Cloud computing in ocean and atmospheric sciences

CLOUD COMPUTING IN OCEAN AND ATMOSPHERIC SCIENCES Edited by TIFFANY C VANCE Alaska Fisheries Science Center, NOAA Fisheries, Seattle, WA, USA NAZILA MERATI Merati and Associates, Seattle, WA, USA CHAOWEI YANG George Mason University, Fairfax, VA, USA MAY YUAN Geospatial Information Sciences, School of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX, USA Amsterdam • Boston • Heidelberg • London New York • Oxford • Paris • San Diego San Francisco • Singapore • Sydney • Tokyo Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, UK 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, USA The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Copyright © 2016 Elsevier Inc All rights reserved Tiffany C.Vance’s editorial and chapter contributions to the Work are the work of a U.S Government employee No part of this publication may 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compounds, or experiments described herein In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-803192-6 For information on all Academic Press publications visit our website at https://www.elsevier.com/ Publisher: Janco Candice Acquisition Editor: Louisa Hutchins Editorial Project Manager: Rowena Prasad Production Project Manager: Paul Prasad Chandramohan Designer: Mark Rogers Typeset by TNQ Books and Journals In memory of Doug Nebert, whose gentle guidance and steadfast support was critical to many of the projects described in this book LIST OF CONTRIBUTORS A Arribas Met Office Informatics Lab, Exeter, UK K Butler Esri, Redlands, CA, USA H Caumont Terradue Srl, Rome, Italy G Cervone Pennsylvania State University, University Park, PA, USA B Combal IOC-UNESCO, Paris, France R Correa European Centre for Medium-Range Weather Forecasts, Reading, UK P Dhingra Microsoft Corporation, Seattle, WA, USA R Fatland University of Washington, Seattle, WA, USA D Gannon School of Informatics and Computing, Indiana University, Bloomington, IN, USA R Hogben Met Office Informatics Lab, Exeter, UK Q Huang University of Wisconsin–Madison, Madison, WI, USA C.N James Embry-Riddle Aeronautical University, Prescott, AZ, USA Y Jiang George Mason University, Fairfax,VA, USA J Li University of Denver, Denver, CO, USA W Li School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA K Liu George Mason University, Fairfax,VA, USA P MacCready University of Washington, Seattle, WA, USA xiii xiv List of Contributors B McKenna RPS ASA, Wakefield, RI, USA R Mendelssohn NOAA/NMFS/SWFSC, Santa Cruz, CA, USA N Merati Merati and Associates, Seattle, WA, USA A Merten NOAA, National Ocean Service, Seattle, WA, USA R Middleham Met Office Informatics Lab, Exeter, UK N Oscar Oregon State University, Corvallis, OR, USA T Powell Met Office Informatics Lab, Exeter, UK R Prudden Met Office Informatics Lab, Exeter, UK M Ramamurthy University Corporation for Atmospheric Research, Boulder, CO, USA B Raoult European Centre for Medium-Range Weather Forecasts, Reading, UK; University of Reading, Reading, UK N Robinson Met Office Informatics Lab, Exeter, UK M Saunby Met Office Informatics Lab, Exeter, UK J.L Schnase NASA Goddard Space Flight Center, Greenbelt, MD, USA H Shao School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA K Sheets NOAA, National Weather Service, Bohemia, NY, USA B Simons NOAA/NMFS/SWFSC, Santa Cruz, CA, USA A Sinha Esri Inc., Redlands, CA, USA S Stanley Met Office Informatics Lab, Exeter, UK List of Contributors xv K Tolle Microsoft Research, Seattle, WA, USA J Tomlinson Met Office Informatics Lab, Exeter, UK T.C Vance Alaska Fisheries Science Center, NOAA Fisheries, Seattle, WA, USA S Wang School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA J Weber University Corporation for Atmospheric Research, Boulder, CO, USA R.S Wigton Bin Software Co., Bellevue, WA, USA R Wright NOAA, National Ocean Service, Silver Spring, MD, USA S Wu School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA J Xia George Mason University, Fairfax,VA, USA C Yang George Mason University, Fairfax,VA, USA M Yuan Geospatial Information Sciences, School of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX, USA X Zhou School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA AUTHOR BIOGRAPHIES Alberto Arribas, Science Fellow at Met Office (United Kingdom) and Head of Informatics Lab The Informatics Lab combines scientists, software engineers, and designers to make environmental science and data useful We achieve this through innovation and experimentation, moving rapidly from concepts to working prototypes In the past, Alberto has led the development of monthly-to-seasonal forecasting systems, co-authored over 40 scientific papers, been a lecturer and committee member for organizations such as World Meteorological Organization or the US National Academy of Sciences and has been Associate Editor for the Quarterly Journal of the Royal Meteorological Society Kevin A Butler is a member of the Geoprocessing and Analysis team at Esri working primarily with the spatial statistics and multidimensional data tools He holds a Bachelor of Science degree in computer science from the University of Akron, and a doctorate in geography from Kent State University Prior to joining ESRI, he was a senior lecturer and manager of GIScience research at the University of Akron, where he taught courses in spatial statistics, geographic information system (GIS) programming, and database design Hervé Caumont Products & Solutions Program Manager at Terradue (http://www.terradue.com) is in charge of developing and maintaining the company’s business relationships across international projects and institutions This goes through the coordination of R&D activities co-funded by several European Commission projects, and the management of corporate programs for business development, product line innovation, and solutions marketing At the heart of this expertise, a set of flagship environmental systems designed for researchers with data-intensive requirements, and active contributions to the Open Geospatial Consortium (http://opengeospatial.org), the Global Earth Observations System of Systems (http://earthobservations.org), and the Helix Nebula European Partnership for Cloud Computing in Science (http://www.helix-nebula.eu) Guido Cervone is associate director of the Institute for CyberScience, director of the laboratory for Geoinformatics and Earth Observation, and associate professor of geoinformatics in the Department of Geography