Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Science Pipeline”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

NASA Tech Briefs, November 2008

Topics covered include: Digital Phase Meter for a Laser Heterodyne Interferometer; Vision System Measures Motions of Robot and External Objects; Advanced Precipitation Radar Antenna to Measure Rainfall From Space; Wide-Band Radar for Measuring Thickness of Sea Ice; Vertical Isolation for Photodiodes in CMOS Imagers; Wide-Band Microwave Receivers Using Photonic Processing; L-Band Transmit/Receive Module for Phase-Stable Array Antennas; Microwave Power Combiner/Switch Utilizing a Faraday Rotator; Compact Low-Loss Planar Magic-T; Using Pipelined XNOR Logic to Reduce SEU Risks in State Machines; Quasi-Optical Transmission Line for 94-GHz Radar; Next Generation Flight Controller Trainer System; Converting from DDOR SASF to APF; Converting from CVF to AAF; Documenting AUTOGEN and APGEN Model Files; Sequence History Update Tool; Extraction and Analysis of Display Data; MRO DKF Post-Processing Tool; Rig Diagnostic Tools; MRO Sequence Checking Tool; Science Activity Planner for the MER Mission; UAVSAR Flight-Planning System; Templates for Deposition of Microscopic Pointed Structures; Adjustable Membrane Mirrors Incorporating G-Elastomers; Hall-Effect Thruster Utilizing Bismuth as Propellant; High-Temperature Crystal-Growth Cartridge Tubes Made by VPS; Quench Crucibles Reinforced with Metal; Deep-Sea Hydrothermal-Vent Sampler; Mars Rocket Propulsion System; Two-Stage Passive Vibration Isolator; Improved Thermal Design of a Compression Mold; Enhanced Pseudo-Waypoint Guidance for Spacecraft Maneuvers; Altimetry Using GPS-Reflection/Occultation Interferometry; Thermally Driven Josephson Effect; Perturbation Effects on a Supercritical C7H16/N2 Mixing Layer; Gold Nanoparticle Labels Amplify Ellipsometric Signals; Phase Matching of Diverse Modes in a WGM Resonator; WGM Resonators for Terahertz-to-Optical Frequency Conversion; Determining Concentration of Nanoparticles from Ellipsometry; Microwave-to-Optical Conversion in WGM Resonators; Four-Pass Coupler for Laser-Diode-Pumped Solid-State Laser; Low-Resolution Raman-Spectroscopy Combustion Thermometry; Temperature Sensors Based on WGM Optical Resonators; Varying the Divergence of Multiple Parallel Laser Beams; Efficient Algorithm for Rectangular Spiral Search; Algorithm-Based Fault Tolerance Integrated with Replication; Targeting and Localization for Mars Rover Operations; Terrain-Adaptive Navigation Architecture; Self-Adjusting Hash Tables for Embedded Flight Applications; Schema for Spacecraft-Command Dictionary; Combined GMSK Communications and PN Ranging; System-Level Integration of Mass Memory; Network-Attached Solid-State Recorder Architecture; Method of Cross-Linking Aerogels Using a One-Pot Reaction Scheme; An Efficient Reachability Analysis Algorithm.

Source record↗
DOE OSTI2022

Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2

This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexibly extended for in situ analysis. The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware heterogeneity lies in the decoupling of data description in domain sciences, such as plasma physics simulations, from concrete implementations in hardware and IO. The streaming backend is provided by the ADIOS2 framework, developed at Oak Ridge National Laboratory. This paper surveys two openPMD-based loosely-coupled setups to demonstrate flexible applicability and to evaluate performance. In loose coupling, as opposed to tight coupling, two (or more) applications are executed separately, e.g. in individual MPI contexts, yet cooperate by exchanging data. This way, a streaming-based workflow allows for standalone codes instead of tightly-coupled plugins, using a unified streaming-aware API and leveraging high-speed communication infrastructure available in modern compute clusters for massive data exchange. We determine new challenges in resource allocation and in the need of strategies for a flexible data distribution, demonstrating their influence on efficiency and scaling on the Summit compute system. The presented setups show the potential for a more flexible use of compute resources brought by streaming IO as well as the ability to increase throughput by avoiding filesystem bottlenecks.

