Earth and Space Science Informatics [IN]

IN41A
 MC:Hall D  Thursday  0800h

Visualizing Scientific Data Using KML and Virtual Globes I Posters


Presiding:  M Weiss-Malik, Google Inc.; J Dehn, University of Alaska Fairbanks

IN41A-1117

Alaska Volcano Observatory's KML Tools

* Valcic, L lovro@giseis.alaska.edu, Alaska Volcano Observatory/Geophysical Institute, University of Alaska Fairbanks (UAF), 903 Koyukuk Drive, Fairbanks, AK 99775, United States
Webley, P W pwebley@gi.alaska.edu, Arctic Region Super Computing Center, University of Alaska Fairbanks (UAF), 909 Koyukuk Drive, Fairbanks, AK 99775, United States
Webley, P W pwebley@gi.alaska.edu, Alaska Volcano Observatory/Geophysical Institute, University of Alaska Fairbanks (UAF), 903 Koyukuk Drive, Fairbanks, AK 99775, United States
Bailey, J E jbailey@gi.alaska.edu, Arctic Region Super Computing Center, University of Alaska Fairbanks (UAF), 909 Koyukuk Drive, Fairbanks, AK 99775, United States
Bailey, J E jbailey@gi.alaska.edu, Alaska Volcano Observatory/Geophysical Institute, University of Alaska Fairbanks (UAF), 903 Koyukuk Drive, Fairbanks, AK 99775, United States
Dehn, J jdehn@gi.alaska.edu, Alaska Volcano Observatory/Geophysical Institute, University of Alaska Fairbanks (UAF), 903 Koyukuk Drive, Fairbanks, AK 99775, United States

Virtual Globes are now giving the scientific community a new medium to present data, which is compatible across multiple disciplines. They also provide scientists the ability to display their data in real-time, a critical factor in hazard assessment. The Alaska Volcano Observatory remote sensing group has developed Keyhole Markup Language (KML) tools that are used to display satellite data for volcano monitoring and forecast ash cloud movement. The KML tools allow an analyst to view the satellite data in a user-friendly web based environment, without a reliance on non-transportable, proprietary software packages. Here, we show how the tools are used operationally for thermal monitoring of volcanic activity, volcanic ash cloud detection and dispersion modeling, using the Puff model.

http://avo- animate.images.alaska.edu/

IN41A-1118

Enhancements and Evolution of the Real Time Mission Monitor

* Goodman, M michael.goodman@nasa.gov, NASA Marshall Space Flight Center, 320 Sparkman Drive, Huntsville, AL 35805, United States
Blakeslee, R rich.blakeslee@nasa.gov, NASA Marshall Space Flight Center, 320 Sparkman Drive, Huntsville, AL 35805, United States
Hardin, D dhardin@itsc.uah.edu, The University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, AL 35899, United States
Hall, J john.hall@nasa.gov, The University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, AL 35899, United States
He, Y mhe@itsc.uah.edu, The University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, AL 35899, United States
Regner, K kregner@itsc.uah.edu, The University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, AL 35899, United States

The Real Time Mission Monitor (RTMM) is a visualization and information system that fuses multiple Earth science data sources, to enable real time decision-making for airborne and ground validation experiments. Developed at the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center, RTMM is a situational awareness, decision-support system that integrates satellite imagery, radar, surface and airborne instrument data sets, model output parameters, lightning location observations, aircraft navigation data, soundings, and other applicable Earth science data sets. The integration and delivery of this information is made possible using data acquisition systems, network communication links, network server resources, and visualizations through the Google Earth virtual earth application. RTMM has proven extremely valuable for optimizing individual Earth science airborne field experiments. Flight planners, mission scientists, instrument scientists and program managers alike appreciate the contributions that RTMM makes to their flight projects. RTMM has received numerous plaudits from a wide variety of scientists who used RTMM during recent field campaigns including the 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA), 2007 Tropical Composition, Cloud, and Climate Coupling (TC4), 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) missions, the 2007-2008 NOAA-NASA Aerosonde Hurricane flights and the 2008 Soil Moisture Active-Passive Validation Experiment (SMAP-VEX). Improving and evolving RTMM is a continuous process. RTMM recently integrated the Waypoint Planning Tool, a Java-based application that enables aircraft mission scientists to easily develop a pre-mission flight plan through an interactive point-and-click interface. Individual flight legs are automatically calculated for altitude, latitude, longitude, flight leg distance, cumulative distance, flight leg time, cumulative time, and satellite overpass intersections. The resultant flight plan is then generated in KML and quickly posted to the Google Earth-based RTMM for planning discussions, as well as comparisons to real time flight tracks in progress. A description of the system architecture, components, and applications along with reviews and animations of RTMM during the field campaigns, plus planned enhancements and future opportunities will be presented.

http://rtmm.nsstc.nasa.gov/

IN41A-1119

Virtual Globe Visualizations of Cryospheric Data at the National Snow and Ice Data Center

* Gergely, K kara.gergely@nsidc.org, National Snow and Ice Data Center, NSIDC/CIRES 449 UCB University of Colorado, Boulder, CO 80309-0449, United States
Haran, T tharan@nsidc.org, National Snow and Ice Data Center, NSIDC/CIRES 449 UCB University of Colorado, Boulder, CO 80309-0449, United States
Billingsley, B brendon.billingsley@nsidc.org, National Snow and Ice Data Center, NSIDC/CIRES 449 UCB University of Colorado, Boulder, CO 80309-0449, United States