and Institute for CyberScience at The Pennsylvania State University In addition, xvii xviii Author Biographies he is affiliate scientist with the Research Application Laboratory (RAL) at the National Center of Atmospheric Research (NCAR) in Boulder, Colorado, and research fellow with the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign, Illinois He sits on the advisory committee of the United Nations Environmental Program (UNEP), Division of Early Warning and Assessment (DEWA) He received the Ph.D in Computational Science and Informatics in 2005 His fields of expertise are geoinformatics, machine learning, and remote sensing His research focuses on the development and application of computational algorithms for the analysis of spatiotemporal remote sensing, numerical modeling, and social media “Big Data.” The problem domains of his research are related to environmental hazards and renewable energy His research has been funded by Office of Naval Research (ONR), US Department of Transportation (USDOT), National Geospatial-Intelligence Agency (NGA), National Aeronautics and Space Administration (NASA), Italian Ministry of Research and Education, Draper Labs, and StormCenter Communications In 2013, he received the “Medaglia di Rappresentanza” from the President of the Italian Republic for his work related to the Fukushima crisis He does not own a cell phone He has sailed over 4000 offshore miles Bruno Combal studied atmospheric physics and has a Ph.D on radiative transfer modeling After 8 years of research on the assessment of vegetation biophysical parameters from space observations, he joined the European Commission Joint Research Center (JRC) in which he developed several satellite image-processing chains, and a computer system to process EumetCast data in near real time (eStation) Since December 2012, he has worked for the Intergovernmental Oceanographic Commission (IOC) of United Nations Educational, Scientific and Cultural Organization (UNESCO) in Paris, as a scientific data and scientific computing expert in the Ocean Observations and Services section Ricardo Correa, European Center for Medium-Range Weather Forecasts (ECMWF) Ricardo has been working at ECMWF since 1997 in a number of different analyst roles ranging from the design and deployment of a wide area Multiprotocol Label Switching (MPLS) private network for meteorological data to projects such as Distributed European Infrastructure for Supercomputing Applications (DEISA) for establishing a supercomputer grid coupling the distributed resources of 11 National Super-computing Services across Europe Currently, he leads the Network Applications Team and has a special interest in Cloud Computing, High-performance Computing, and distributed software design Author Biographies xix Prashant Dhingra is a Principal Program Manager with Microsoft where he works with data scientists and engineers to build a portfolio of Machine Learning models He works to identify gaps and feature requirement for Azure Machine Learning (ML) and related technology and to ensure models are built efficiently, performance and accuracy are good, and they have a good return on investment He is working with National Flood Interoperability Experiment (NFIE) to build a flood-forecasting solution Rob Fatland is the University of Washington Director of Cloud and Data Solutions From a background in geophysics and a career built on computer technology, he works on environmental data science and real-world relevance of scientific results; from carbon cycle coupling to marine microbial ecology to predictive modeling that can enable us to restore health to coastal oceans Dennis Gannon is a computer scientist and researcher working on the application of cloud computing in science His blog is at http://esciencegr oup.com From 2008 until he retired in 2014, he was with Microsoft Research (MSR) and MSR Connections as the Director of Cloud Research Strategy In this role, he helped provide access to cloud computing resources to over 300 projects in the research and education community Gannon is a professor emeritus of Computer Science at Indiana University and the former science director of the Indiana Pervasive Technology Labs His interests include large-scale cyber infrastructure, programming systems and tools, distributed and parallel computing, data analysis, and machine learning He has published more than 200 refereed articles and three co-edited books Richard Hogben is a computer programmer and communications expert His qualifications include a degree in physics, a diploma in Spanish, and a certificate in programming FORTRAN Prior to joining the Met Office, he taught science to teenagers in Zimbabwe and did statistical analysis for a government agency in London In recent years, he has worked on the development and support of the Met Office’s web applications He is now using his creative skills in the Informatics Lab Qunying Huang received her Ph.D in Earth System and Geoinformation Science from George Mason University in 2011 She is currently an Assistant Professor in the Department of Geography at University of Wisconsin–Madison Her fields of expertise are geographic information science (GIScience), cyberinfrastucture, Big Data mining, large-scale environmental modeling and simulation She is very interested in applying different computing models, such as cluster, grid, graphics processing unit (GPU), citizen computing, and especially cloud computing, to address contemporary computing challenges in GIScience Most recently, she is xx Author Biographies leveraging and mining social media data for various applications, such as emergency response, disaster coordination, and human mobility Curtis James is Professor of Meteorology and Department Chair of Applied Aviation Sciences at Embry–Riddle Aeronautical University (ERAU) in Prescott, Arizona He has taught courses in beginning meteorology, aviation weather, thunderstorms, satellite and radar imagery interpretation, atmospheric physics, mountain meteorology, tropical meteorology, and weather forecasting for over 16 years He has also served as Director of ERAU’s Undergraduate Research Institute and as faculty representative to the University’s Board of Trustees He participates in ERAU’s Study Abroad program, offering alternating summer programs each year in Switzerland and Brazil He earned a Ph.D in Atmospheric Sciences from the University of Washington (2004) and participated in the Mesoscale Alpine Program (MAP, 