]> 2009-07-03T22:25:55-04:00 DataSystem Template:DataSystemProfile Form:DataSystemProfile EPA AQS Jake Summers summer.jake@epa.gov State, local, tribal, and other federal agencies submit and own the data in AQS. Air Quality System (AQS) AQS is the national repository for ambient air quality data. It is intended primarily or State, Local, and Tribal organizations to supply their data to the USEPA. However, the system does allow for the extraction of data through standardized reports and on-line forms to view the data. AQS is the basis for EPA’s characterization of air quality effecting the populations and ecosystems, the efficacy of control strategies, and exposure to toxic pollutants – all top level EPA air quality management goals. To my knowledge, this data is not used in forecasting. Data from AQS is used by other organizations to evaluate the effectiveness of the current air quality standards and to assess where the standards are being violated. It is also a major source of input to epidemiological and other health studies. AQS provides no capabilities to model data or to evaluate emissions. However the ground observation data in AQS is used validate model projections of air quality. Not Given The AQS database can be accessed by end users through the AQS application or Oracle Discoverer. The AQS Data Mart can be accessed via SOAP web services, a query interface on the EPA TTN, or via an SQL connection for EPA users and applications. Not Given EPA architecture Exchange Network web services List server, emails. AQS contains data from all 50 states, the Virgin Islands, Guam, Puerto Rico, and data from border sites in Mexico and Canada. AQS has data available from 1957 to the present day consisting primarily of hourly and 24-hour samples. AQS contains ambient air quality measurements (mostly at the hourly or daily sample length), summaries of these measurements (at a daily, quarterly, and annual basis), special calculations required by the regulations (like 8-hour running averages for ozone), site and monitor metadata, and quality assurance information about the data. AQS is the EPA’s repository for air quality measurements from ground based stations. The stations are operated by state, local, tribal, and other federal agencies. Some of the data is required by regulation, other data is reported voluntarily. The agency that collects, analyzes, and submits the sample data to AQS maintains ownership of the data. Over 1,000 parameters are collected and maintained by AQS including criteria pollutants, hazardous air pollutants, speciation data, and air toxics. AQS provides standard and ad hoc report access to users. By policy, only state, local, tribal and federal agency employees and contractors are allowed an AQS account. External access is handled by the AQS Data Mart. The AQS Data Mart is the dissemination piece of AQS. It contains all of the data reported to AQS from 1980 to the present (even AQS does not keep that much on-line). It is updated nightly with the data added to AQS during the business day. Access requires registration but is open to anyone and there is a guest account. Access is via 5 SOAP web services or a “Direct Interface” query builder (which is just a GUI on top of the SOAP web services). Data from multiple networks is included (criteria, toxics, visibility, etc.) Not Given Not Given Quality assurance and temporal aggregation. Not Given Not Given Station based air quality measurements from the national ambient air quality monitoring network(s). GIOVANNI Gregory Leptoukh Gregory.Leptoukh@nasa.gov NASA GIOVANNI The Goddard Online Interactive Visualization ANd aNalysis Infrastructure or "Giovanni" is an online tool allowing researchers, students, and application users to easily explore, inter-compare, and analyze remote sensing data using only a Web browser. Not Given MERRA: MODERN ERA RETROSPECTIVE-ANALYSIS FOR RESEARCH AND APPLICATIONS (1979 – 2008) NASA reanalysis of atmospheric observations from using GEOS-5, focusing on hydrological cycle, resolution 1/2x2/3x72 Indirect - air pollution, humidity, temperature, etc. GOCART, GEOS-5, LDAS Not Given SOA, WMS, WCS, OPeNDAP, custom arrangements Not Given Not Given GrADS, IDL, Python, Perl Aggregation, statistics Msgs to giovanni listserv Global OMI-Aura, MLS-Aura, HIRDLS-Aura, AIRS-Aqua, MODIS-Terra, MODIS-Aqua, TRMM, MISR, SeaWiFS, TOMS, CALIPSO, CloudSat, Polder, MERIS The Goddard Earth Science Data and Information Services Center (GES DISC) developed Giovanni to easily explore, inter-compare, and analyze data using only a Web browser. Giovanni employs scientifically valid algorithms and a rich schema for describing data parameters, a combination that provides users with scientifically meaningful results. This, sometimes subtle, distinction is often overlooked in other online data visualization and analysis tools. The current version of Giovanni, version 3, evolved from the successes of previous versions that emphasized a simple interface, rapid image rendering, and scientifically valid processing that correctly accounted for the algorithms employed (including underlying assumptions). Based on Common Gateway Interface (CGI) Perl scripts, HyperText Markup Language (HTML) templates, and GrADS (Grid Analysis and Display System) scripts, the early versions of Giovanni were enormously successful and deployments at the GES DISC quickly proliferated. Although successful, the architecture was based on old technology. The costs to maintain and extend Giovanni for new science driven requirements and for supporting standard interfaces and protocols became too great. As a result, a radical departure for a new Giovanni architecture was undertaken. The result was Giovanni, version 3. Giovanni is based on a services oriented architecture (SOA) supported by a workflow management system. It is designed to be extensible with respect to new science requirements and