Virtual globes do a fantastic job of rendering geolocated imagery on a 3D earth. For just the cost of creating compatible imagery, members of the scientific community can develop data visualization capabilities with features such as 3D perspective, zoom, variable transparency, overlays, and time series animation. The National Snow and Ice Data Center (NSIDC) has been creating imagery to visualize our data holdings in virtual globes for several years with considerable success. But since different kinds of data have different visualization needs, we are constantly looking for new ways to use virtual globe technologies to help the earth science community. This presentation highlights a number of projects currently in progress at NSIDC. In particular the MODIS Mosaic of Antarctica (MOA) Image Map is featured in Google Earth and ArcGlobe.

http://nsidc.org/data/virtual_globes

IN41A-1120

The New USGS Volcano Hazards Program Web Site

* Venezky, D Y dvenezky@usgs.gov, Volcano Hazards Team, 345 Middlefield Road, MS 910, Menlo Park, CA 94025, United States
Graham, S E sgraham@usgs.gov, Cascades Volcano Observatory, 1300 SE Cardinal Court, 100, Vancouver, WA 98683, United States
Parker, T J tparker@usgs.gov, Alaska Volcano Observatory, 4200 University Drive, Anchorage, AK 99508, United States
Snedigar, S F seth.snedigar@alaska.edu, Alaska Volcano Observatory, DGGS, 3354 College Road, Fairbanks, AK 99709, United States

The U.S. Geological Survey's (USGS) Volcano Hazard Program (VHP) has launched a revised web site that uses a map-based interface to display hazards information for U.S. volcanoes. The web site is focused on better communication of hazards and background volcano information to our varied user groups by reorganizing content based on user needs and improving data display. The Home Page provides a synoptic view of the activity level of all volcanoes for which updates are written using a custom Google® Map. Updates are accessible by clicking on one of the map icons or clicking on the volcano of interest in the adjacent color-coded list of updates. The new navigation provides rapid access to volcanic activity information, background volcano information, images and publications, volcanic hazards, information about VHP, and the USGS volcano observatories. The Volcanic Activity section was tailored for emergency managers but provides information for all our user groups. It includes a Google® Map of the volcanoes we monitor, an Elevated Activity Page, a general status page, information about our Volcano Alert Levels and Aviation Color Codes, monitoring information, and links to monitoring data from VHP's volcano observatories: Alaska Volcano Observatory (AVO), Cascades Volcano Observatory (CVO), Long Valley Observatory (LVO), Hawaiian Volcano Observatory (HVO), and Yellowstone Volcano Observatory (YVO). The YVO web site was the first to move to the new navigation system and we are working on integrating the Long Valley Observatory web site next. We are excited to continue to implement new geospatial technologies to better display our hazards and supporting volcano information.

http://volcanoes.usgs.gov

IN41A-1121

Efficiently Communicating Rich Heterogeneous Geospatial Data from the FeMO2008 Dive Cruise with FlashMap on EarthRef.org

* Minnett, R C rminnett@ucsd.edu, Institute of Geophysics and Planetary Physics, Scripps Institute of Oceanography University of California, San Diego, San Diego, CA 92093, United States
Koppers, A A akoppers@coas.oregonstate.edu, Marine Geology and Geophysics, College of Oceanic & Atmospheric Sciences Oregon State University, Corvallis, OR 97331, United States
Staudigel, D dstaudigel@gmail.com, Institute of Geophysics and Planetary Physics, Scripps Institute of Oceanography University of California, San Diego, San Diego, CA 92093, United States
Staudigel, H hstaudigel@ucsd.edu, Institute of Geophysics and Planetary Physics, Scripps Institute of Oceanography University of California, San Diego, San Diego, CA 92093, United States

EarthRef.org is comprehensive and convenient resource for Earth Science reference data and models. It encompasses four main portals: the Geochemical Earth Reference Model (GERM), the Magnetics Information Consortium (MagIC), the Seamount Biogeosciences Network (SBN), and the Enduring Resources for Earth Science Education (ERESE). Their underlying databases are publically available and the scientific community has contributed widely and is urged to continue to do so. However, the net result is a vast and largely heterogeneous warehouse of geospatial data ranging from carefully prepared maps of seamounts to geochemical data/metadata, daily reports from seagoing expeditions, large volumes of raw and processed multibeam data, images of paleomagnetic sampling sites, etc. This presents a considerable obstacle for integrating other rich media content, such as videos, images, data files, cruise tracks, and interoperable database results, without overwhelming the web user. The four EarthRef.org portals clearly lend themselves to a more intuitive user interface and has, therefore, been an invaluable test bed for the design and implementation of FlashMap, a versatile KML-driven geospatial browser written for reliability and speed in Adobe Flash. FlashMap allows layers of content to be loaded and displayed over a streaming high-resolution map which can be zoomed and panned similarly to Google Maps and Google Earth. Many organizations, from National Geographic to the USGS, have begun using Google Earth software to display geospatial content. However, Google Earth, as a desktop application, does not integrate cleanly with existing websites requiring the user to navigate away from the browser and focus on a separate application and Google Maps, written in Java Script, does not scale up reliably to large datasets. FlashMap remedies these problems as a web-based application that allows for seamless integration of the real-time display power of Google Earth and the flexibility of the web without losing scalability and control of the base maps. Our Flash-based application is fully compatible with KML (Keyhole Markup Language) 2.2, the most recent iteration of KML, allowing users with existing Google Earth KML files to effortlessly display their geospatial content embedded in a web page. As a test case for FlashMap, the annual Iron-Oxidizing Microbial Observatory (FeMO) dive cruise to the Loihi Seamount, in conjunction with data available from ongoing and published FeMO laboratory studies, showcases the flexibility of this single web-based application. With a KML 2.2 compatible web-service providing the content, any database can display results in FlashMap. The user can then hide and show multiple layers of content, potentially from several data sources, and rapidly digest a vast quantity of information to narrow the search results. This flexibility gives experienced users the ability to drill down to exactly the record they are looking for (SERC at Carleton College's educational application of FlashMap at http://serc.carleton.edu/sp/erese/activities/22223.html) and allows users familiar with Google Earth the ability to load and view geospatial data content within a browser from any computer with an internet connection.