1999), an international field research project in the European Alps His research specialties include radar, mesoscale, and mountain meteorology He earned his B.S degree in Atmospheric Science from the University of Arizona (1995), during which time he gained operational experience as a student intern with the National Weather Service Forecast Office in Tucson, Arizona (1993–1995) Yongyao Jiang is a Ph.D student in Earth Systems and GeoInformation Sciences, at Department of Geography and GeoInformation Science and National Science Foundation (NSF) Spatiotemporal Innovation Center, George Mason University, Fairfax, Virginia Prior to Mason, he earned his M.S degree (2014) in GIScience from Clark University, Worcester, Massachusetts, and B.E degree (2012) in remote sensing from Wuhan University, Wuhan, China He has received the First Prize in the Robert Raskin CyberGIS student competition, Association of American Geographers His research interests range from geospatial cyberinfrastructure, to data mining, and spatial data quality Jing Li received her M.S degree in earth system science, and Ph.D In Earth System and Geoinformation Science from George Mason University, Fairfax,Virginia, in 2009 and 2012, respectively She is currently an Assistant Professor with the Department of Geography and the Environment, University of Denver, Denver, Colorado Her research interests include spatiotemporal data modeling, geovisualization, and geocomputation Wenwen Li is an assistant professor in GIScience at Arizona State University She obtained her B.S degree in Computer Science from Beijing Normal University (Beijing, China); M.S degree in Signal and Information Index ESGF See Earth System Grid Federation (ESGF) ESGF Compute Working Team (ESGF-CWT), 209–210 esgf-tools, 70 ESI map See Environmental Sensitivity Index map (ESI map) ESIP See Earth Science Information Partners (ESIP) ESRI Environmental Systems Research Institute (ESRI) ESRL See Earth System Research Laboratory (ESRL) ESSE See Error Subspace Statistical Estimation (ESSE) ETL models See Extract-Transform-Load models (ETL models) Eucalyptus-based cloud computing, 359–360 European Centre for Medium-Range Weather Forecasts (ECMWF), 25, 121, 207 ECMWF real-time dissemination, 122 European Molecular Biology Laboratory (EMBL), 124 European Organization for Nuclear Research (CERN), 124 European Space Agency (ESA), 66–67, 124 Event detection, 303 Event tracking, 303 Everything as a Service (XaaS), 388 Exabyte (EB), 96–97 Exeter Initiative for Science and Technology (ExIST), 105 ExpressRoute, 226 Extensible Markup Language (XML), 145, 169–170, 210 Extract-Transform-Load models (ETL models), 250, 253 F Facebook, 300–301 Family of analysis patterns, 18–19 FAQs See Frequently Asked Questions (FAQs) Fault tolerance, 227 401 Federal Emergency Management Agency (FEMA), 232–233 Federal GeoCloud project, 376, 378–380 Federal Geographic Data Committee See Federal Geospatial Data Committee (FGDC) Federal Geospatial Data Committee (FGDC), 2, 26–27, 168–169, 376–378 Federal government, 381 cloud service, 383 constraints on contracts, 382 ERD-DAP server, 383 funds, 382 I/O costs, 382–383 NOAA, 383–384 Federal Information Security Management Act (FISMA), 369 Federal Risk and Authorization Management Program (FedRAMP), 369 FEMA See Federal Emergency Management Agency (FEMA) FGDC See Federal Geospatial Data Committee (FGDC) Field Programmable Gate Arrays (FPGAs), 309 Fifth Assessment Report (AR5), 72, 202–203 file system backed by Amazon S3 (s3fs), 380 File transfer protocol (FTP), 96–97, 334 FISMA See Federal Information Security Management Act (FISMA) Flexibility, 309–310 Flood prediction, 231 Fog computing, 388–389 Four-phase categorization, 297 4D browser visualization collaboration and outreach external collaboration, 105 internal impact, 104 public engagement, 105 concept, 95–97 forecast cloud fields, 96f horizontal data-field slices, 100f implementation, 97–103 mobile controls, 103–104 process schematic, 98f user testing early iPad version, 103f 402 Index FPGAs See Field Programmable Gate Arrays (FPGAs) Frequently Asked Questions (FAQs), 325–326 FTP See File transfer protocol (FTP) G Gb See gigabits (Gb) GB See Gigabytes (GB) GCC See GNU Compiler Collection (GCC) GCI See Geospatial cyberinfrastructure (GCI); GEOSS Common Infrastructure (GCI) GCMD See Global Change Master Directory (GCMD) GCMs See General Circulation Models (GCMs) GÉANT, 127 GEBCO See General Bathymetric Chart of the Oceans (GEBCO) GEF See Global Environment Facility (GEF) General Bathymetric Chart of the Oceans (GEBCO), 19 General Circulation Models (GCMs), 347, 385–386 General Regularly-distributed Information in Binary form (GRIB)), 122, 250, 332–333 Generic lab approach, 91 approach to (physical and virtual) workspace, 92–94 approach to data and source code, 91–92 approach to it infrastructure, 94–95 approach to people, 91 GEO See Group on Earth Observation (GEO) “Geo-sensor” network, 298–299, 303 GeoCloud Sandbox, 2, 26–27 Geographic Information System (GIS), 24, 62, 143, 213, 251, 386 as a Service, 7–9, 24–25, 156 map, 21, 366 PostGIS, 167, 169 tools for Hadoop, 254–256, 255f Geographic JavaScript Object Notation (GeoJSON), 151, 253 Geographic Tagged Image File Format (Geo–TIFF), 197, 250 Geography Markup Language (GML), 151 GeoJSON See Geographic JavaScript Object Notation (GeoJSON) Geometry Supertype (ST_Geometry), 255 GeoNetwork, 167–169 Geophysical Fluid Dynamics Laboratory (GFDL), 74f GEOS See Goddard Earth Observing System (GEOS) GEOS-5 See Goddard Earth Observing System Data Assimilation System Version (GEOS-5) Geoscience Cyberinfrastructure See Geospatial cyberinfrastructure (GCI) Geospatial cyberinfrastructure (GCI), 138, 164 See also Atmosphere Analysis Cyberinfrastructure (A2CI) GEOSS See Global Earth Observation System of Systems (GEOSS) GEOSS Common Infrastructure (GCI), 61–62, 71, 79 Geostationary Operational Environmental Satellite (GOES), 52 Geo–TIFF See Geographic Tagged Image File Format (Geo–TIFF) GEOWOW project See Global Earth Observation interoperability for Weather, Ocean and Water project (GEOWOW project) GetFileNameByAttribute query method, 197 GetVariableBy_Operation_TemporalExtent_ SpatialExtent order method (GetVarByOpTeSe order method), 196, 199 GFDL See Geophysical Fluid Dynamics Laboratory (GFDL) GFS See Global Forecast System (GFS) GHz See Gigahertz (GHz) gigabits (Gb), 193 Gigabytes (GB), 193 Gigahertz (GHz), 193 GIS See Geographic Information System (GIS) GISS