundances. Our computational results show that the EECC model reproduces molecular spectral line profiles in sensible agreement with the observational data of the Institut de Radioastronomie Millimetrique (IRAM), Five College Radio Astronomical Observatory, and Effelsberg 100 m telescopes for L1517B. We also report the spatially resolved observations of the optically thick line HCO{sup +}(1 - 0) using the Purple Mountain Observatory 13.7 m telescope at Delingha in China and the relevant fitting results. Hyperfine line structures of NH{sub 3} and N{sub 2}H{sup +} transitions are also fitted to consistently reveal the dynamics of the central core collapse. As a consistent model check, radial profiles of 1.2 mm and 850 {mu}m dust continua observed by the IRAM 30 m telescope and the Submillimeter Common-User Bolometer Array, respectively, are also fitted numerically using the same EECC model that produces the molecular line profiles. L1517B is likely undergoing an EECC shock phase. For future observational tests, we also predict several molecular line profiles with spatial distributions, the radial profile of the sub-millimeter continuum at wavelength 450 {mu}m, as well as the radial profiles of the column density and visual extinction for L1517B.

79 ASTRONOMY AND ASTROPHYSICS↗

Edge at the Pier: EPCAPE Software-Defined Sensing Field Campaign Report

The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was aimed to enhance the understanding of cloud and aerosol properties in the region surrounding La Jolla, California. To address challenges in data collection and processing from various instruments, an edge computing device known as Waggle Sage Node (WSN) was deployed at the Ellen Browning Scripps Memorial Pier. WSN is a distributed-sensing platform designed to collect and analyze environmental data at the edge. Sage is a multi-agency-supported project that designs and builds a new kind of national-scale reusable cyberinfrastructure to enable artificial intelligence (AI) at the edge based on the Waggle platform. Sponsors include the U.S. Department of Energy (DOE) Advanced Scientific Computing Research (ASCR), DOE National Nuclear Security Administration (NNSA), DOE Biological and Environmental Research (BER) through DOE Artificial Intelligence for Earth System Predictability (AI4ESP), Argonne Laboratory-Directed Research and Development (LDRD). Sage (https://sagecontinuum.org/) is funded as a National Science Foundation Mid-Scale Research Infrastructure (MSRI) project (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935984). This robust, multi-architecture edge computing platform facilitated environmental monitoring during the campaign. This report details the scientific objectives, deployment process, and key results of integrating Waggle into the EPCAPE field campaign.

54 ENVIRONMENTAL SCIENCES↗

A study of the Mg II k line in Beta Persei

Spectral scans of the Mg II k line in Beta Per were obtained with Copernicus during two primary eclipses. A brief description of observation in a short frame mode is given. The observed data are compared with computed line profiles based on the eclipse geometry of spherical stars. It is shown that a nonuniform circumstellar envelope, or cloud, probably is present. In one scan, a red-shifted line of P Cygni-type profile appeared, indicating the occurrence of an unusual event in the close binary system.

Chen, K.-Y.↗

A Near-Global Survey of Cirrus Particle Size Using ISCCP

Cirrus is the most frequently occurring and widely distributed cloud type. The average annual frequency of occurrence for cirrus is 34% and its global coverage is about 20-30% (Warren et al. 1985). It strongly influences weather and climate processes through its effects on the radiation budget of the earth and the atmosphere (Liou 1986). Microphysics of cirrus is a critical component in understanding cloud-climate radiative interactions. For example, ice water content feedback is positive from a 1-D model study. But the feedback is substantially reduced upon the inclusion of small ice crystals (Sinha and Shine 1994). Due to the complexity caused by the non-spherical shape of ice crystals in cirrus, retrievals of cirrus properties are difficult. In recent years, advances have been made both in models and in case studies (e.g., Takano and Liou 1989, Young et al. 1994), but no global scale survey has been conducted. Similar to our previous near-global survey of droplet sizes of liquid water clouds (Han et al. 1994), a survey of cirrus ice crystal sizes is conducted over both continental and oceanic areas. We describe a method for retrieving cirrus particle size information on a near-global scale 50 deg S to 50 deg N using currently available satellite data from ISCCP. To retrieve cirrus particle size, we use a radiative transfer model that includes all major absorbing gases and cloud scattering/absorption to compute synthetic radiances as a function of satellite viewing geometry. Ice crystal shapes are assumed to be hexagonal columns and plates. The model results have been validated against clear sky observations and are consistent with the observed radiance range under cloudy conditions.