http://earthref.org/SBN/

IN41A-1122

Sea-floor Geology and Benthic Habitats of the San Pedro Shelf, California: the View in Google Earth

* Wong, F L fwong@usgs.gov, U.S. Geological Survey, 345 Middlefield Road, MS 999, Menlo Park, CA 94025,
Edwards, B D bedwards@usgs.gov, U.S. Geological Survey, 345 Middlefield Road, MS 999, Menlo Park, CA 94025,
Dartnell, P pdartnell@usgs.gov, U.S. Geological Survey, 345 Middlefield Road, MS 999, Menlo Park, CA 94025,
Phillips, E L ephillips@usgs.gov, U.S. Geological Survey, 345 Middlefield Road, MS 999, Menlo Park, CA 94025,

The mainland shelf offshore of San Pedro in southern California is made up of a variety of geological materials and rich biological communities. The U.S. Geological Survey, in cooperation with Los Angeles and Orange County Sanitation District, surveyed the sea floor from the shoreline to a depth of 100 m with multibeam sonar, sediment samples, and still and video photography. Results of these surveys include detailed seafloor bathymetric data, seafloor facies interpreted from acoustic-backscatter data, sediment texture, seafloor photographs and video, and descriptions of plants and animals. These data sets are organized in a Geographic Information System (GIS) for spatial analyses. Virtual globes such as Google Earth add an intuitive and accessible tool for researchers and stakeholders to explore these vast data sets. Mud, sand, and bare-rock surfaces identified in the facies map are spatially correlated to still and video photographic images of these surfaces and the biologic communities that prefer or avoid particular geologic surfaces.

http://soundwaves.usgs.gov/2005/02/fieldwork2.html

IN41A-1123

Tsunami Inundation Mapping With KML

* West, D daw@gi.alaska.edu, Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, United States
Suleimani, E elena@gi.alaska.edu, Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, United States
Hansen, R roger@giseis.alaska.edu, Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, United States

The Geophysical Institute of the University of Alaska Fairbanks participates in the National Tsunami Hazard Mitigation Program by evaluating and mapping potential tsunami inundation of selected coastal communities in Alaska. Tsunami waves are a threat for many Alaska coastal locations, and community education and preparedness plays an important role in saving lives and property. We develop hypothetical tsunami scenarios based on the parameters of potential underwater earthquakes and landslides for a specified coastal community. The modeling results are delivered to the community for local tsunami hazard planning and construction of evacuation maps. While we spend most of our days mired in processing elevation data and producing predictive models of tsunami events, we are faced with the equally thorny challenge of determining how this data will be disseminated to the general public and emergency officials in at-risk areas. Distribution of these results is made trickier in Alaska by the small populations and resultant dearth of resources available in many coastal communities, most of which have no local GIS capabilities or dedicated disaster planning staff. Though the bulk of our modeled results are still formally distributed in professional GIS formats and static illustrations, we have found the simplicity and flexibility of the KML platform to be useful in education and circulation to a wider audience, namely those residents, students and community officials most at risk. With a relatively low-end computer and an internet connection, residents can explore the visualizations we produce in 3 and 4 dimensions, gaining a strong impression of what to expect and how to react in the case of a tsunami. We will demonstrate some of these, including "on-earth" animations of tsunami wave inundation within Resurrection Bay near Seward, Alaska.

IN41A-1124

Virtual Ocean, an Example of Augmenting KML to Move Beyond Visualization to Analysis and Synthesis

Ryan, W B billr@ldeo.columbia.edu, Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, NY 10964, United States
* Coplan, J O joc0225@rit.edu, Rochester Institute of Technology, Department of Computer Science, 102 Lomb Memorial Drive, Rochester, NY 14623-5608, United States

As important and revolutionary as the Keyhole MarkUp Language (KML) has been to the widespread use of virtual globes for visualization of geospatial data, there is a lack of adequate structure for the content of the tag that is most commonly used to deliver associated data objects. Our Virtual Ocean globe (www.virtualocean.org) offers a prototype solution using a spreadsheet or an ascii stream with XML tags from a Web Feature Service to supply the content. This solution has the necessary consistent and self- described format to allow interaction with the symbols, polygons and images so that the virtual globe becomes a tool for interactive data manipulation and analysis. Although the and tags now permit the efficient viewing of large images as individual tiles, the required code within a single kml file is rather intricate and lengthy and must be customized for each large image. This complexity can be reduced with a short and simple kml file linked to a cgi-script in a network server. The server script computes and returns the necessary image tiles to the virtual globe as areas come into view with changing perspectives and resolutions. This "pseudo" Web Mapping Service demands little computational overhead from either the virtual globe or the server. A large number of placemarks or polygons often reduces performance in a virtual globe if the graphics are to be constantly recomputed and redrawn as individual line segments and icons. We offer a solution in Virtual Ocean where the graphics are drawn to transparent images and just the image tiles are displayed. Thus the graphics engine in the client computer is only called upon to wrap the resulting images into their respective locations. Consequently thousands of polygons and tens of thousands of placemarks can be displayed and manipulated without degradation of performance. Virtual Ocean uses Java classes adapted from our GeoMapApp application and NASA World Wind.