See Goddard Institute for Space Studies (GISS) GitHub, 71 Index Global Change Master Directory (GCMD), 144–145 Global Earth Observation interoperability for Weather, Ocean and Water project (GEOWOW project), 26–27, 61, 63, 79, 385–386 vision for GEOSS, 64t Global Earth Observation System of Systems (GEOSS), 26–27, 61, 171 vision for, 64t Global Environment Facility (GEF), 62–63 Global Forecast System (GFS), 332–333 Global namespace management, 206 Global Ocean Data Assimilation Experiment (GODAE), 66 Global Resource Information Database format (GRID format), 151 Global Warming Science course, 108 GLSL See OpenGL Shader Language (GLSL) GML See Geography Markup Language (GML) GNU Compiler Collection (GCC), 333–334 GODAE See Global Ocean Data Assimilation Experiment (GODAE) Goddard Earth Observing System (GEOS), 259–260 Goddard Earth Observing System Data Assimilation System Version (GEOS-5), 192–193 Goddard Institute for Space Studies (GISS), 348 GOES See Geostationary Operational Environmental Satellite (GOES) Google Inc., 68–69 GPU See Graphics processing unit (GPU) Graphical User Interface (GUI), 157, 167 A2CI, 157, 158f Graphics processing unit (GPU), 36, 99, 116, 309 GPU-based tasks, 349–350 Greenwich Sidereal Time (GST), 334 GRIB See General Regularly-distributed Information in Binary form (GRIB) GRID format See Global Resource Information Database format (GRID format) 403 Gridded data, 250–251 distributed processing of, 253–265 distributed database for climate dataset analytics, 264–265 GIS tools for Hadoop, 254–256, 255f Hadoop-GIS, 258–260, 259f MERRA analytics service, 263–264 SciHadoop, 263 Spatial Hadoop, 260–263, 261f spatial polygon-map-random tree indexing and analytics, 256–258 Gridded datasets, 251–252 Group on Earth Observation (GEO), 61 GST See Greenwich Sidereal Time (GST) GUI See Graphical User Interface (GUI) H HA/DR responders See Humanitarian Aid and Disaster Relief responders (HA/DR responders) HABs See Harmful Algal Blooms (HABs) Hadoop cluster deployment in cloud, 249–250 Hadoop Distributed File System (HDFS), 69, 193, 247–248, 248f Hadoop for large-scale datasets, 247–250 Hadoop cluster deployment in cloud, 249–250 HDFS, 247–248, 248f MR, 248–249, 249f Hadoop JobTracker, 69 Hadoop MapReduce Framework, 68–69 processing chain, 64–65 Hadoop-enabled processing chain, 76 Hadoop-GIS, 258–260, 259f Harmful Algal Blooms (HABs), 288 HDF See Hierarchical Data Format (HDF) HDF-EOS format See Hierarchical Data Format–Earth Observing System format (HDF-EOS format) HDFS See Hadoop Distributed File System (HDFS) Health Insurance Portability and Accountability Act (HIPAA), 227 Heartbeat, 247–248 HEC See High-end computing (HEC) Helix Nebula project, 404 Index Helix Nebula—The Science Cloud Initiative (HNI), 2, 27, 124 Hierarchical Data Format (HDF), 138, 250 HDF-4, 194 HDF5, 53 Hierarchical Data Format–Earth Observing System format (HDF-EOS format), 194 High performance, 309 data analytics platform, 189–190, 193 parallel computing, 349–350 “High value” computing, 108–109 High-end computing (HEC), 108–109, 116 High-level OAIS abstractions, 200 High-performance computing (HPC), 35, 138, 281–282, 336–337, 347–348 High-resolution forecast (HRES forecast), 122, 385–386 High-resolution imagery, 265–266 Higher-resolution imagery, 253 HIPAA See Health Insurance Portability and Accountability Act (HIPAA) Hive Query Language (HiveQL), 255 HiveQL See Hive Query Language (HiveQL) HiveSP, 259–260 HNI See Helix Nebula—The Science Cloud Initiative (HNI) HPC See High-performance computing (HPC) HRES forecast See High-resolution forecast (HRES forecast) HTML See Hypertext Markup Language (HTML) HTTP See Hypertext Transfer Protocol (HTTP) HTTP/HTTPS See Hypertext Transfer Protocol/HTTP Secure (HTTP/HTTPS) Humanitarian Aid and Disaster Relief responders (HA/DR responders), 298–299, 303–304 Humanity Road, 32 Hurricane Sandy, 315–316, 366, 368f Hybrid cloud, 11, 226 Hybrid Coordinate Ocean Model (HYCOM), 279, 333 HYCOM See Hybrid Coordinate Ocean Model (HYCOM) Hydrology, 230 Hypertext Markup Language (HTML), 145 Hypertext Transfer Protocol (HTTP), 96–97, 118, 132, 169–170, 200, 288 Hypertext Transfer Protocol/HTTP Secure (HTTP/HTTPS), 229 I I/O See Input/output (I/O) IaaS See Infrastructure as a Service (IaaS) IBM See International Business Machines (IBM) IBM’s Multimedia Analysis and Retrieval System (IMARS), 265–266 IDD See Internet Data Distribution (IDD) IDEs See Integrated development environments (IDEs) IDV See Integrated Data Viewer (IDV) IHO-IOC See International Hydrographic Organization–Intergovernmental Oceanographic Commission (IHO-IOC) Image classification, 265–266 Image processing, 266–268, 268f IMARS See IBM’s Multimedia Analysis and Retrieval System (IMARS) Individual US projects, InfoaaS See Information as a Service (InfoaaS) Informatics Lab, 90, 93, 93f Information as a Service (InfoaaS), 132–133 Information Collector, 168 Information security, 369 Information technology (IT), 47, 365–366, 375, 387 Information technology security officers (ITSOs), 370 Infrastructure as a Service (IaaS), 7–9, 17–18, 36, 109, 132, 139, 224–225, 309, 336, 350–351, 368, 385 Ingest methods, 195–196 Initialization method indicator, 72–73 Innovation, negative consequences for, 89–90 Input/output (I/O), 28–29, 253, 341–342, 381 Index InSAR See Interferometric Synthetic Aperture Radar (InSAR) Instances, 337 Integrated analytics, 210–213 Integrated Data Viewer (IDV), 30, 51–52 application-streaming cloud servers, 54–55 Integrated development environments (IDEs), 200, 287 Integrated Rule-Oriented Data System (iRODS), 197 Integration paths, 293–294 Interferometric Synthetic Aperture Radar (InSAR), 64–65 Intergovernmental Panel on Climate Change (IPCC), 28, 61–62, 202–203 Interim Reanalysis (ERA-Interim), 207 International Business Machines (IBM), 224, 249–250 International Hydrographic Organization– Intergovernmental Oceanographic Commission (IHO-IOC), 19 International Organization for Standardization (ISO), 164, 195, 376–378 International Polar Initiative (IPI), 163 International Polar Year (IPY), 163 International Traffic in Arms Regulations (ITAR), 113 Internet, 306 Internet Data Distribution (IDD), 49 Internet of Things technologies (IoT technologies), 389 Internet Protocol (IP), 118 Invariant attributes, 190 IOC/UNESCO, 80 IoT technologies See Internet of Things technologies (IoT technologies) IP See Internet Protocol (IP) IPCC See Intergovernmental Panel on Climate Change (IPCC) IPI See International Polar