interoperable with standard protocols and data formats. More 1200 geophysical parameters measured by sensors onboard satellites and from models. Berrick, S., Leptoukh, G., Farley, J., & Rui, H. (2009). Giovanni: A Web services workflow-based data visualization and analysis system. IEEE Trans. Geosci. Remote Sens., 47(1), 106-113 Acker, J. and G. Leptoukh, 2007. Online Analysis Enhances Use of NASA Earth Science Data, EOS, Transactions of American Geophysical Union, 88, 14 Chen, A., Leptoukh, G., Kempler, S., Nadeau, D. and Zhang, X. 2008. Augmenting the Research Value of Geospatial Data using Google Earth. Journal of the Virtual Explorer, Electronic Edition, 29, paper 100. Savtchenko, A., Kummerer, R., Smith, P., Gopalan, A., Kempler, S., and Leptoukh, G., 2008. A-Train Data Depot - Bringing Atmospheric Measurements Together, IEEE Trans. on Geoscience and Remote Sensing,46, 2788-2795 Ahmad, S., G. Leptoukh, J. Johnson, J. Farley, S. Kempler, Global Monitoring of Atmospheric Pollutants from the Aura Satellite, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA. Berrick, S., G. Leptoukh, C. Lynnes, GES DISC Interoperable Services for Atmospheric Data The 2007 Summer Federation of Earth Science Information Partners (ESIP) Conference, Madison, Wisconsin, July 17-20, 2007. Aijun Chen, Gregory Leptoukh, Liping Di, Christopher Lynnes, Steven Kempler and Denis Nadeau, NASA A-Train Vertical Data (.Curtains.) in Google Earth, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA. Johnson, J., Ahmad, S., Leptoukh, G., Gerasimov, I., Farley, J., Zhu, T., Enhanced Capabilities for Aura MLS, OMI and HIRDLS Data Within Giovanni, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA. View Poster Steve Kempler, Cloudsat, CALIOP, MODIS, AIRS, OMI, and POLDER Data Search and Visualization Available to Facilitate Multi Instrument Cloud Studies Along the A-Train Path, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA. Leptoukh, G., Zubko, V., Gopalan, A., Remer, L., Intercomparison of Atmospheric Aerosol Climatologies Obtained From Passive Satellite Instruments, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA Ana Prados, Gregory Leptoukh, Erica J. Alston, and Irina N. Sokolik, Assessing U.S Air Quality Using CALIPSO and MODIS Data via Giovanni, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA. Hualan Rui, John Farley, Gregory Leptoukh, Stephen Berrick, New Data and Capabilities in the NASA Goddard Giovanni System, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA. Savtchenko, A. R. Kummerer, P. Smith, S. Kempler, G. Leptoukh, A-Train Data Depot Bringing Atmospheric Measurements Together International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, July 23-27, 2007. Peter Smith, Steve Kempler, Greg Leptoukh, Andrey Savtchenko, Graeme Stephens, Dave Winker, Multi-Sensor Data From A-Train Instruments Brought Together for Atmospheric Research, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA Viktor Zubko, Gregory Leptoukh, Arun Gopalan, Merging MODIS Terra and Aqua Level 3 Aerosol Optical Thickness for Giovanni Online Data Analysis and Visualization, 2007 AGU Fall Meeting, December 10-14, San Francisco, CA. Giovanni provides access to the original and their subsets in various formats: HDF, netCDF, ASCII, kml/kmz (for Google Earth) CALIPSO, CloudSat, MODIS Not Given Not Given S4PM, Giovanni workflow Plot Types for Single Parameters: * Area plots of time-averaged parameters * Time series plots of area-averaged parameters * Meridional averages * Zonal averages * Vertical profiles * Longitude-latitude-pressure-time cross sections Plot Types for Multi-Parameter Intercomparisons: * Area plots of overlain time-averaged parameters * Time series plots of area-averaged parameters * Difference plots * Scatter plots with regression * Temporal correlation maps Animations Climatology and anomalies ASCII output for maps and plots Web services for downloading subsetted data Complete end-to-end chains starting from the original data source via various protocols are integrated together into Giovanni instances OMI, MLS, HIRDLS, AIRS, TRMM, TOMS VIEWS Shawn McClure [mailto:mcclure@cira.colostate.edu mcclure@cira.colostate.edu] RPOs, NPS, WRAP, CIRA VIEWS The Visibility Information Exchange Web System (VIEWS) is an online system of data and tools designed to support the EPA's Regional Haze Rule by providing states, tribes, scientists, researchers, planners, students, and others with easy online access to a wide variety of air quality datasets. The VIEWS team maintains a comprehensive database of air quality data from over two dozen monitoring networks and is constantly acquiring new data sets and adding new networks. The VIEWS website offers many tools and resources, including the Database Query Wizard, an air quality trends tool, an aerosol species composition tool, a monitoring site metadata browser, a dynamic contour-mapping tool, general-purpose charting and graphing tools, visibility photographs, Class I Area webcams, and more. The VIEWS website currently has over 1100 registered users from over 100 different countries and hosts thousands of visitors each month. The VIEWS team also develops and maintains the IMPROVE website, the WRAP Technical Support System (TSS), and the Air Toxics Data Archive (ATDA), all of which utilize the foundational database and software architecture developed for VIEWS. Ongoing development and maintenance of VIEWS is conducted by Colorado State University's Cooperative Institute for Research in the Atmosphere (CIRA) in Fort Collins, Colorado. Visitors to VIEWS are encouraged to register and provide comments, questions, and feedback to the VIEWS team regarding all aspects of the website and database. Not Given The Regional Haze Rule (RHR) goals include achieving natural visibility conditions at 156 Federally mandated Class I areas by 2064. In more specific terms, that RHR goal is defined as (1) visibility improvement toward natural conditions for the 20% of days that have the worst visibility and (2) no worsening in visibility for the 20% of days that have the best visibility. One component of the states’ demonstration to EPA that they are making reasonable progress toward this 2064 goal is the comparison of modeled visibility projections for the first milestone year of 2018 with what is termed a uniform rate of progress (URP) goal. The 2018 URP goal is obtained by constructing a “linear glide path” (in deciviews) that has at one end the observed visibility conditions during the mandated five-year (2000-2004) baseline period and at the other end natural visibility conditions in 2064; the visibility value that occurs on the glide path at year 2018 is the URP goal. The VIEWS infrastructure has been used to develop two online tools for the WRAP's Technical Support System (TSS) that are designed to help states and tribes forecast future visibility conditions and analyze their progress toward achieving natural visibility conditions by 2064. The [http://vista.cira.colostate.edu/TSS/Results/HazePlanning.aspx Monitoring Glide Slope Tool] allows users to view the glide slope information, and the [http://vista.cira.colostate.edu/TSS/Results/HazePlanning.aspx Visibility Projection Tool] displays RHR glide slope information in addition to model-based visibility projections for the 2018 reasonable progress milestone. Additional information on how the data in the VIEWS integrated database is used by the TSS tools to project future visibility conditions and assess reasonable progress towards future visibility goals can be found in the [http://vista.cira.colostate.edu/docs/wrap/Modeling/AirQualityModeling.doc TSS Air Quality Modeling document]. Many of the datasets maintained by VIEWS are frequently used for health research and epidemiological studies. In particular, university researchers and state departments of health frequently visit VIEWS to download and/or analyze the various PM2.5 and Hazardous Air Pollutant (HAP) datasets maintained in the integrated database. A recent addition to VIEWS is the dataset from the NADP Mercury Deposition Network (MDN), and data from the NPS Ozone network is currently being added as well. The VIEWS team also developed and currently maintains the [http://vista.cira.colostate.edu/atda Air Toxics Data Archive (ATDA)] for the EPA, which contains HAP, criteria pollutant, and other air toxics data. The [http://vista.cira.colostate.edu/TSS/Results/HazePlanning.aspx TSS Model Performance Tool] can be used to evaluate model performance on a site-by-site basis by comparing the 2002 modeling results to the IMPROVE monitoring data collected in 2002. A detailed description of how the TSS data and tools can be used for model evaluation can be found in the [http://vista.cira.colostate.edu/docs/wrap/Modeling/AirQualityModeling.doc TSS Air Quality Modeling document]. <div style="margin:0px 20px 10px 0px;text-align:justify;">To further leverage the capabilities of VIEWS/TSS, the VIEWS team collaborated in mid-2007 with the Institute for the Environment at the University of North Carolina, Chapel Hill to submit a NASA ROSES proposal to incorporate NASA satellite data into the VIEWS/TSS integrated database in order to enhance the decision support capabilities of these combined systems. The proposal was awarded in early 2008 and over the next few months work will begin to further expand VIEWS/TSS with relevant satellite data and the appropriate tools to access and analyze it.</div> Raw data and metadata can be obtained from VIEWS in a variety of output mediums: Delimited or fixed width ASCII text files, Microsoft Excel workbooks, static HTML tables, and/or interactive HTML "data grids". In addition, the user can select the specific fields that are included in the output results and can specify a variety of output options, such as "wide" or "skinny" format, date format, missing data placholder, quoted text, etc. The VIEWS team is also working to provide the option to output data in XML and as XML SOAP responses. Not Given All data, tools, and other resources on VIEWS are freely accessible. No account is required to use VIEWS, but users are encouraged to register. Direct, read-only access to the integrated VIEWS database is also available via direct MS SQL Server connection, OLEDB, or ODBC. Several VIEWS tools have been developed as web services, and many existing tools are currently being re-factored to function as web services as well. In addition, several VIEWS web pages have been designed to allow developers of other web sites to "embed" and "brand" them on their own web sites with a minimum of effort. For an example of this, please visit the [http://vista.cira.colostate.edu/views2/development/sitebrowserdevelopment.aspx VIEWS Site Browser Development page]. User feedback is facilitated through a variety of methods: the online [http://vista.cira.colostate.edu/views/Web/General/Feedback.aspxVIEWS Feedback form], direct email to the VIEWS team, or via the contact information published on VIEWS. The VIEWS team handles many feedback responses throughout a given week, and are typically able to respond within a day or two of receving a request or suggestion. User feedback is also actively solicited via periodic workshops and training sessions, or via conference calls with the steering committee and end users. Currently, the datasets in VIEWS are mostly relevant to the United States and neighboring countries such as Canada and Mexico. For complete information on the geographical coverage of the various datasets in VIEWS, please visit the [http://vista.cira.colostate.edu/views2/NetworkBrowser/NetworkBrowser.aspx VIEWS Network Browser], which is an online tool that displays maps of the monitoring sites for each network. Future plans include the addition of various international datasets and global satellite