http://www.virtualocean.org

IN41A-1125

World Wind Java Enabling Technology

* Hogan, P Patrick.Hogan@nasa.gov, NASA, Ames Research Center, Moffett Field, CA 94035, United States
Gaskins, T Tom.Gaskins@nasa.gov, NASA, Ames Research Center, Moffett Field, CA 94035, United States
Bailey, J E jbailey@gi.alaska.edu, Arctic Region Supercomputing Center, 909 Koyukuk Drive, Fairbanks, AK 99775, United States

World Wind Java (WWJ) solves the Geo-Browser problem of functionality being controlled and constrained largely by the browser's manufacturer. WWJ is a plug-in providing an Earth context for web-based or stand alone applications. It does the hard work of: terrain generation from real, remote data at high frequency; image display and selection from terabytes of remote imagery; and rapid management of data retrieval from distributed sources. World Wind Java is cross-platform and open-source, and therefore accessible to all. WWJ is not an application, it is enabling technology that makes it possible to include virtual globe technology in support of your particular objectives.

http://worldwind.arc.nasa.gov/java/index.html

IN41A-1126

Using the WorldWind at the MM5 Grid Portal

* Kwon, O okkwon@kisti.re.kr, KISTI, P.O BOX 122, Yu-seoung Gu, Daejeon, 305-333, Korea, Republic of
Joh, M msjoh@kisti.re.kr, KISTI, P.O BOX 122, Yu-seoung Gu, Daejeon, 305-333, Korea, Republic of
Hahm, J jaehahm@kisti.re.kr, KISTI, P.O BOX 122, Yu-seoung Gu, Daejeon, 305-333, Korea, Republic of
Lee, P pwlee@kisti.re.kr, KISTI, P.O BOX 122, Yu-seoung Gu, Daejeon, 305-333, Korea, Republic of

The MM5 Grid Portal is the weather-GRID integrated portal to produce, analyze and visualize weather information on the computational GRID infrastructure. The MM5 is a limited-area, nonhydrostatic, terrain- following sigma coordinate model designed to simulate mesoscale atmospheric circulation. The portal is consisted of 4 building blocks to process the MM5 as follows: Terrain-, Pre-, Main-, and Post-Processing. We use the WorldWind to visualize the input and result data from the each block at the portal. The WorldWind is a virtual globe developed by NASA. The program overlays satellite imagery, aerial photography, topographic maps and publicly available GIS data on 3D models of the Earth. The Terrain-Processing lets users design mesoscale model configuration including where to place your grid, the grid size, what resolution data to use to generate terrain elevation, landuse category, and other datasets. At the Terrain-Processing, the WorldWind takes advantages of specifying the input parameters, and verifying the output of terrain processing. Instead of specifying manually the input parameters for model configuration using the keyboard, users can easily select the inputs using the mouse at the globe. Also, regarding the output of terrain data, users can check if it is generated at the right values on the virtual globe. The Pre-Processing is to execute a sequence of programs for preparing analyses to an MM5 model. In order to check if it has valid inputs for the Main-Processing, users can overlay the image on the globe. Finally, the Post-Processing is to visualize the output of the MM5 system programs using graphics tool such as RIP. All outputs related with this model are overlaid on the globe. This allows users to see the trail of typhoon tracks on the WorldWind.

IN41A-1127

Geo-visualization for Geosciences data in World Wind

* Li, J jlih@gmu.edu, Joint Center for Intelligent Spatial Computing, College of Science,George Mason University, 4400 Univ. Dr., Fairfax, VA, 22030-4444, Fairfax, VA 22030, United States
Li, Z zli1@gmu.edu, Joint Center for Intelligent Spatial Computing, College of Science,George Mason University, 4400 Univ. Dr., Fairfax, VA, 22030-4444, Fairfax, VA 22030, United States
Xie, J jxie2@gmu.edu, Joint Center for Intelligent Spatial Computing, College of Science,George Mason University, 4400 Univ. Dr., Fairfax, VA, 22030-4444, Fairfax, VA 22030, United States
Huang, Q qhuang1@gmu.edu, Joint Center for Intelligent Spatial Computing, College of Science,George Mason University, 4400 Univ. Dr., Fairfax, VA, 22030-4444, Fairfax, VA 22030, United States
Li, W wli6@gmu.edu, Joint Center for Intelligent Spatial Computing, College of Science,George Mason University, 4400 Univ. Dr., Fairfax, VA, 22030-4444, Fairfax, VA 22030, United States
Yang, C cyang3@gmu.edu, Joint Center for Intelligent Spatial Computing, College of Science,George Mason University, 4400 Univ. Dr., Fairfax, VA, 22030-4444, Fairfax, VA 22030, United States

The visualization of multiple dimensional data is an important and challenge task for us to understand the geoscience principles and research results. This paper introduces our research on utilizing NASA World Wind to visualize four dimensional Geoscience data output by WRF model. NASA World Wind is an open source visualization platform that provides a considerable number of options for exploring domain applications requiring profound scientific visualization from various data sources, such as WMS and GPX. We utilize the World Wind and expand its functions to handle time dimension and vertical data visualization by focusing on how to visualize atmospheric data produced by WRF (Weather Research and Forecasting) -NMM (Nonhydrostatic Mesoscale Model) model. The data could include temperature, air quality, rainfall and others. Visualization examples are conducted with a coverage of (41.48, -96.51; 25.56, -123.0) by latitude and longitude. Temporal resolution is set as hourly. The visualization toll based on World Wind can 1) display the transformation of climatic parameters, such as temperature, from the ground surface to a certain height continuously, 2) show climate evolution clearly with time lapses, and 3) simulate comprehensible climate change process with animations generated simultaneously by selecting interested routes. The development has been integrated into spatial web portals to support access by the general public.