Initiative (IPI) iPlant, 211–212 collaborative’s discover environment interface, 212f Data Store, 212–213 IPY See International Polar Year (IPY) 405 iPython Notebook fragment, 284f Iris, 95 iRODS See Integrated Rule-Oriented Data System (iRODS) ISO See International Organization for Standardization (ISO) IT See Information technology (IT) ITAR See International Traffic in Arms Regulations (ITAR) ITSOs See Information technology security officers (ITSOs) J Japanese 25-year Reanalysis (JRA-25), 209 Japanese 55-year Reanalysis (JRA-55), 209 Java Web Application, 69 JavaScript Object Notation (JSON), 149 Jet Propulsion Laboratory (JPL), 64–65 Joint Photographic Experts Group (JPEG), 155 Jupyter Notebook technology, 52 K Keyhole Markup Language (KML), 155 Keyword-based search technologies, 165 L Lab working model, 104 Land-use data, 265–266 Landsat program, 252–253 LarvaMap, 2, 3f LDM See Local Data Manager (LDM); Logical Data Model (LDM) LEAD See Linked Environments for Atmospheric Discovery (LEAD) Leadership, 55–56 Learning Management Systems (LMSs), 108, 118–119 Light Detection and Ranging (Lidar), 15, 329 Linked Environments for Atmospheric Discovery (LEAD), 138 Linux AMI, 338 Linux operating systems, 109, 114–115 Linux RPM format, 68 406 Index LiveOcean (LO), 25, 277, 285f API details, 288–289 clients, 285–286 cloud middleware, 284–285 data structure, 279–283 iPython Notebook fragment, 284f middleware, 287 model simulations, 281f modular design, 286–288 parallel efforts, 277 physical domain, 280f project logo, 278f project motivation, 278 ROMS validation, 279 scenarios for liveocean use, 291–294 client paths, 294 growth and adoption of liveocean cloud solution, 293 integration paths, 293–294 year-over-year time scales, 291–292 technical components API, 283–284 ROMS model forecast generation, 279 validation, 289–290 LMSs See Learning Management Systems (LMSs) LO See LiveOcean (LO) Load-balance controls, 36 Local Data Manager (LDM), 115 Local data processing services, 142 Logical Data Model (LDM), 51–52 M Machine learning, 222–223, 228–229, 234–235 Machine-to-machine interoperability, 200 MADS See Meteorological Analysis & Diagnosis Software (MADS) Many Integrated Cores (MICs), 309 MapReduce (MR), 193–194, 247–249, 249f codeset proper, 195 MapReduce-based tools, 133 programming model, 68–69 Marine Geoscience Data System (MGDS), 23 Marine-Geo Digital Library, 23 MARS See Meteorological Archiving and Retrieval System (MARS) Massachusetts Institute of Technology (MIT), 108 Massive Online Open Courses (MOOCs), 108 Massive processing on cloud, 66–71 MB See Megabytes (MB) MBRs See Minimum Bounding Rectangles (MBRs) MD-HBase See Multidimensional HBase (MD-HBase) Measured service, Megabytes (MB), 252–253 MERRA See Modern-Era Retrospective Analysis for Research and Applications (MERRA) Message coding, 304–305 Message Passing Interface (MPI), 327 MPI-based tasks, 349–350 Message Passing Interface over CHameleon (MPICH), 349 Met Office, 89–91 Metadata, 168 Meteorological Analysis & Diagnosis Software (MADS), 114 METeorological and OCEANographic (MetOcean), 325 Meteorological Archiving and Retrieval System (MARS), 122 MetOcean See METeorological and OCEANographic (MetOcean) MetOcean Forecast System (MOFS), 325 development, 333–334 numerical model components, 332f runtime, 343f workflow in cloud implementation, 339f MGDS See Marine Geoscience Data System (MGDS) Microservices, 190–191 MICs See Many Integrated Cores (MICs) Minimum Bounding Rectangles (MBRs), 259–260 MIT See Massachusetts Institute of Technology (MIT) Mitigation, 297–298 Mobile controls, 103–104 Model domains, 327 ROMS oceanographic model, 329, 330f SWAN wave model, 329, 331f WRF model, 327–329, 328f Index ModelE, 355 See also Atmospheric modeling cloud computing to supporting massive model runs, 359–361 customizing to cloud framework, 356 identifying appropriate computing configurations, 357–359 Modern science, 15 Modern-Era Retrospective Analysis for Research and Applications (MERRA), 29, 192, 250, 385–386 analytics service, 263–264 data analytics system architecture, 189, 189f MERRA-2, 194, 209 Modern-Era Retrospective Analysis for Research and Applications Analytic Service (MERRA/AS), 192–199 Modern-Era Retrospective Analysis for Research and Applications Persistence Service (MERRA/PS), 192–199 MOFS See MetOcean Forecast System (MOFS) MOOCs See Massive Online Open Courses (MOOCs) MPI See Message Passing Interface (MPI) MPICH See Message Passing Interface over CHameleon (MPICH) MR See MapReduce (MR) Multi-model average, 215 Multi-PB dataset, 129 catalog, 131–132 cost of holding and serving data, 131 cost of pushing data, 130–131 data access models, 132–133 data volume, 130 research data to community, 129–130 Multidimensional HBase (MD-HBase), 260–261 Multidimensional visualization service, 153–154, 154f N NameNode, 247 NANOOS See Northwest Association of Networked Ocean Observing Systems (NANOOS) 407 NANOOS Visualization System (NVS), 294 NASA See National Aeronautics and Space Administration (NASA) NASA Center for Climate Simulation (NCCS), 246 National Aeronautics and Space Administration (NASA), 9, 48, 144–145, 164, 348, 385–386 National Center for Atmospheric Research (NCAR), 130, 232–233, 250 National Centers for Environmental Prediction (NCEP), 52–53, 207, 326 National Climatic Data Center (NCDC), 174–175 National Flood Interoperability Experiment (NFIE), 230–233, 235 analytics with Microsoft azure, 235–242 ADF, 237–238, 238f Azure machine learning, 238, 241f Azure VMs for running models, 236–237 Azure Web applications, 237 NFIE example, 239–242 National Geophysical Data Center (NGDC), 174–175 National Institute of Standards and Technology (NIST), 139 National Oceanic and Atmospheric Administration (NOAA), 19, 46, 52–53, 107, 147–148, 207, 221, 279, 365–366, 368, 383–384 National Oceanographic Data Center (NODC), 169–170 National Oceanographic Partnership Program (NOPP), 279 National Research and Education Networks (NRENs), 127 National Science Foundation (NSF), 16, 43, 138 National Snow and Ice Data Center (NSIDC), 147–148, 169–170 National Virtual Ocean Data System (NVODS), 75 National Weather Service (NWS), 52–53, 114, 232–233 408 Index Natural hazards, 297 case studies, 310 Earthquake in Haiti, 316–319 Hurricane Sandy, 315–316 Tsunami in Japan, 310–315 disaster management cloud computing to facilitate, 308–310 and coordination, 298 social media for, 300–308 visualizing and mining Tweets for, 317f NBD See Next business day (NBD) NCAR See National Center for Atmospheric Research (NCAR) NCCS See NASA Center for Climate Simulation (NCCS) NCDC See National Climatic Data Center (NCDC) NCEP See National Centers for Environmental Prediction (NCEP) Near real-time, 302 Network Common Data Format (NetCDF), 49, 138, 194, 197, 199, 236–237, 250, 281–282, 338, 355 files, 24, 71–72, 95 Network latency, 22–23 Network transfers, 130–131 NEXRAD See Next Generation Weather Radar (NEXRAD) Next business day (NBD), 380 Next Generation Weather Radar (NEXRAD), 52 NFIE See National Flood Interoperability Experiment (NFIE) NGDC See National Geophysical Data Center (NGDC) NIST See National Institute of Standards and Technology (NIST); US National Institute of Standards and Technology (NIST) NOAA See National Oceanic and Atmospheric Administration (NOAA) NODC See National Oceanographic Data Center (NODC) Nodes, 194 Nonrelational Structured Query Language (NoSQL), 264–265, 299, 311 Nontime sensitive processing parallelization, 94–95 NOPP See National Oceanographic Partnership Program (NOPP) Northwest Association of Networked Ocean Observing Systems (NANOOS), 294 NoSQL See Nonrelational Structured Query Language (NoSQL) NRENs See National Research and Education Networks (NRENs) NSF See National Science Foundation (NSF); US National Science Foundation (NSF) NSIDC See National Snow and Ice Data Center (NSIDC) Numerical weather predictions, 121 NumPy, 250–251 NVODS See National Virtual Ocean Data System (NVODS) NVS See NANOOS Visualization System (NVS) NWS See National Weather Service (NWS) O OAIPMH See Open Archives Initiative Protocol for Metadata Harvesting (OAIPMH) OAIS See Open Archival Information System (OAIS) OAIS Reference Model, 198, 200 OBIS See Ocean Biogeographic Information System (OBIS) Ocean Biogeographic Information System (OBIS), Ocean Exploration and Research (OER), 32 Ocean Observatories Initiative (OOI), 289 OER See Ocean Exploration and Research (OER) OGC See Open Geospatial Consortium (OGC) OGC Web Services (OWS), 144–145 On-demand self-service, OOI See Ocean Observatories Initiative (OOI) Oozie, 69 Index Open Archival Information System (OAIS), 195, 196f Open Archives Initiative Protocol for Metadata Harvesting (OAIPMH), 164 Open Geospatial Consortium (OGC), 23, 62, 140, 142, 164, 209–210, 255 Open Graphics Library (OpenGL), 99, 114 Open Science Data Cloud, 11 Open Source processing chain, 64–65 Open-Source Project for a Network Data Access Protocol (OPeNDAP), 72, 75–76, 79, 338 online resources, 70 Open-source software (OSS), 36–37 OPeNDAP See Open-Source Project for a Network Data Access Protocol (OPeNDAP) OpenGL See Open Graphics Library (OpenGL) OpenGL Shader Language (GLSL), 100–101 OpenLayers, 155 OpenNational Aeronautics and Space Administration Earth Exchange (OpenNEX), OpenSearch, 169–170 OpenStreetMap, 319, 320f Operating system (OS), 225, 256 Oracle VM Virtual Box, 117–118 Order methods, 195–196 Oregon State University Tidal Inversion Software (OTIS), 333 OS See Operating system (OS) OSS See Open-source software (OSS) OTIS See Oregon State University Tidal Inversion Software (OTIS) OWS See OGC Web Services (OWS) P PaaS See Platform as a Service (PaaS) Paged List Viewer, 174 Parallelism, 36 PARR See Public Access to Research Results (PARR) Pattern language, 18–21 “Pay after use” paradigm, 112–113 “Pay-as-you-go” 409 approach, service model, 174 PB See Petabyte (PB) PCMDI See Program for Climate Model Diagnosis and Intercomparison (PCMDI) Persistence service, 189, 191 Petabyte (PB), 121 PHP See PHP: Hypertext Preprocessor (PHP) PHP: Hypertext Preprocessor (PHP), 198 Pigeon (language layer), 261–262 Pivot Viewer, 174 Plankton, 291 Platform as a Service (PaaS), 7, 9, 25, 36, 67–68, 132–133, 138–141, 157, 224–225, 350–351, 368, 385 Plumes, 124 PMR tree See Polygon-Map-Random tree (PMR tree) PNG format See Portable Network Graphics format (PNG format) Polar CI Portal architecture, 166f challenges, 164–165 cloud-computing environments, 174 data harvesting middleware, 169–170, 170f data warehouse, 168–169 implementation, 167–168 objectives, 165–166 OGC filter fragment sample, 172t Polar Regions, 163–164 QoS engine, 173, 173f search broker, 168 semantic engine, 170–172 status data harvesting, 174–175 GUI of search result page, 180f–182f GUI of service quality viewer, 178f keyword and semantic-aided search comparison, 176f–177f QoS engine, 175–179 semantic engine, 175 visualization tools, 179 system architecture, 166–167 visualization tool, 173–174 Polar Cyberinfrastructure, 138 410 Index Polar geospatial resource discovery, 164 Polar Regions, 163–164 Polar Viewer, 174 PolarHub, 143–148, 144f Polygon-Map-Random tree (PMR tree), 254 Portable Network Graphics format (PNG format), 151 Portal, 165–166 PostGIS, 168–169 Preliminary experiments, 124–128 Preparedness, 298 Presentation Controller, 168 Private cloud, 10–12, 133–134, 225, 374 Program for Climate Model Diagnosis and Intercomparison (PCMDI), 72 Provisioning, Public Access to Research Results (PARR), 24 Public cloud, 11–12, 225 Publication support, 293 Python applications, 202 Q QLSP See Stored-procedure query language (QLSP) Quality of Service (QoS), 165–166 engine, 173, 173f, 175–179 Query methods, 195–196 Query Statement Creator, 168 Query/Feedback broker, 173 Query–response mechanism, 287 R RAID See Redundant array of independent disks (RAID) RAM See Random access memory (RAM) RAMM See Royal Albert Memorial Museum (RAMM) Random access memory (RAM), 249–250, 336 RAPID See Routing Application for Parallel Computation of Discharge (RAPID) Rapid elasticity, Raster-based atmospheric data, 151 Ray marching, 101 Ray tracing, 100, 102f RCMED See Regional Climate Model Evaluation System (RCMED) RCP See Representative Common Pathway (RCP) RDD See Resilient Distributed Datasets (RDD) RDF See Resource Description Framework (RDF) Real-time dissemination, ECMWF, 122 Real-Time Spatial Query Engine (RESQUE), 258 “Realization” number, 72–73 Reanalysis, 129 Reanalysis Ensemble Service (RES), 209 RECOVER See Restoration, Coordination, and Verification (RECOVER) Recovery, 298 Red Hat Enterprise Linux (RHEL), 116, 338 RedHat Package Manager (RPM), 68 Reduce function, 248 Redundant array of independent disks (RAID), 380 Region of interest (ROI), 281, 282f Regional Climate Model Evaluation System (RCMED), 138 Regional Ocean Model System (ROMS), 326 See also Simulating WAves Nearshore wave model (SWAN wave model); Weather Research and Forecasting atmospheric model (WRF atmospheric