Poeschel, Franz↗
DOE OSTI2022

Protocol for single-cell isolation and genome amplification of environmental microbial eukaryotes for genomic analysis

We describe environmental microbial eukaryotes (EMEs) sample collection, single-cell isolation, lysis, and genome amplification, followed by the rDNA amplification and OTU screening for recovery of high-quality species-specific genomes for de novo assembly. These protocols are part of our pipeline that also includes bioinformatic methods. The pipeline and its application on a wide range of phyla of different sample complexity are described in our complementary paper. In addition, this protocol describes optimized lysis, genome amplification, and OTU screening steps of the pipeline. For complete details on the use and execution of this protocol, please refer to Ciobanu et al. (2021).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Low-Probability High-Consequence (LPHC) Failure Events in Geologic Carbon Sequestration Pipelines and Wells: Framework for LPHC Risk Assessment Incorporating Spatial Variability of Risk

If Carbon dioxide Capture and Storage (CCS) is to be effective in mitigating climate change, it will need to be carried out on a very large scale. This will involve many thousands of miles of dedicated high-pressure pipelines in order to transport many millions of tonnes of CO 2 annually, with the CO 2 delivered to many thousands of wells that will inject the CO 2 underground. The new CCS infrastructure could rival in size the current U.S. upstream natural gas pipeline and well infrastructure. This new infrastructure entails hazards for life, health, animals, the environment, and natural resources. Pipelines are known to rupture due to corrosion, from external forces such as impacts by vehicles or digging equipment, by defects in construction, or from the failure of valves and seals. Similarly, wells are vulnerable to catastrophic failure due to corrosion, cement degradation, or operational mistakes. While most accidents involving pipelines and wells will be minor, there is the inevitable possibility of accidents with very high consequences, especially to public health. The most important consequence of concern is CO 2 release to the environment in concentrations sufficient to cause death by asphyxiation to nearby populations. Such accidents are thought to be very unlikely, but of course they cannot be excluded, even if major engineering effort is devoted (as it will be) to keeping their probability low and their consequences minimized. This project has developed a methodology for analyzing the risks of these rare but high-consequence accidents, using a step-by-step probabilistic methodology. A key difference between risks for pipelines and wells is that the former are spatially distributed along the pipe whereas the latter are confined to the vicinity of the well. Otherwise, the methodology we develop for risk assessment of pipeline and well failures is similar and provides an analysis both of the annual probabilities of accident sequences of concern and of their consequences, and crucially the methodology provides insights into what measures might be taken to mitigate those accident sequences identified as of concern. Mitigating strategies could address reducing the likelihood of an accident sequence of concern, or reducing the consequences, or some combination. The methodology elucidates both local and integrated risks along the pipeline or at the well providing information useful to decision makers at various levels including local (e.g., property owners and town councils), regional (e.g., county and state representatives), and national levels (federal regulators and corporate proponents).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accelerating the identification of novel secondary metabolites in bioenergy plant root exudates using MicroED

Small molecule metabolites drive inter- and intraspecies communication and dependencies in diverse biological systems, yet a large proportion of these important chemical compounds remain uncharacterized in plants and microbes. Approximately 90% of the metabolites in root exudate profiles are unknown compounds, despite the importance of root exudate composition in plant-microbe interactions. We need advanced analytical capabilities that will support rapid discovery and structural elucidation of metabolites from biological samples that may be limited in quantity and high in complexity. To fill this gap, this project aimed to develop an integrated workflow involving metabolite extraction, separation, and crystallization from plant root exudates followed by characterization using nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, and microcrystal electron diffraction (MicroED). Using crude root exudates from sorghum, this project successfully developed higher throughput exudate fractionation strategies to obtain pure compounds for crystallization and identified crystals in multiple fractions that diffracted. Additional efforts to increase the throughput of high-quality crystal generation for MicroED, such as crystallization screening and crystallization chaperone exploration, will be needed to further advance root exudate metabolite identification. The overall optimized sample preparation process can then be integrated with the existing data collection and data analysis pipelines for MicroED at PNNL to facilitate more rapid natural product discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Accelerating Advanced Light Source Science Through Multi-Facility HPC Workflows