IN41A-1128

Historical Weather and Climate KML datasets at NOAA's National Climatic Data Center

Baldwin, R Rich.Baldwin@noaa.gov, NOAA's National Climatic Data Center, 151 Patton Ave, Asheville, NC 28801, United States
* Ansari, S Steve.Ansari@noaa.gov, NOAA's National Climatic Data Center, 151 Patton Ave, Asheville, NC 28801, United States
Reid, G Glen.Reid@noaa.gov, I.M. Systems Group, 151 Patton Ave, Asheville, NC 28801, United States
Del Greco, S Stephen.A.Delgreco@noaa.gov, NOAA's National Climatic Data Center, 151 Patton Ave, Asheville, NC 28801, United States
Lott, N Neal.Lott@noaa.gov, NOAA's National Climatic Data Center, 151 Patton Ave, Asheville, NC 28801, United States

NOAA's National Climatic Data Center is using KML to share historical weather and climate data with the Virtual Globe community. Many diverse datasets are available as dynamic, static or custom manually created KML. The following dynamic datasets include archives delivered as REST-based KML web services: - NEXRAD Level-III point features describing general storm structure, hail, mesocyclone and tornado signatures - NOAA's National Weather Service Storm Events Database - NOAA's National Weather Service Local Storm Reports collected from storm spotters - NOAA's National Weather Service Warnings Static datasets include: - Integrated Surface Data (ISD), worldwide surface weather observations - Global Climate Observing System, a comprehensive system focused on the requirements for climate issues - Monthly Climatic Data for the World, approximately 1200 surface and 500 upper air worldwide stations In addition, the NOAA Weather and Climate Toolkit provides custom KML output for NEXRAD Radar and GOES Satellite Imagery. These various access methods provide KML capability to a wide variety of historical data and enhance the interoperability, integration and usability of NCDC data.

http://gis.ncdc.noaa.gov

IN41A-1129

In-flight Visualization of Airborne Doppler Wind Lidar Data Using KML and Google Earth

* Shipley, S T sshipley@wxanalyst.com, WxAnalyst, LTD, 5800 Chase Commons Ct #408, Burke, VA 22015-4636, United States
Greco, S sxg@swa.com, Simpson Weather Associates, 309 E. Jefferson St, Charlottesville, VA 22902, United States
Emmitt, D gde@swa.com, Simpson Weather Associates, 309 E. Jefferson St, Charlottesville, VA 22902, United States
Wood, S A saw@swa.com, Simpson Weather Associates, 309 E. Jefferson St, Charlottesville, VA 22902, United States

The Keyhole Markup Language (KML) and Google Earth (GE) are utilized together for real-time in-flight visualization of wind, aerosol and turbulence data taken by an airborne Doppler Wind Lidar (DWL). Among the DWL products that can be displayed within GE are vertical profiles, cross-sections, and raster volume scans. External user interfaces are added to enhance GE capabilities and usability in the airborne environment. Additional information including gridded mesoscale model output is also superimposed in GE for comparison studies and flight experiment planning. The system architecture supports real-time feedback for in-flight experiment plan modification, and reprogramming of the scanning DWL sampling pattern. Various DWL products are demonstrated in post-flight analysis mode using GE for several topographic environments.

http://wxanalyst.com/WxAzygy

IN41A-1130

Profiler: An Interactive Tool for the Visualization of Data Products From Atmospheric Sounding Instruments

Realmuto, V J vincent.j.realmuto@jpl.nasa.gov, Jet Propulsion Laboratory, California Institute of Technology, M/S 183-501 4800 Oak Grove Drive, Pasadena, CA 91109, United States
* Li, P P p.p.li@jpl.nasa.gov, Jet Propulsion Laboratory, California Institute of Technology, M/S 306-463 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Stough, T M timothy.m.stough@jpl.nasa.gov, Jet Propulsion Laboratory, California Institute of Technology, M/S 306-463 4800 Oak Grove Drive, Pasadena, CA 91109, United States

The Tropospheric Emission Spectrometer (TES), Atmospheric Infrared Sounder (AIRS), and Microwave Limb Sounder (MLS) are examples of sounding instruments that measure variations in atmospheric temperature and composition with altitude. The data products derived from these measurements represent vertical profiles of the atmosphere, and sequences of such profiles represent cross-sections of the atmosphere. Atmospheric cross-sections are inherently three dimensional and the collection of time-series data adds a fourth dimension to this data space. Conventional image-based analysis tools do not provide access to all four dimensions of this space. We have developed Profiler, a data visualization system that allows users to render cross-sections of temperature or composition interactively, visualize changes in atmospheric properties over time and space, and define regions of interest to request data subsets from an archive. We have used TES data to design and test the system. Profiler consists of a 3D visualization client, based on Google Earth (GE), and a server that provides access to the TES data archive, generates cross sections, together with the KML and Collada code necessary to display the cross sections in GE. The generation of cross sections is dynamic and interactive, as users can specify the altitude, species concentration, or temperature ranges portrayed by the cross-sections and request that new cross-sections be generated and displayed. Communication between the users, GE client, and Profiler Server is enabled through network links, unique to each user session. To begin a Profiler session users select sequences of TES profiles, know as Runs, from the data archive. Profiler extracts the complete set of temperature and composition data from these Runs, interpolates the data on equally-spaced pressure levels, and saves the interpolated data arrays to NetCDF files. Given the altitude, temperature, or composition ranges specified by the user, Profiler subsets the corresponding data arrays and generates color-contoured maps representing the variations in atmospheric temperature or composition sampled by the Run. The maps and attendant KML/Collada code are served to GE for the generation of 3-D cross sections. Users may also save the data subsets to NetCDF files for further analysis outside of the Profiler environment. Profiler will have an immediate impact on the display and analysis of TES data and, with some modification of our data access and pre-processing procedures, our technology could be extended to MLS or AIRS data products. Given appropriate tools to parse and translate KML, Profiler could incorporate other geo- browsers, such as Virtual Earth, World Winds, or ArcGIS Explorer. In principal, the concept of interactive cross-sections can be used to visualize any volumetric data set describing the properties of an atmosphere or ocean.