model) model domains, 330f oceanographic model, 329, 333 Regional Ocean Modeling System (ROMS), 277, 386 model forecast generation, 279 validation, 279 Remote data processing services, 142 Remote procedure call (RPC), 210 Renaissance Computing Institute (RENCI), 230–231 Repeat Orbit Interferometry Package processor (ROI_PAC processor), 64–65 Index Replication controller, 94 Representational State Transfer (REST), 132, 167, 198, 288, 376 communications module, 198 Representative Common Pathway (RCP), 61–62 RES See Reanalysis Ensemble Service (RES) Resilience, 89, 310 Resilient Distributed Datasets (RDD), 271 Resource Description Framework (RDF), 70 Resource pooling, Response, 298 RESQUE See Real-Time Spatial Query Engine (RESQUE) REST See Representational State Transfer (REST) RESTful See Representational State Transfer (REST) Restoration, Coordination, and Verification (RECOVER), 213 RHEL See Red Hat Enterprise Linux (RHEL) Risk, 59–60 assessment, 59–60 ROI See Region of interest (ROI) ROI_PAC processor See Repeat Orbit Interferometry Package processor (ROI_PAC processor) ROMS See Regional Ocean Model System (ROMS); Regional Ocean Modeling System (ROMS) Routing Application for Parallel Computation of Discharge (RAPID), 232–233 Royal Albert Memorial Museum (RAMM), 105 RPC See Remote procedure call (RPC) RPM See RedHat Package Manager (RPM) S S3 See Simple Storage Service (S3) s3fs See file system backed by Amazon S3 (s3fs) SaaS See Software as a Service (SaaS) 411 SaaS—Spatial Analysis as a Service (SAaaS), 153 Sandbox mode, 68 SAP See Systems Applications Products (SAP) Satellite imagery, 252–253 distributed processing, 265–268 image classification, 265–266 image processing, 266–268, 268f Scalability, 242 Scalable data processing, 78–81 See also Climate model output processing; Sharable data-processing Scalable Structured Query Language (Scalable SQL), 299 Science, 44–45 Scientific cloud computing See also Cloud computing collaboration and visualization, 39–40 forces and challenges in scientific cloud adoption, 36–37 forces and patterns in, 35 period of fit and retrofit, 35–38 Scientific reproducibility, 68 Scientific workflow, 22–23, 28–30, 36–37 SciHadoop, 263 SciPy, 250–251 SDI See Spatial Data Infrastructure (SDI) SDK See Software Development Kit (SDK) Sea surface temperature variable, 72 Search broker, 168 Search Processor, 168 Secure shell (SSH), 96–97 Security, 113, 118 Semantic engine, 170–172, 175 Semantic Web for Earth and Environmental Terminology (SWEET), 170–171 Service Bus, 226 Service models, 132 for cloud computing, 7–10 Service Quality Viewer, 174–179 Service-oriented data integration, 150–153, 152f Sharable data-processing chain, 81–82 See also Climate model output processing; Scalable data processing Sharepoint, 301 412 Index Sharpening algorithm, 266 Shellfish, 277, 285–286, 294–295 Silverlight, 167 Simple Notification Service (SNS), 99 Simple Object Access Protocol (SOAP), 167 Simple Queue Service (SQS), 99 Simple Storage Service (S3), 132 buckets, 343 Simulating WAves Nearshore wave model (SWAN wave model), 326, 329 See also Regional Ocean Model System (ROMS); Weather Research and Forecasting atmospheric model (WRF atmospheric model) data sources, 333 model domains, 331f SIP See Submition Information Package (SIP) Situational awareness, 298 SLES See SUSE Linux Enterprise Server (SLES) SNS See Simple Notification Service (SNS) SOAP See Simple Object Access Protocol (SOAP) Sobel filter, 266 Social bookmarking, 300 Social media challenges, 305 big data, 307–308 data quality, 306–307 digital divide, 306 data, 298–299 for disaster management, 300 fundamentals, 300–301 networks, 298–299 opportunities, 301–302 state-of-the-art work and practice, 302–305 damage assessment, 304 disaster response and relief, 303–304 event detection and tracking, 303 message coding, 304–305 user rank model, 305 in Tsunami in Japan, 311 Social networking, 300 Social news, 300 Social photo and video sharing, 300 Software as a Service (SaaS), 7, 9, 17–18, 67, 108, 132–133, 138–140, 224–225, 350–351, 368, 385 Software Development Kit (SDK), 325–326 Solid State Drive (SSD), 266–268 Solid-state storage architectures, 37 Spatial Analysis, Spatial Big Data, 245–246 holy grail of, 246–247 Spatial Data Infrastructure (SDI), 164 Spatial domain See Area of interest (AOI) Spatial extent, 190 Spatial Hadoop, 260–263, 261f differences in Spatial Hadoop frameworks, 269–271 Spatial indexing, 271 Spatial polygon-map-random tree indexing and analytics, 256–258 Spatial Reference Systems (SRSs), 151–153, 155 SpatialFileSplitter, 262 SpatialRecordReader, 262 Spinning up See Provisioning SQL See Structured Query Language (SQL) SQS See Simple Queue Service (SQS) SRSs See Spatial Reference Systems (SRSs) SSD See Solid State Drive (SSD) SSH See Secure shell (SSH) ST_Geometry See Geometry Supertype (ST_Geometry) Standard OGC OpenSearch Geo & Time extension interface, 82 Statistical/Post Processor, 168 Stored-procedure query language (QLSP), 258 Stressors, 60 Structured Query Language (SQL), 168–169, 224, 251, 299 Submition Information Package (SIP), 197–198 Support Vector Machine (SVM), 265–266 Supporting marine sciences with cloud services bridging technical gaps between scientific communities, 62–71 Index climate model output processing, 72–78 processing service, 61–62 GEOSS, 61 GEOWOW project, 61 human and natural system relationship, 59, 60f scalable data processing, 78–81 sharable data-processing chain, 81–82 SUSE Linux Enterprise Server (SLES), 197 SVM See Support Vector Machine (SVM) SWAN wave model See Simulating WAves Nearshore wave model (SWAN wave model) SWEET See Semantic Web for Earth and Environmental Terminology (SWEET) System architecture development, 333–334 first implementation, 334–335 second implementation, 335–336 System interface, 189, 191–192, 198 Systems Applications Products (SAP), 224 T TACC See Texas Advanced Computing Center (TACC) Tagged Image File Format (TIFF), 155 Tapes, sending, 130 TB See Terabytes (TB) TCP See Transmission Control Protocol (TCP) TCP/IP See Transmission Control Protocol/ Internet Protocol (TCP/IP) Temperature Of Surface (TOS), 72, 76 climatology, 78 Temporal extent, 190 Terabytes (TB), 66, 122, 193, 245–246, 380 Teraflops (TF), 193 Terradue Cloud Platform, 66–67 Texas Advanced Computing Center (TACC), 236 TF See Teraflops (TF) Thematic Real-Time Environmental Distributed Data Services (THREDDS), 25, 49, 94, 168–169, 385–386 413 Thetao, 76 Thick and thin clients, THREDDS See