Synchrotron light sources support a wide array of techniques to investigate materials, often producing complex, high-volume data that challenge traditional workflows. At the Advanced Light Source (ALS), we developed infrastructure to move microtomography data over ESnet to ALCF and NERSC, where CPU- and GPU-based algorithms generate 3D reconstructed volumes of experimental samples. We employ two data movement and reconstruction models: real-time processing as data streams directly to NERSC compute nodes, and automated file transfer to NERSC and ALCF file systems. The streaming pipeline provides users with feedback in under ten seconds, while the file-based workflow produces high-quality reconstructions suitable for deeper analysis in 20-30 minutes. This infrastructure enables users to utilize HPC resources without direct access to backend systems. We plan to extend this architecture to more endstations, supporting our beamline scientists and users.

Abramov, David↗
DOE OSTI2016

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine Learning (ML) Classifier to Assist Metadata Creation

The Atmospheric Radiation Measurement (ARM) Data Center is responsible for the timely collection, archival, and curation of science data products. These products are freely available through an online data repository. Metadata creation is paramount for scientific users to find and access over seven petabytes of atmospheric science data. The hierarchical metadata structure allows users to search for information at both broad and narrow levels. This project aims to leverage 30 years’ worth of manually created metadata to enable machine predictions of broad-term classifications from narrow-term descriptions. These classification predictions would assist metadata coordinators with their term selections. This paper discusses the cleaning and preprocessing of the training data, the pipeline developed to determine the best model for this task, and the creation of an API metadata classifier for ARM measurement metadata. Our results show that the Linear Support Vector Classification (LinearSVC) algorithm, along with the Term Frequency – Inverse Document Frequency (TF-IDF) vectorizer, is well-suited for our multi-class classification task. Lengthier input training data led to better results, and artificial balancing was unnecessary for this particular use case. This predictive classifier enhances efficiency in metadata creation, as well as supports greater consistency and accuracy in metadata tagging.

Collier, Hannah [ORNL] (ORCID:0000000341284292)↗
DOE OSTI2019

multiPhATE: bioinformatics pipeline for functional annotation of phage isolates

To address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene calling algorithm and assigns putative functions to gene calls using protein-, virus- and phage-centric databases. multiPhATE’s modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT): Semi-Annual Progress Report

This report summarizes work carried out by the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Team for the period of July 1, 2011 through December 31, 2011. It discusses highlights, overall progress, period goals, and collaborations and lists papers and presentations. The UV-CDAT team is positioned to address the following high-level visualization requirements: (1) Alternative parallel streaming statistics and analysis pipelines - Data parallelism, Task parallelism, Visualization parallelism; (2) Optimized parallel input/output (I/O); (3) Remote interactive execution; (4) Advanced intercomparison visualization; (5) Data provenance processing and capture; and (6) Interfaces for scientists - Workflow data analysis and visualization construction tools, Visualization interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Remote methane sensor for emissions from pipelines and compressor stations using chirped-laser dispersion spectroscopy