IN41A-1131

Integration of Multiple OGC Standards for Delivery of Earth Science Information - Presentation of Time-Enabled WMS Through KML as Implemented by the PHAiRS Project

Hudspeth, W B bhudspeth@edac.unm.edu, Earth Data Analysis Center, University of New Mexico, MSC01 1110, 1 University of New Mexico, Albuquerque, NM 87131-0001, United States
* Benedict, K K kbene@edac.unm.edu, Earth Data Analysis Center, University of New Mexico, MSC01 1110, 1 University of New Mexico, Albuquerque, NM 87131-0001, United States

Since 2004 the Earth Data Analysis Center has, in collaboration with researchers from the University of Arizona and George Mason University, with funding from NASA, developed a services oriented architecture (SOA) designed for the delivery of historic and current dust forecast data products to the public health user community. This system has generated nearly three years of daily 48-hour dust forecasts, ultimately representing over 289,000 individual hourly forecast rasters for ground surface dust concentrations in four model particle size classes and PM 2.5 and PM 10 size classes. This large collection of model outputs is published as a time-enabled Open Geospatial Consortium (OGC) Web Map Service (WMS) that allows for the efficient retrieval of a single hourly forecast map image for each of these particle size classes, for the entire collection of model outputs. While this WMS service has proven effective in meeting the specific project goals of providing services that support the integration of project products into existing public health decision support systems, the development of an alternative visualization capability that takes advantage of virtual globe technologies was also seen as a valuable complementary capability for making these model outputs accessible to a greater audience of environmental public health users. This paper presents the results of a development effort that produced a system that automatically generates time-enabled KML that enables sequential acquisition of hourly model outputs (via time-enabled WMS) in time-enabled virtual globe applications (e.g. Google Earth). While this effort has proven very successful, it has also highlighted areas where support for time-enabled WMS could be improved, both within the KML standard, and within clients that implement time-enabled viewers.

IN41A-1132

NASA Airborne-simulated Vertical Data in Google Earth

* Chen, A aijunchen@gmail.com, George Mason University, 6301 Ivy Lane, Suite 620, Greenbelt, MD 20770,
* Chen, A aijunchen@gmail.com, NASA Goddard Earth Sciences Data and Information Services Center, Code 610.2, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, United States
Leptoukh, G Gregory.Leptoukh@nasa.gov, NASA Goddard Earth Sciences Data and Information Services Center, Code 610.2, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, United States
Kempler, S steven.j.kempler@nasa.gov, NASA Goddard Earth Sciences Data and Information Services Center, Code 610.2, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, United States
Liu, Z Zhong.Liu@nasa.gov, George Mason University, 6301 Ivy Lane, Suite 620, Greenbelt, MD 20770,
Liu, Z Zhong.Liu@nasa.gov, NASA Goddard Earth Sciences Data and Information Services Center, Code 610.2, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, United States

Google Earth has been widely used as a tool to visualize scientific data that have geospatial elements. The data can be two dimensional and three dimensional, or even four-dimensional. NASA A-Train constellation satellites such as CloudSat, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation), and Aqua have been producing lots of vertical data about the atmosphere. Those data are being used for such scientific research as global climate change, weather forecast, etc. NASA also uses airplanes to load some instruments to simulate satellite flying for establishing the sensitivity, calibration, and initial validation of the instruments that will be loaded at satellites. The airborne simulated flying produces simulated vertical data of the atmosphere. Visualization of these kinds of vertical data in Google Earth is helpful for scientific research. Here, a new method is proposed to visualize the simulated vertical data in Google Earth to expose cloud, aerosol, and other atmospheric profiles in the form of curtain along the flying track of the airplane. An interface description language-based render is designed and implemented to process and display the simulated vertical data in the format of image. The image is further processed and cut into transparent small image slices according to the track of the airplane. A COLLADA (COLLAborative Design Activity) 3D model, which is supported by Google Earth, is devised to make the image slices vertically displayed in Google Earth. Using the COLLADA models and airplane flying track coordinates, an airplane track model is implemented in the format of KML (Keyhole Markup Language). The track curtain makes simulated vertical data viewable, transparently or opaquely, in Google Earth. Thus, airborne simulated vertical geospatial data are available to scientists and the general public in a popular venue.

http://disc.gsfc.nasa.gov/googleearth

IN41A-1133

Virtual Globes and KML – The Tools of Neogeography

* Sfraga, M mike.sfraga@alaska.edu, UA Geography Department, 909 Koyukuk Drive, Fairbanks, AK 99775, United States
Bailey, J E jbailey@gi.alaska.edu, Alaska Volcano Observatory, 909 Koyukuk Drive, Fairbanks, AK 99775, United States
Bailey, J E jbailey@gi.alaska.edu, Arctic Region Supercomputing Center, 909 Koyukuk Drive, Fairbanks, AK 99775, United States
Bailey, J E jbailey@gi.alaska.edu, UA Geography Department, 909 Koyukuk Drive, Fairbanks, AK 99775, United States
Dehn, J jdehn@gi.alaska.edu, Alaska Volcano Observatory, 909 Koyukuk Drive, Fairbanks, AK 99775, United States