Thematic Real-Time Environmental Distributed Data Services (THREDDS) 3D visualization service, 155–156, 159f TIFF See Tagged Image File Format (TIFF) TIGER See Topologically Integrated Geographic Encoding and Referencing (TIGER) Tiled map visualization, 155 Tiles, 258–259 Timestamp, 194–195 Tobler’s First Law of Geography, 246–247 Topographic JSON (TopoJSON), 156 Topologically Integrated Geographic Encoding and Referencing (TIGER), TOS See Temperature Of Surface (TOS) Transboundary Water Assessment Programme (TWAP), 62–63 Transmission Control Protocol (TCP), 127 Transmission Control Protocol/Internet Protocol (TCP/IP), 127, 193 Tsunami in Japan, 310–315 TWAP See Transboundary Water Assessment Programme (TWAP) Tweedr, 303–304 20th Century Reanalysis (20CR), 209 Twitter, 300–301, 311, 312f Twittermining tool, 303–304 2015 Unidata Users Workshop, 56 2D visualization service, 154–155 U UAE See United Arab Emirates (UAE) UCAR See University Corporation for Atmospheric Research (UCAR) UDF See User Defined Functions (UDF) UIs See User Interfaces (UIs) Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT), 71 UML See Unified Modeling Language (UML) UNEP See United Nations Environment Program (UNEP) 414 Index UNESCO-IOC See United Nations Educational Scientific and Cultural Organization-Intergovernmental Oceanographic Commission (UNESCO-IOC) Unidata, 43, 50, 55–56 AWIPS II software, 53 cloud-related activities, 51–54 AWIPS II, 52–54 Docker container technology, 51–52 product generation, 52 familiarity in helping geoscientists, 44 program, 43 strategic plan, 44 Unified Modeling Language (UML), 148, 148f Uniform Resource Identifier (URI), 264–265 Uniplexed Information and Computing System (UNIX), 109, 206 United Arab Emirates (UAE), 325 United Kingdom (UK), 16 United Nations Educational Scientific and Cultural Organization-­ Intergovernmental Oceanographic Commission (UNESCO-IOC), 62–63 United Nations Environment Program (UNEP), 62–63, 202–203 United Parcel Service (UPS), 306–307 United States (US), 15 Universal Resource Locator (URL), 69, 132, 143, 202, 285–286 Universal Serial Bus (USB), 130 University Corporation for Atmospheric Research (UCAR), 43 UNIX See Uniplexed Information and Computing System (UNIX) UPS See United Parcel Service (UPS) URI See Uniform Resource Identifier (URI) URL See Universal Resource Locator (URL) US Army Corps of Engineers (USACE), 232–233 US Geological Survey (USGS), 147–148, 171, 232–233 US National Institute of Standards and Technology (NIST), 1, 385 US National Science Foundation (NSF), 211–212 USACE See US Army Corps of Engineers (USACE) USB See Universal Serial Bus (USB) User Defined Functions (UDF), 254–255 User Interfaces (UIs), 164, 168 User rank model, 305 USGS See US Geological Survey (USGS) USGS ecosystem WMS, 171 UV-CDAT See Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) UV-CDAT-cdms2 “Climate Data Analysis Tools”, 71 V Very high frequency (VHF), 286 vGPU See Virtual graphics processing unit (vGPU) VHF See Very high frequency (VHF) Virtual graphics processing unit (vGPU), 116 Virtual Machine (VM), 39, 51, 117–118, 127, 223–224, 228 Virtual Reality (VR), 40 Visualization, 39–40, 353–355 examples, 361f functions, 360–361 interface, 355f tools, 173–174, 179 VM See Virtual Machine (VM) Volunteer computing, 350 VR See Virtual Reality (VR) W W3C See World Wide Web Consortium (W3C) “Water wms”, 175 Waterfall-system life-cycle design, 372 WAVEWATCH III (WW3), 333 WCF See Windows Communication Foundation (WCF) WCPS See Web Coverage Processing Service (WCPS) WCRP See World Climate Research Programme (WCRP) WCS See Web Coverage Service (WCS) Index Weather, 221 Weather forecast, 124 Weather Research and Forecasting atmospheric model (WRF atmospheric model), 326, 328f See also Regional Ocean Model System (ROMS); Simulating WAves Nearshore wave model (SWAN wave model) data sources, 332–333 model domains, 327–329 Weather Research and Forecasting Environmental Modeling System (WRF EMS), 113–114 Weather Research and Forecasting–­Nonhydrostatic Mesoscale Model (WRF-NMM), 347–348 weather@home project, 350 Web client, 168 Web Coverage Processing Service (WCPS), 140 Web Coverage Service (WCS), 151 Web data services, 382 Web Distributed Authoring and Versioning (WebDAV), 168–169 Web Feature Service (WFS), 151 Web Graphics Library (WebGL), 156 Web Map Service (WMS), 151, 171 Web Map Tile Service (WMTS), 156 Web portal, 352–353 See also Polar CI Portal Web Processing Service (WPS), 71, 140, 153, 210 Web services, 62 “Web site/portal” approach, 284 WebDAV See Web Distributed Authoring and Versioning (WebDAV) WebGL, 99, 102 WebGL See Web Graphics Library (WebGL) Well Known Binary (WKB), 255–256 Well Known Text (WKT), 255–256 WFS See Web Feature Service (WFS) Wikipedia, 300 Wikis, 300–301 415 Wildfire decision support system, 213 Windows Communication Foundation (WCF), 167 Windows XML Setup file (WxS), 168–169 WKB See Well Known Binary (WKB) WKT See Well Known Text (WKT) WMO See World Meteorological Organization (WMO) WMS See Web Map Service (WMS) WMTS See Web Map Tile Service (WMTS) WordPress, 301 World Climate Research Programme (WCRP), 72 World Meteorological Organization (WMO), 202–203 World Ocean Basemap, 19 World Sea Surface Temperature (World SST), 145 World Wide Web Consortium (W3C), 200 Worldwide Telescope (WWT), 286 WPS See Web Processing Service (WPS) WRF atmospheric model See Weather Research and Forecasting atmospheric model (WRF atmospheric model) WRF EMS See Weather Research and Forecasting Environmental Modeling System (WRF EMS) WRF-NMM See Weather Research and Forecasting–Nonhydrostatic Mesoscale Model (WRF-NMM) WW3 See WAVEWATCH III (WW3) WWT See Worldwide Telescope (WWT) WxS See Windows XML Setup file (WxS) X X Virtual Frame Buffer (Xvfb), 52 XaaS See Everything as a Service (XaaS) XML See Extensible Markup Language (XML) Y YouTube, 300–301 yum install esgf-tools, 70 ... disciplines of oceanic and atmospheric sciences Distributed computing and resource sharing toward developing models and sharing scientific results are not new concepts to science Grid computing and. .. use of cloud computing in the atmospheric and oceanographic sciences Rather than being an introduction to the infrastructure of cloud computing, the authors focus on scientific applications and. .. processing unit (GPU), citizen computing, and especially cloud computing, to address contemporary computing challenges in GIScience Most recently, she is xx Author Biographies leveraging and mining