Leak rates of methane (CH 4 ) from the natural gas supply chain result in lost profit from unsold product, public safety and property concerns due to potential explosion hazards, and a potentially large source of economic damages from legal liabilities. Yet large measurement challenges exist in identifying and quantifying CH 4 leak rates along the vast number and type of components in the natural gas supply chain. This is particularly true of the “midstream” components involved in the gathering, processing, compression, transmission, and storage of natural gas. This project developed and deployed new advances in chirped laser dispersion spectroscopy (CLaDS) to detect methane leaks from pipelines, compressor stations, and other midstream infrastructure from a remote position (standoff detection). The system was deployed from a van to measure fugitive methane leaks from a local compressor station as a proof-of-concept. The technique was validated through mobile laboratory measurements with in-situ sensors as well as controlled releases of methane. The system also mapped a plume from a controlled release of methane by tracking a small unmanned aerial system (sUAS) that carried a corner cube retroreflector which reflected the beam back to the instrument. The drone-based system quantified a leak rate to within 30% of the actual rate and localized the emission location within 5 m of the actual release location at standoff distances of 25-45 m. This sUAS-reflector tracking approach has benefits for mapping leak locations remotely using small drones flying around a facility. Benefits of a commercial sensor with these capabilities include reductions of leaks for pipeline operators (more profit), earlier detection of leaks to avoid catastrophic explosion hazards for public health and to mitigate property damage, and reduced methane emissions to the atmosphere (improving air quality).

03 NATURAL GAS↗
DOE OSTI2025

Phylogenomic discovery and engineering of nitrogen fixation into the bioenergy woody crop poplar

Biological nitrogen fixation (BNF) is a key process enabling plants in specific lineages to convert atmospheric dinitrogen (N₂) into bioavailable ammonia through symbioses with diazotrophic microbes. Expanding this capability beyond native nitrogen-fixing clades into non-nodulating crops would reduce synthetic fertilizer use, lowering energy inputs and environmental impacts in agriculture. Supported by DOE Funding Award DE-SC0018247, the NitFix project advanced foundational knowledge required to engineer root-nodule symbioses in new host species. The team generated the most comprehensive phylogenomic analysis to date of all known nodulating lineages, resolving the evolutionary history of nitrogen-fixing symbiosis and identifying core gene suites retained across nodulating taxa. Through multimodal genomics, transcriptomics, and functional analyses in Medicago truncatula and related species, the project mapped regulatory networks underlying nodule organogenesis, bacterial infection, and nitrogen-fixation efficiency. Key discoveries include the identification of conserved signaling modules for rhizobial recognition, transcription factors controlling nodule differentiation, and metabolic pathways integrating fixed nitrogen into plant growth. The project also developed enabling tools—including optimized transformation pipelines, gene-editing workflows, and imaging-based phenotyping—to accelerate engineering efforts in emerging models. Together, these results refine the mechanistic framework of symbiotic nitrogen fixation and highlight transferable components essential for rewiring these traits into non-nodulating crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

GenomeTrakr proficiency testing for foodborne pathogen surveillance: an exercise from 2015

Pathogen monitoring is becoming more precise as sequencing technologies become more affordable and accessible worldwide. This transition is especially apparent in the field of food safety, which has demonstrated how whole-genome sequencing (WGS) can be used on a global scale to protect public health. GenomeTrakr coordinates the WGS performed by public-health agencies and other partners by providing a public database with real-time cluster analysis for foodborne pathogen surveillance. Because WGS is being used to support enforcement decisions, it is essential to have confidence in the quality of the data being used and the downstream data analyses that guide these decisions. Routine proficiency tests, such as the one described here, have an important role in ensuring the validity of both data and procedures. In 2015, the GenomeTrakr proficiency test distributed eight isolates of common foodborne pathogens to participating laboratories, who were required to follow a specific protocol for performing WGS. Resulting sequence data were evaluated for several metrics, including proper labelling, sequence quality and new single nucleotide polymorphisms (SNPs). Illumina MiSeq sequence data collected for the same set of strains across 21 different laboratories exhibited high reproducibility, while revealing a narrow range of technical and biological variance. The numbers of SNPs reported for sequencing runs of the same isolates across multiple laboratories support the robustness of our cluster analysis pipeline in that each individual isolate cultured and resequenced multiple times in multiple places are all easily identifiable as originating from the same source.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes.

16S rRNA↗
DOE OSTI2013

Data co-processing for extreme scale analysis level II ASC milestone (4745).