Since Virtual Globes have entered the public's consciousness, our concept of how we view the geography of the planet we live on has permanently changed. Similar to the way the internet changed the way we store, search and view information, Virtual Globes represents an evolutionary change in how we visualize geospatial data. One the key components of this new or neogeography has been emergence of Keyhole Markup Language (KML). Now an Open Geospatial Consortium (OGC) international standard, KML is supported by not just Google Earth, but a whole range of virtual globes and other geospatial applications. We believe that these tools have a lot to offer the geoscience community and look to promote their use through special session such as Virtual Globes at AGU.

http://earth.images.alaska.edu

IN41A-1134

Beyond Earth: Using Google Earth to Visualize Other Planetary Bodies

* Hancher, M Matthew.D.Hancher@nasa.gov, NASA Ames Research Center, Mail Stop 269-3, Moffett Field, CA 94035, United States
Beyer, R Ross.A.Beyer@nasa.gov, SETI/NASA, Mail Stop 245-3, Moffett Field, CA 94035, United States
Broxton, M michael.broxton@nasa.gov, NASA Ames Research Center, Mail Stop 269-3, Moffett Field, CA 94035, United States
Gorelick, N gorelick@google.com, Google, Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, United States
Kolb, E ekolb@google.com, Google, Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, United States
Weiss-Malik, M michaelwm@google.com, Google, Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, United States

Virtual globes have revolutionized the way we visualize and understand the Earth, but there are other planetary bodies that can be visualized as well. We will demonstrate the use of Google Earth, KML, and other modern mapping tools for visualizing data that's literally out of this world. Extra-terrestrial virtual globes are poised to revolutionize planetary science, bring an exciting new dimension to science education, and allow users to explore the increasingly breathtaking imagery being sent back to Earth by modern planetary science satellites. We will demonstrate several uses of the latest Google Earth and KML features to visualize planetary data. Global maps of planetary bodies---not just visible imagery maps, but also terrain maps, infra-red maps, minerological maps, and more---can be overlaid on the Google Earth globe using KML, and a number of sources are already making many such maps available. Coverage maps show the polygons that have been imaged by various satellite sensors, with links to the imagery and science data. High-resolution regionated ground overlays allow you to explore the most breathtaking imagery at full resolution, in its geological context, just as we have become accustomed to doing with Earth imagery. Panoramas from landed missions to the Moon and Mars can even be embedded, giving users a first-hand experience of other worlds. We will take you on a guided tour of how these features can best be used to visualize places other than the Earth, and provide pointers to KML from many sources---ourselves and others---that users can build on in constructing their own KML content of other planetary bodies. Using this paradigm for sharing geospatial data will not only enable planetary scientists to more easily build and share data within the scientific community, but will also provide an easy platform for public outreach and education efforts, and will easily allow anyone to layer geospatial information on top of planetary data.

IN41A-1135

Data Visualization of Lunar Orbiter KAGUYA (SELENE) using web-based GIS

* Okumura, H okumura.hayato@jaxa.jp, Japan Aerospace Exploration Agency, 2-1-1,Sengen,Tsukuba, Ibaraki, 305-8505, Japan
Sobue, S sobue.shinichi@jaxa.jp, Japan Aerospace Exploration Agency, 2-1-1,Sengen,Tsukuba, Ibaraki, 305-8505, Japan
Yamamoto, A aya@restec.or.jp, Remote Sensing Technology Center of Japan, 1-9-9, Roppongi, Minato-ku,, Tokyo, 106-0032, Japan
Fujita, T tfujita@restec.or.jp, Remote Sensing Technology Center of Japan, 1-9-9, Roppongi, Minato-ku,, Tokyo, 106-0032, Japan

The Japanese Lunar Orbiter KAGUYA (SELENE) was launched on Sep.14 2007, and started nominal observation from Dec. 21 2007. KAGUYA has 15 ongoing observation missions and is obtaining various physical quantity data of the moon such as elemental abundance, mineralogical composition, geological feature, magnetic field and gravity field. We are working on the visualization of these data and the application of them to web-based GIS. Our purpose of data visualization is the promotion of science and education and public outreach (EPO). As for scientific usage and public outreach, we already constructed KAGUYA Web Map Server (WMS) at JAXA Sagamihara Campus and began to test it among internal KAGUYA project. KAGUYA science team plans the integrated science using the data of multiple instruments with the aim of obtaining the new findings of the origin and the evolution of the moon. In the study of the integrated science, scientists have to access, compare and analyze various types of data with different resolution. Web-based GIS will allow users to map, overlay and share the data and information easily. So it will be the best way to progress such a study and we are developing the KAGUYA WMS as a platform of the KAGUYA integrated science. For the purpose of EPO, we are customizing NASA World Wind (NWW) JAVA for KAGUYA supported by NWW project. Users will be able to search and view many images and movies of KAGUYA on NWW JAVA in the easy and attractive way. In addition, we are considering applying KAGUYA images to Google Moon with KML format and adding KAGUYA movies to Google/YouTube.

IN41A-1136

Adaptation of NASA World Wind for Helio-informatics

* Kobashi, A aki@lmsal.com, Lockheed Martin Solar and Astrophysics Laboratory, Bldg. 252, Org. ADBS 3251 Hanover Street, Palo Alto, CA 94304, United States
Jaffey, A ajaffey@lmsal.com, Lockheed Martin Solar and Astrophysics Laboratory, Bldg. 252, Org. ADBS 3251 Hanover Street, Palo Alto, CA 94304, United States
Cheung, M cheung@lmsal.com, Lockheed Martin Solar and Astrophysics Laboratory, Bldg. 252, Org. ADBS 3251 Hanover Street, Palo Alto, CA 94304, United States
Hurlburt, N hurlburt@lmsal.com, Lockheed Martin Solar and Astrophysics Laboratory, Bldg. 252, Org. ADBS 3251 Hanover Street, Palo Alto, CA 94304, United States
DeRosa, M derosa@lmsal.com, Lockheed Martin Solar and Astrophysics Laboratory, Bldg. 252, Org. ADBS 3251 Hanover Street, Palo Alto, CA 94304, United States
Schrijver, C schryver@lmsal.com, Lockheed Martin Solar and Astrophysics Laboratory, Bldg. 252, Org. ADBS 3251 Hanover Street, Palo Alto, CA 94304, United States