Han, Qingyuan↗

Observing the Global Water Cycle from Space

This paper presents an approach to measuring all major components of the water cycle from space. Key elements of the global water cycle are discussed in terms of the storage of water-in the ocean, air, cloud and precipitation, in soil, ground water, snow and ice, and in lakes and rivers, and in terms of the global fluxes of water between these reservoirs. Approaches to measuring or otherwise evaluating the global water cycle are presented, and the limitations on known accuracy for many components of the water cycle are discussed, as are the characteristic spatial and temporal scales of the different water cycle components. Using these observational requirements for a global water cycle observing system, an approach to measuring the global water cycle from space is developed. The capabilities of various active and passive microwave instruments are discussed, as is the potential of supporting measurements from other sources. Examples of space observational systems, including TRMM/GPM precipitation measurement, cloud radars, soil moisture, sea surface salinity, temperature and humidity profiling, other measurement approaches and assimilation of the microwave and other data into interpretative computer models are discussed to develop the observational possibilities. The selection of orbits is then addressed, for orbit selection and antenna size/beamwidth considerations determine the sampling characteristics for satellite measurement systems. These considerations dictate a particular set of measurement possibilities, which are then matched to the observational sampling requirements based on the science. The results define a network of satellite instrumentation systems, many in low Earth orbit, a few in geostationary orbit, and all tied together through a sampling network that feeds the observations into a data-assimilative computer model.

Hildebrand, P. H.↗

Processing AIRS Scientific Data Through Level 2

The Atmospheric Infrared Spectrometer (AIRS) Science Processing System (SPS) is a collection of computer programs, denoted product generation executives (PGEs), for processing the readings of the AIRS suite of infrared and microwave instruments orbiting the Earth aboard NASA s Aqua spacecraft. AIRS SPS at an earlier stage of development was described in "Initial Processing of Infrared Spectral Data' (NPO-35243), NASA Tech Briefs, Vol. 28, No. 11 (November 2004), page 39. To recapitulate: Starting from level 0 (representing raw AIRS data), the PGEs and their data products are denoted by alphanumeric labels (1A, 1B, and 2) that signify the successive stages of processing. The cited prior article described processing through level 1B (the level-2 PGEs were not yet operational). The level-2 PGEs, which are now operational, receive packages of level-1B geolocated radiance data products and produce such geolocated geophysical atmospheric data products such as temperature and humidity profiles. The process of computing these geophysical data products is denoted "retrieval" and is quite complex. The main steps of the process are denoted microwave-only retrieval, cloud detection and cloud clearing, regression, full retrieval, and rapid transmittance algorithm.

Oliphant, Robert↗

HyspIRI Low Latency Concept and Benchmarks

Topics include HyspIRI low latency data ops concept, HyspIRI data flow, ongoing efforts, experiment with Web Coverage Processing Service (WCPS) approach to injecting new algorithms into SensorWeb, low fidelity HyspIRI IPM testbed, compute cloud testbed, open cloud testbed environment, Global Lambda Integrated Facility (GLIF) and OCC collaboration with Starlight, delay tolerant network (DTN) protocol benchmarking, and EO-1 configuration for preliminary DTN prototype.

Mandl, Dan↗

Computerized reduction of airborne foil impactor data

A new technique for reducing data obtained from a foil impactor carried on the T-28 thunderstorm penetration aircraft is described. The technique employs a video-image processing system that was originally developed for satellite imagery and a computer-reduction program that was originally developed for determining cloud-size distributions to count and size particle impressions on the foil. This eliminates the tedious manual processing that has limited the usefulness of such devices, while at the same time improving objectivity. The technique has been applied to foil data acquired in thunderstorms in the southeastern United States during the Cooperative Huntsville Meteorological Experiment, where most of the particles were at least roughly spherical. Its usefulness for storms in which highly irregular or fragile ice particles predominate has yet to be established.

Peterson, Bret A.↗

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗

On the Response of the Special Sensor Microwave/Imager to the Marine Environment: Implications for Atmospheric Parameter Retrievals

A reasonably rigorous basis for understanding and extracting the physical information content of Special Sensor Microwave/Imager (SSM/I) satellite images of the marine environment is provided. To this end, a comprehensive algebraic parameterization is developed for the response of the SSM/I to a set of nine atmospheric and ocean surface parameters. The brightness temperature model includes a closed-form approximation to microwave radiative transfer in a non-scattering atmosphere and fitted models for surface emission and scattering based on geometric optics calculations for the roughened sea surface. The combined model is empirically tuned using suitable sets of SSM/I data and coincident surface observations. The brightness temperature model is then used to examine the sensitivity of the SSM/I to realistic variations in the scene being observed and to evaluate the theoretical maximum precision of global SSM/I retrievals of integrated water vapor, integrated cloud liquid water, and surface wind speed. A general minimum-variance method for optimally retrieving geophysical parameters from multichannel brightness temperature measurements is outlined, and several global statistical constraints of the type required by this method are computed. Finally, a unified set of efficient statistical and semi-physical algorithms is presented for obtaining fields of surface wind speed, integrated water vapor, cloud liquid water, and precipitation from SSM/I brightness temperature data. Features include: a semi-physical method for retrieving integrated cloud liquid water at 15 km resolution and with rms errors as small as approximately 0.02 kg/sq m; a 3-channel statistical algorithm for integrated water vapor which was constructed so as to have improved linear response to water vapor and reduced sensitivity to precipitation; and two complementary indices of precipitation activity (based on 37 GHz attenuation and 85 GHz scattering, respectively), each of which are relatively insensitive to variations in other environmental parameters.