data. <br /> <table class="table1" width="95%" cellpadding="0" cellspacing="0" border="1" style="margin:10px 0px 10px 20px;border:solid 1px #dedede;border-collapse:collapse;font-size:11px;"> <tr><th align="left">Program</th><th align="left">Freq</th><th align="right">Start</th><th align="right">End</th><th align="right">Records</th><th align="right">Updated</th></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx AQS Fine Mass FRM (D)]</td><td>Daily</td><td align="right">01/01/1999</td><td align="right">12/31/2003</td><td align="right">798317</td><td align="right">04/13/2004</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx AQS Fine Mass FRM (H)]</td><td>Hourly</td><td align="right">01/01/1999</td><td align="right">12/31/2003</td><td align="right">7028023</td><td align="right">04/13/2004</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx AQS Fine Speciation (D)]</td><td>Daily</td><td align="right">02/09/2000</td><td align="right">12/31/2006</td><td align="right">6839576</td><td align="right">05/24/2007</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx AQS PM10 - Daily]</td><td>Daily</td><td align="right">01/01/1994</td><td align="right">12/31/2003</td><td align="right">1121497</td><td align="right">07/30/2004</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx CASTNet Dry Chemistry]</td><td> Weekly</td><td align="right">01/06/1987</td><td align="right">03/23/2005</td><td align="right"> 793290</td><td align="right">11/23/2005</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx CASTNet Visibility Chemistry]</td><td>Daily</td><td align="right">10/25/1993</td><td align="right">12/27/2001</td><td align="right">166950</td><td align="right">03/11/2004</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx GAViM]</td><td>Daily</td><td align="right">05/31/1994</td><td align="right">01/07/2001</td><td align="right">65366</td><td align="right"></td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx IMPROVE Aerosol (Preliminary)]</td><td>Daily</td><td align="right">8/1/2006</td><td align="right">2/26/2007</td><td align="right"> 660492</td><td align="right">08/31/2007</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx IMPROVE Aerosol (Raw)]</td><td>Daily</td><td align="right">03/02/1988</td><td align="right"> 7/31/2006</td><td align="right"> 10804852</td><td align="right">08/31/2007</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx IMPROVE Coarse Mass Speciation Study]</td><td>Daily</td><td align="right">03/01/2003</td><td align="right">07/29/2003</td><td align="right">444</td><td align="right"></td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx IMPROVE Nephelometer]</td><td>Hourly</td><td align="right">01/01/1993</td><td align="right">06/30/2007</td><td align="right">13839744</td><td align="right">10/09/2007</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx IMPROVE Aerosol (RHR1)]</td><td>Daily</td><td align="right">01/01/1988</td><td align="right"> 12/31/2004</td><td align="right"> 7220634</td><td align="right">12/12/2005</td></tr> <tr> <td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx IMPROVE Aerosol (RHR2)] (New Algorithm)</td><td>Daily</td><td align="right">01/01/1988</td><td align="right"> 12/31/2005</td><td align="right"> 4861358</td><td align="right"> 03/09/2006</td> </tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx MOHAVE]</td><td>Daily</td><td align="right">01/10/1992</td><td align="right">09/02/1992</td><td align="right">106484</td><td align="right"></td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx NADP/AIRMoN]</td><td>Weekly</td><td align="right">09/23/1992</td><td align="right">02/15/2004</td><td align="right">281505</td><td align="right">06/04/2004</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx NADP/Mercury Deposition Network (MDN)]</td><td>Weekly</td><td align="right">11/22/1995</td><td align="right">09/28/2004</td><td align="right">109820</td><td align="right">03/30/2005</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx NADP/Nation Trends Network (NTN)]</td><td>Weekly</td><td align="right">07/05/1978</td><td align="right">02/03/2004</td><td align="right">4824603</td><td align="right"></td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx Navaho Generating Station (NGS)]</td><td>Daily</td><td align="right">03/04/1992</td><td align="right">05/30/1992</td><td align="right">701</td><td align="right">04/02/2004</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx NPS SFU Aerosol]</td><td>Daily</td><td align="right">07/27/1979</td><td align="right">11/13/1993</td><td align="right">850126</td><td align="right">03/02/2004</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx PREVENT]</td><td>Daily</td><td align="right">06/21/1990</td><td align="right">09/03/1990</td><td align="right">58547</td><td align="right"></td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx REVEAL]</td><td>Daily</td><td align="right">04/20/1994</td><td align="right">06/17/1995</td><td align="right">9618</td><td align="right">04/02/2004</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx SEARCH All Variables]</td><td>Daily</td><td align="right">05/01/1998</td><td align="right">3/31/2005</td><td align="right">438650</td><td align="right">02/13/2006</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx SEARCH Best Estimate]</td><td>Daily</td><td align="right">05/01/1998</td><td align="right">3/31/2005</td><td align="right">153416</td><td align="right">02/13/2006</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx SEARCH FRM]</td><td>Daily</td><td align="right">05/01/1998</td><td align="right">3/31/2005</td><td align="right">153392</td><td align="right">02/13/2006</td></tr> <tr><td>[http://vista.cira.colostate.edu/views/web/data/datawizard.aspx SEAVS]</td><td>Daily</td><td align="right">07/15/1995</td><td align="right">08/25/1995</td><td align="right">3564</td><td align="right"></td></tr> </table> In 2002, a steering committee composed