The upcoming Solar Dynamics Observatory, along with other space-borne and ground-based observatories, will soon inundate the heliophysics community with data. Novel and efficient approaches to the analysis and transport of these data and metadata are being developed order to maximize its usage. As a front-end to the Heliophysics Events Knowledgebase, we are adapting the recent Java release of NASA World Wind (see link below) for applications in Helio-informatics. Key features of this interactive tool include, but are not limited to: (1) Display of imagery at progressive resolutions (e.g. magnetograms from various instruments), (2) Direct queries to the Heliophysics Events Knowledgebase (e.g. find active regions within coronal holes), (3) Links to original data used for identification of solar and heliospheric features, and (4) 3D visualization of magnetic field structures (e.g. from magnetic field extrapolations).

IN41A-1137

Visualizing the Operations of the Phoenix Mars Lander

* Schwehr, K kurt@ccom.unh.edu, Center for Coastal and Ocean Mapping, University of New Hampshire Chase Ocean Engineering 24 Colovos Rd, Durham, NH 03824, United States
* Schwehr, K kurt@ccom.unh.edu, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Andres, P pma@jpl.nasa.gov, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Craig, J jason@ssvmail.jpl.nasa.gov, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Deen, R Robert.G.Deen@jpl.nasa.gov, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
De Jong, E eric.m.dejong@jpl.nasa.gov, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Fortino, N nfortino@caltech.edu, California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125, United States
Gorgian, Z zareh.gorjian@jpl.nasa.gov, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Kuramura, K koji@seismiceffects.com, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Lemmon, M lemmon@tamu.edu, Texas A&M University, Dept. of Atmospheric Sciences, College Station, TX 77843, United States
Levoe, S steve.levoe@jpl.nasa.gov, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Leung, C christopher.leung@gmail.com, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Lutz, N njlutz@uchicago.edu, University of Chicago, 5801 South Ellis Avenue, Chicago, IL 60637, United States
Ollerenshaw, R ryan.ollerenshaw@jpl.nasa.gov, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Smith, P psmith@lpl.arizona.edu, Lunar and Planetary Laboratory, University of Arizona 1629 E. University Blvd, Tucson, AZ 85721, United States
Stetson, M mike-jpl@keystoneanimation.com, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Suzuki, S shigeru@jpl.nasa.gov, NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
Phoenix Science Team, T schwehr@ccom.unh.edu

With the successful landing of the Phoenix Mars Lander comes the task of visualizing the spacecraft, its operations and surrounding environment. The JPL Solar System Visualization team has brought together a wide range of talents and software to provide a suit of visualizations that shed light on the operations of this visitor to another world. The core set of tools range from web-based production tracking (Image Products Release Website), to custom 3D transformation software, through to studio quality 2D and 3D video production. We will demonstrate several of the key technologies that bring together these visualizations. Putting the scientific results of Phoenix in context requires managing the classic powers-of-10 problem. Everything from the location of polar dust storms down to the Atomic Force Microscope must be brought together in a context that communicates to both the scientific and public audiences. We used Lightwave to blend 2D and 3D visualizations into a continuous series of zooms using both simulations and actual data. Beyond the high-powered industrial strength solutions, we have strived to bring as much power down to the average computer user's standard view of the computer: the web browser. Zooming and Interactive Mosaics (ZIM) tool is a JavaScript web tool for displaying high-resolution panoramas in a spacecraft-centric view. This tool allows the user to pan and zoom through the mosaic, indentifying feature and target names, all the while maintaining a contextual frame-of-reference. Google Earth presents the possibility of taking hyperlinked web browser interaction into the 3D geo-browser modality. Until Google releases a Mars mode to Google Earth, we are forced to wrap the Earth in a Mars texture. However, this can still provide a suitable background for exploring interactive visualizations. These models range over both regional and local scales, with the lander positioned on Mars and the local environment mapped into pseudo-"Street View" modes. Many visualizations succeed by altering the interaction metaphor. Therefore, we have attempted to completely overload the Google Earth interface from a traditional planetary globe into a mosaic viewer by mapping the Phoenix Mosaics onto the sphere and using geographic latitude and longitude coordinates as the camera pointing coordinates of a Phoenix mosaic. This presentation focuses on the data management and visualization aspects of the mission. For scientific results, please see the special section "U13 The Phoenix Mission."

http://phoenix.lpl.arizona.edu/

IN41A-1138

Google Integration at the Alaska Satellite Facility (ASF)

* Haney, J jhaney@asf.alaska.edu, Alaska Satellite Facility, Geophysical Institute University of Alaska Fairbanks PO Box 75320, Fairbanks, AK 99775, United States

With the increased popularity of Google products within ASF¹s data user community, ASF has begun developing layers in Keyhole Markup Language (KML) for displaying remote-sensing data within Google Earth. In the user-friendly, virtual-earth platform of Google Earth, ASF¹s KML layers will be showcased. Demonstrations include the display of archived data, tracking near real-time (NRT) data, and the projection of sample data sets. ASF¹s utilization of Google products for defining areas of interest and for displaying search results within the User Remote Sensing Access (URSA) software will also be highlighted. By using KMLs, ASF data can be displayed in ways that are understandable for the novice data user, exciting for outreach purposes, and supportive of a more visual data-selection process.

http://www.asf.alaska.edu