Petty, Grant W.↗

Multi-Sensor Cloud and Aerosol Retrieval Simulator and Remote Sensing from Model Parameters : Aerosols - Part 2

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms. Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (MOYD04). TheMOYD04 product is of course normally produced from MOYD021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a MOYD021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source. We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

aerosol retrieval↗

Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

Sonar↗

ICESat-2 Atmospheric Channel Description, Data Processing and First Results

The Advanced Topographic Laser Altimeter System (ATLAS) was launched aboard the Ice Cloud and land-Elevation Satellite-2 (ICESat-2) satellite in September 2018. ATLAS is a single wavelength (532 nm) lidar system designed to acquire high resolution measurements of the earth's surface while also obtaining atmospheric backscatter from molecules, clouds, and aerosols. Because ATLAS is optimized for altimetry, the atmospheric data acquired is unique in many respects and requires non-standard analysis techniques. For example, the high repetition rate laser limits the vertical extent of the profiles to just 14 km and causes atmospheric scattering from above 15 km to be added to the scattering in the lower 0–14 km profile. In addition, the limited vertical range of the acquired profiles renders it difficult to compute the magnitude of the solar background and hinders the application of standard calibration techniques. Despite these limitations, methods have been developed to successfully produce data products that have value to the atmospheric community for cloud and aerosol research and are currently available at the National Snow and Ice Data Center (NSIDC). In this paper we describe the ICESat-2 atmospheric channel and the methods used to process the ATLAS raw photon count data to obtain calibrated backscatter and higher level products such as layer heights and type, blowing snow, column optical depth and apparent surface reflectance.

Stephen P Palm↗

Cloud Bursting with GlideinWMS: Means to satisfy ever increasing computing needs for Scientific Workflows

Scientific communities have been in the forefront of adopting new technologies and methodologies in the computing. Scientific computing has influenced how science is done today, achieving breakthroughs that were impossible to achieve several decades ago. For the past decade several such communities in the Open Science Grid (OSG) and the European Grid Infrastructure (EGI) have been using GlideinWMS to run complex application workflows to effectively share computational resources over the grid. GlideinWMS is a pilot-based workload management system (WMS) that creates on demand, a dynamically sized overlay HTCondor batch system on grid resources. At present, the computational resources shared over the grid are just adequate to sustain the computing needs. We envision that the complexity of the science driven by 'Big Data' will further push the need for computational resources. To fulfill their increasing demands and/or to run specialized workflows, some of the big communities like CMS are investigating the use of cloud computing as Infrastructure-As-A-Service (IAAS) with GlideinWMS as a potential alternative to fill the void. Similarly, communities with no previous access to computing resources can use GlideinWMS to setup up a batch system on the cloud infrastructure. To enable this, the architecture of GlideinWMS has been extended to enable support for interfacing GlideinWMS with different Scientific and commercial cloud providers like HLT, FutureGrid, FermiCloud and Amazon EC2. In this paper, we describe a solution for cloud bursting with GlideinWMS. The paper describes the approach, architectural changes and lessons learned while enabling support for cloud infrastructures in GlideinWMS.

Mhashilkar, Parag↗

Using Satellite Data in Weather Forecasting: I

The GOES Product Generation System (GPGS) is a set of computer codes and scripts that enable the assimilation of real-time Geostationary Operational Environmental Satellite (GOES) data into regional-weather-forecasting mathematical models. The GPGS can be used to derive such geophysical parameters as land surface temperature, the amount of precipitable water, the degree of cloud cover, the surface albedo, and the amount of insolation from satellite measurements of radiant energy emitted by the Earth and its atmosphere. GPGS incorporates a priori information (initial guesses of thermodynamic parameters of the atmosphere) and radiometric measurements from the geostationary operational environmental satellites along with mathematical models of physical principles that govern the transfer of energy in the atmosphere. GPGS solves the radiative-transfer equation and provides the resulting data products in formats suitable for use by weather-forecasting computer programs. The data-assimilation capability afforded by GPGS offers the potential to improve local weather forecasts ranging from 3 hours to 2 days - especially with respect to temperature, humidity, cloud cover, and the probability of precipitation. The improvements afforded by GPGS could be of interest to news media, utility companies, and other organizations that utilize regional weather forecasts.

Jedlovec, Gary J.↗

Cloud and surface textural features in polar regions

The study examines the textural signatures of clouds, ice-covered mountains, solid and broken sea ice and floes, and open water. The textural features are computed from sum and difference histogram and gray-level difference vector statistics defined at various pixel displacement distances derived from Landsat multispectral scanner data. Polar cloudiness, snow-covered mountainous regions, solid sea ice, glaciers, and open water have distinguishable texture features. This suggests that textural measures can be successfully applied to the detection of clouds over snow-covered mountains, an ability of considerable importance for the modeling of snow-melt runoff. However, broken stratocumulus cloud decks and thin cirrus over broken sea ice remain difficult to distinguish texturally. It is concluded that even with high spatial resolution imagery, it may not be possible to distinguish broken stratocumulus and thin clouds from sea ice in the marginal ice zone using the visible channel textural features alone.

Welch, Ronald M.↗

MOLNs: A Cloud Platform for Interactive, Reproducible, and Scalable Spatial Stochastic Computational Experiments in Systems Biology Using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. In conclusion, MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

97 MATHEMATICS AND COMPUTING↗