of representatives from the EPA and the five Regional Planning Organizations (RPOs) – Western Regional Air Partnership (WRAP), Central Regional Air Planning Association (CENRAP), Midwest Regional Planning Organization (MRPO), Mid-Atlantic/Northeast Visibility Union (MANE-VU) and Visibility Improvement State and Tribal Association of the Southeast (VISTA) – decided to extend the contract between CIRA and the Western Governors Association (WGA) in order to develop and expand the existing WRAP online ambient monitoring database into a national system called the Visibility Information Exchange Web System (VIEWS). As a result, the VIEWS development team designed and implemented an improved database management system and a new website to serve as the foundational elements of VIEWS, based upon requirements and feedback from the VIEWS steering committee. Over the next few years the user base for VIEWS grew significantly, as did user requests for additional datasets and new functionality. Based largely upon its popularity and success within the general air quality community, VIEWS was selected by the WRAP in 2005 to serve as the foundational architecture and starting point for the development of the WRAP’s Technical Support System (TSS), a high-level planning and decision and support system intended to aid the development of state and tribal Implementation Plans in the WRAP as required by the Regional Haze Regulations. The TSS was built upon the database and software infrastructure of VIEWS, and the first version was launched in early 2007. The TSS is designed to be a portal to the technical data and analytical results prepared by WRAP Forums and Workgroups in support of the air quality planning needs of western state and tribes. These resources are maintained and updated to support the implementation of regional haze plans and other air quality analysis and management activities. The integrated VIEWS database contains data for over 250 air quality variables and parameters. These include surface observations, modeled parameters, and emissions data. For a complete list of the parameters maintained for each individual dataset, please visit the [http://vista.cira.colostate.edu/views/Web/Data/DataWizard.aspx VIEWS Query Wizard] and note the information on the "Select Parameters" tab as you select various datasets. The VIEWS website provides easy online access to the integrated database through a wide variety of tools and applications. Users are encouraged to register with the website, but no account or login is required to use any of the VIEWS resources. The entire contents of the integrated database are freely and openly available for ad hoc querying, display, and download. In addition, users are encouraged to submit custom requests for data and to provide additional relevant datasets for inclusion in the database. VIEWS employs an advanced data acquisition and import system to integrate data from several air quality data centers into a single, highly-optimized data warehouse. Ground-based measurements from dozens of monitoring networks, air quality modeling results, and detailed emissions inventories are imported and updated on a regular basis using a generalized, uniform data model and carefully standardized metadata. Names, codes, units, and quality flags from the source datasets are carefully mapped to a unified standard, and native formats and organizations are transformed into a common, normalized database schema. This design enables users to explore, merge, and analyze datasets of widely-varying origin in a consistent, unified manner with a common set of tools and web services. This degree of interoperability allows decision-makers to analyze diverse datasets side-by-side and focus on high-level planning strategies without having to contend with the details of data management and manipulation. VIEWS/TSS users are typically asking questions of “What pollutants are impacting a given area?” and “Where are these pollutants coming from?” The answers depend upon accurate assessments of aerosol loading and source attribution, and FLMs and states are occupied on an ongoing basis with these goals. States are further mandated to answer the question of “What can be done to reduce these impacts?”, because the Regional Haze Rule requires states and tribes to develop implementation plans for reducing emissions and demonstrating reasonable progress towards doing so, and these plans must provide for an improvement during the 20% worst visibility days while also ensuring no degradation during the 20% best visibility days. To accomplish this, users must identify the pollutants, quantify their amounts, and determine the sources of anthropogenic emissions that contribute to this pollution on both the “best” and the “worst” visibility days in a given area. They must then determine available control measures for each source and evaluate these measures on the basis of costs, time, energy and environmental impacts, and the remaining life of the source. Planners then employ these analyses to make decisions about what controls to implement, to estimate projected improvements, and to track their progress in reaching these goals. The resulting decisions have obvious ecological impacts, but can also have important political and economic impacts in the sense that deciding which sources to control is a politically-significant issue and the process of controlling emissions and tracking progress costs money and takes time. VIEWS and the TSS have been designed with these decision needs in mind from the outset, and input and feedback from primary decision-makers is solicited on a routine basis through workshops and training sessions. Such input is carefully factored back into the design process to improve existing tools and design new ones in an ongoing process of progressive evolution. <div style="margin:0px 20px 10px 0px;text-align:justify;">In addition to importing and consolidating data from many different monitoring networks, VIEWS and the TSS specifically summarize results and consolidate information about air quality monitoring, meteorological and receptor modeling data analyses, emissions inventories and models, and gridded air quality/visibility regional modeling simulations. These copious and diverse data are integrated for application to air quality planning purposes by prioritizing and refining key information and results into explanatory documents and tools. Finally, a major goal is to make the standard and user-specified maps, charts, tables, and graphs easily available for export, while maintaining the original source data available for verification and subsequent analysis through the online tools.