Exascale supercomputing will embody many revolutionary changes in the hardware and software of high-performance computing. A particularly pressing issue is gaining insight into the science behind the exascale computations. Power and I/O speed con- straints will fundamentally change current visualization and analysis work ows. A traditional post-processing work ow involves storing simulation results to disk and later retrieving them for visualization and data analysis. However, at exascale, scien- tists and analysts will need a range of options for moving data to persistent storage, as the current o ine or post-processing pipelines will not be able to capture the data necessary for data analysis of these extreme scale simulations. This Milestone explores two alternate work ows, characterized as in situ and in transit, and compares them. We nd each to have its own merits and faults, and we provide information to help pick the best option for a particular use.

Rogers, David↗
NASA NTRS2012

Demystifying Kepler Data: A Primer for Systematic Artifact Mitigation

The Kepler spacecraft has collected data of high photometric precision and cadence almost continuously since operations began on 2009 May 2. Primarily designed to detect planetary transits and asteroseismological signals from solar-like stars, Kepler has provided high quality data for many areas of investigation. Unconditioned simple aperture time-series photometry are however affected by systematic structure. Examples of these systematics are differential velocity aberration, thermal gradients across the spacecraft, and pointing variations. While exhibiting some impact on Kepler's primary science, these systematics can critically handicap potentially ground-breaking scientific gains in other astrophysical areas, especially over long timescales greater than 10 days. As the data archive grows to provide light curves for 10(exp 5) stars of many years in length, Kepler will only fulfill its broad potential for stellar astrophysics if these systematics are understood and mitigated. Post-launch developments in the Kepler archive, data reduction pipeline and open source data analysis software have occurred to remove or reduce systematic artifacts. This paper provides a conceptual primer for users of the Kepler data archive to understand and recognize systematic artifacts within light curves and some methods for their removal. Specific examples of artifact mitigation are provided using data available within the archive. Through the methods defined here, the Kepler community will find a road map to maximizing the quality and employment of the Kepler legacy archive.

Kinemuchi, K.↗
DOE OSTI2017

Advances in Cross-Cutting Ideas for Computational Climate Science

This report presents results from the DOE-sponsored workshop titled, Advancing X-Cutting Ideas for Computational Climate Science Workshop,'' known as AXICCS, held on September 12--13, 2016 in Rockville, MD. The workshop brought together experts in climate science, computational climate science, computer science, and mathematics to discuss interesting but unsolved science questions regarding climate modeling and simulation, promoted collaboration among the diverse scientists in attendance, and brainstormed about possible tools and capabilities that could be developed to help address them. Emerged from discussions at the workshop were several research opportunities that the group felt could advance climate science significantly. These include (1) process-resolving models to provide insight into important processes and features of interest and inform the development of advanced physical parameterizations, (2) a community effort to develop and provide integrated model credibility, (3) including, organizing, and managing increasingly connected model components that increase model fidelity yet complexity, and (4) treating Earth system models as one interconnected organism without numerical or data based boundaries that limit interactions. The group also identified several cross-cutting advances in mathematics, computer science, and computational science that would be needed to enable one or more of these big ideas. It is critical to address the need for organized, verified, and optimized software, which enables the models to grow and continue to provide solutions in which the community can have confidence. Effectively utilizing the newest computer hardware enables simulation efficiency and the ability to handle output from increasingly complex and detailed models. This will be accomplished through hierarchical multiscale algorithms in tandem with new strategies for data handling, analysis, and storage. These big ideas and cross-cutting technologies for enabling breakthrough climate simulation advancements also need the "glue" of outreach and learning across the scientific domains to be successful. The workshop identified several strategies to allow productive, continuous engagement across those who have a broad knowledge of the various angles of the problem. Specific ideas to foster education and tools to make material progress were discussed. Examples include follow-on cross-cutting meetings that enable unstructured discussions of the types this workshop fostered. A concerted effort to recruit undergraduate and graduate students from all relevant domains and provide them experience, training, and networking across their immediate expertise is needed. This will broaden and expand their exposure to the future needs and solutions, and provide a pipeline of scientists with a diversity of knowledge and know-how. Providing real-world experience with subject matter experts from multiple angles may also motivate the students to attack these problems and even come up with the missing solutions.

54 ENVIRONMENTAL SCIENCES↗