</div> The VIEWS data import system accepts data in a variety of formats from various providers and can also automatically download and extract data from known online sources. The import system extracts the data and metadata from its source, examines it for basic integrity issues like duplicate records and mismatched data types, performs various data conversions, transforms the data into an integrated schema (described above), calculates checksums for numeric fields, and validates the resultant data against its source before transferring it to the production data warehouse. Many other processing operations – such as filtering, aggregation, averaging, formatting, renaming, recoding, and outputting – can be done on-the-fly by using the various data access and analysis tools on the website. A wide variety of general-purpose visualization and analysis tools are available on the VIEWS website for exploring the data and producing graphs, charts, maps, and formatted data files. Specialized tools allow users to browse and analyze time series, visibility trends, spatial patterns, temporal aerosol profiles, chemical species composition, light extinction budgets, back trajectories, estimated natural conditions, visibility projections, glide slope plots, emissions inventories, weighted emissions potentials, and model performance evaluations. All tools have been designed with common user interface components and are being re-factored with a Service Oriented Architecture approach to allow them to function as web services. The acquisition, import, verification, transformation, integration, presentation, analysis, and dissemination of data are all handled by the various subsystems of VIEWS. In addition, the regular solicitation and incorporation of user feedback ensures that this end-to-end integration is further supplemented by the input of the decision makers who comprise the VIEWS user base. VIEWS is the primary online source for data collected by the [http://vista.cira.colostate.edu/improve IMPROVE (Interagency Monitoring of Protected Visual Environments)] aerosol and optical networks. VIEWS is also the primary source for the official [http://vista.cira.colostate.edu/views/Web/Program/IMPROVE/IMPRHR2.htm Regional Haze Rule data] that includes the baseline IMPROVE data with additional substituted data values and various data aggregations that have been calculated according to the [http://vista.cira.colostate.edu/views/Web/RHR/RHR_Planning.aspx EPA's Regional Haze Rule Guidance documents]. EMF Marc Houyoux fakeemail@epa.gov EPA Emissions Modeling Framework Emissions Modeling Framework (EMF) is an “expert system” software tool for EPA staff and contractors involved in emissions modeling. “Emissions modeling” is process of converting emission inventory data from its raw form to the forms needed by air quality models used to predict ozone, particulate matter, and hazardous air pollutants in the ambient air. This software is needed to improve efficiency and quality control of in-house and contractor emissions modeling and to provide transparency and data access to those who need information about the work of those modelers. Additionally, we anticipate making this software available to those outside of EPA so that they can have a copy of this same tool; these users would not interact with the EPA system, but rather have their own system installed at their location. The software is a client-server application based on a three-tier architecture (see http://www.sei.cmu.edu/str/descriptions/clientserver.html). The three tiers are (1) the user interface client, (2) a database management layer, and (3) a middleware layer between the other two tiers that is used to implement the business logic of the software application. This architecture is needed to support the requirement that both data and a set of compute servers be shared among a set of users. The EPA installation of the software has user access only from inside the EPA/RTP firewall. The EMF provides integrated quality control processes to foster high quality of emissions results, data handling, organization of data in a central database server, tracking of emissions modeling efforts, and real-time accessibility of information. A user interface will allow non-experts to access emission modeling capabilities. The interface will be able to facilitate various activities related to emissions modeling in addition the computation of emission inputs for air quality models. Such activities include 1) the acceptance of new EPA-provided data into the system with appropriate quality control and tracking; 2) set-up and execution of emissions modeling for air quality modeling applications; 3) quality assurance of emissions modeling results; 4) reporting and archiving of reports of emission inventory data; and 5) accessing projection and control information that is used for creating future-year emissions estimates. Can handle data from every year, if available. Emissions modeling includes emissions projections, which are also performed from the EMF. Feed emissions for air quality modeling for the purposes of air quality forecasting Feed emissions data to air quality models in support of health analysis Feed emissions to air quality models. Provide speciated, gridded, and hourly emissions from annual/county unspeciated inventory. Create spatial surrogates for spatial allocation in a scriptable way (GIS functions without having to manually do all of the steps in a GIS). Create chemical speciation factors for getting speciated PM, VOC, Hg, & Chromium. Have data interchange with EIS using formats specified (initial design work in progress). EMF uses a PostgreSQL database, Java, Hibernate, and Tomcat. Many other database sysems at EPA in Oracle. Inside EPA firewall. Not Given Small user base currently inside EPA only. No automated user feedback approach needed currently. Typically continental US. Annual coverage with hourly resolution. Process the National Emissions Inventory. Provide emissions data to (1) air quality models, (2) other OAQPS data analysis, such as PM and ozone desigations and PM NAAQS Regulatory Impact Assessments. Handles all emissions data: both criteria and air toxics. The EMF has been developed for internal EPA use to respond to needs for consistency, quality, and reproduceability that the agency has for air quality modeling. For example, we need to be able to reproduce (or provide documentation and metadata for) emissions modeling runs done months or years in the past for regulatory purposes. The data used for those runs may not be the "latest and greatest" data, thus, we need to be able to keep and quickly find and access the data actually used. We also need to be able to see and document updates to the data that have happened since runs had been done with older datasets. The EMF meets these sorts of needs. Not Given [[Media:EMF_ORD_seminar_17jan2008.ppt|EMF_ORD_seminar_17jan2008.ppt]] Not a current goal of the project. Internal EPA only. Sharing data planned with Emission Inventory System (EIS) to help reduce/prevent duplicate data. Not Given Not Given Uses the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system to process emission inventories to the resolutions needed by the air quality models and other data summaries. Uses the Spatial Surrogate Tool (based on the MIMS Spatial Allocator) to create Spatial Surrogates that are an input to SMOKE. Uses the Speciation Tool to create chemical speciation factors for VOC, needed as an input to SMOKE. MIMS Analysis Engine embedded in EMF. Uses PAVE in SMOKE run scripts to generate plots during SMOKE runs. Hoped-for links to VERDI, but no specific plans. Not Given National Computing Center (NCC) (HPC side). Not available outside of EPA firewall AIRNow John White, Tim Dye white.johne@epa.gov, dye.tim@epa.gov EPA, National, State and Local Partners AIRNow The U.S. EPA, NOAA, NPS, tribal, state, and local agencies developed AIRNow to provide the public, the media, and decision-makers with easy access to nationwide air quality information. The main web site offers daily [http://airnow.gov/index.cfm?action=static.aqi Air Quality Index] (AQI) forecasts as well as real-time AQI conditions for over 300 cities across the US, and provides links to more detailed state and local air quality Web sites. The primary purposes of AIRNow are to 1) collect and provide information to enable the public and others to take make health-based decisions, 2) provide information exchange and data access for local, tribal, state, and federal air quality agencies, and 3) provide real-time data to other research and operational data systems (NASA, NOAA, Universities, etc.). * [http://www.airnow.gov Main AIRNow website] * [http://www.airnowgateway.org Data and information distribution system] that allows organizations to register and access the publicly available AIRNow data. Data can be accessed in text files (CSV format) and web services * [http://www.airnowtech.org Technical portal into AIRNow Data Management Center] that allows stakeholders to control their data, submit their forecasts, use the GIS Navigator, and monitor status of their data. * [http://www.airnowtech.org/dmc-monitoring Monitoring system] that shows the products developed by AIRNow and allows the AIRNow DMC and EPA staff to monitor the AIRNow system. * [http://www.airnowstatus.org/media Media]- Shows examples of how the media are displaying and using information provided by the AIRNow program. * [http://www.airshare.info/smogstories/ Smog Stories] - Shows output from a joint EPA-NASA pilot project to combine real-time air quality data and satellite images to develop informative stories for the media. * [http://www.airshare.info Public communicators’ portal] for contributing and exchanging information about public outreach programs throughout the U.S. Conducting case studies – http://www.epa.gov/airnow//2007conference/monday/chan_et_al.ppt <br /> Local air quality trends and climatology - http://airquality.morpc.org/documents/July07Recap.pdf<br /> Seasonal air quality trends and news stories - [http://www.airshare.info/items/2005/11/2005_ozone_national_recap2.1229.jpg 2005 ozone national recap]<br /> AIRNow-Tech is the main technical portal for the forecasters and analysts to query, process, and display air quality and meteorological data. Typical types of applications include: * Querying real-time and historical data analysis * Submitting forecasts to the AIRNow program; verifying forecasts * Viewing real-time and historical data * Event reanalysis and reconstruction – See these videos for examples: www.airnowtech.org/Resources/AN-Tech_navigator.wmv and www.airnowtech.org/Resources/hysplit-video.wmv * Operational model verification – See: http://www.airnowdata.org/noaa/display.cfm Data and forecasts submitted by the AIRNow partner agencies is distributed in many ways to help protect public health: * AIRNow.gov web site (www.airnow.gov) * 30 Media outlets like: **http://www.weather.com/outlook/health/airquality/?state=NC ** http://www.airnowstatus.org/media/cnn2.wmv ** Look at the weather page on USA Today * EnviroFlash – email alerting service * And many others…. Model initialization and assimilation - http://people.cs.vt.edu/~asandu/Deposit/Sandu_DDDAS.pdf <br /> Model verification<br /> Art and culture: http://www.andreapolli.com/airlight/ - she recently used data from AIRNow in an art exhibit in Boulder. Data is available in a variety of common formats – KML, ASCII text, and web services. Not Given The main security barrier for AIRNow is getting real-time data and products through the EPA firewall. This was overcome by installing a dedicated file transfer server that uses SSH connections to the Data Management Center, as well as SSH to the AIRNow.gov web server. This provides a secure, but rapid, file transfer solution. AIRNow Gateway’s use of standardized web services makes those solutions reusable. User feedback is collected from the public via a Comment Handler package. Stakeholder input is collected via the Data Management Center hotline and its staff. U.S., Canada, and border regions of Mexico from 2002 to present. Hourly, real-time air quality and meteorological surface data from throughout the U.S., Canada, and border regions of Mexico. 2000 sites and growing. <br /> Daily air quality forecast issued for over 300 cites/regions<br /> Data feeds with text files, web services, and web sites to many organizations and other systems (AIRNowTech.org, AIRNowGateway.org)<br /> Daily air quality outlooks depicting weather and air quality conditions (http://www.airnow.gov/index.cfm?action=airnow.outlook)<br /> Daily email forecasts via EnviroFlash<br /> Periodic news pegs describing an air quality event <br /> Educational materials about air quality, pollutants, health effects, forecasting, etc. (http://www.airnow.gov)<br /> The roots of the AIRNow program go back to 1995 when the American Lung Association of Maryland developed ozone pollution maps that regularly appeared on television weather broadcasts in the Washington, D.C. area. Although very useful and informative to the public, the effort needed to produce these maps (data transfer, quality control, processing, etc.) was substantial and too costly to continue the following year. In the United States, air pollution problems tend to be regional in nature. Because ozone measurements collected by numerous state and local agencies were not centrally available for mapping, in 1997 EPA developed a regional Ozone Mapping System to automatically transfer data to a central location, perform data conversion and quality control, and generate animated ozone maps in near real-time. This system would be the starting point of AIRNow. AIRNow expanded rapidly with funding from one of President Clinton’s eGovernment initiatives, EMPACT (Environmental Monitoring for Public Access and Community Tracking). The goal of EMPACT was to take advantage of new technologies for providing environmental information to the public in real time, including: 1) Collecting, managing, and distributing time-relevant environmental information and 2) Providing the public with easy-to-understand information they can use in making informed, day-to-day decisions. AIRNow was the largest and most successful EMPACT project. Funds from EMPACT enabled states to purchase new hardware and software needed to upgrade their data collection infrastructure and effectively participate in AIRNow. Today the AIRNow program continues to grow to include other pollutants and meteorological data. State and local agencies find the AIRNow program to be a critical tool for protecting public health by providing their citizens with real-time air quality information. (See [http://www.airnow.gov/index.cfm?action=airnow.partnerslist Partners]) http://www.airnowtech.org/Resources/NewParameters.pdf '''Presentations:'''<br /> * High level overview of the AIRNow program ([[Media:AIRNow - high level overview.pdf|AIRNow - high level overview.pdf]]) * About AIRNow-International (schematic and initial plans) [[Media:AIRNow and AIRNow-I_Details.pdf|AIRNow and AIRNow-I_Details.pdf]]) * About AIRNow-Tech ([[Media:AIRNowTech.pdf|AIRNowTech.pdf]]) '''Papers:'''<br /> * Environmental Manager paper on the AIRNow program ([[Media:EM-Dec2002-Forecasting.pdf|EM-Dec2002-Forecasting.pdf]]) * Environmental Manager paper on the air quality forecasting efforts ([[Media:EM-airnow.pdf|EM-airnow.pdf]]) * Brief history of the AIRNow program ([[Media:AIRNow_History.pdf|AIRNow_History.pdf]]) * About the AIRNow program ([[Media:AIRNow Program.pdf|AIRNow Program.pdf]]) * Guidance document for developing an air quality forecasting program ([[Media:AQ_Forecasting_Guidance.pdf|AQ_Forecasting_Guidance.pdf]]) '''Document:'''<br /> * Information document on AIRNow Gateway ([[Media:AIRNow Gateway - web services fact sheet.pdf|AIRNow Gateway - web services fact sheet.pdf]]) '''Videos:'''<br /> * [http://www.airnowtech.org/Resources/AN-Tech_navigator.wmv about AIRNow-Tech Navigator] (www.airnowtech.org/Resources/AN-Tech_navigator.wmv) * [http://www.airnowtech.org/Resources/hysplit-video.wmv about AIRNow-Tech] the hysplit tool in Navigator (www.airnowtech.org/Resources/hysplit-video.wmv) AIRNow itself provides AQI data to the public. Raw, numerical data is available to stakeholders via AIRNowTech. AIRNow Gateway is a new addition that provides data streams to a variety of customers, utilizing a web service infrastructure. AIRNow serves to consolidate real-time data and forecasts from agencies across the country. The AIRNowTech systems also perform some integration by pulling in meteorological data, fire and smoke information, trajectory modeling, etc. Public visualization is via animated GIF maps, offered on the AIRNow.Gov website. Dynamic visualization may be accomplished via the Navigator tool on AIRNowTech. In addition, AQI current conditions and forecasts are available as KML and can be used for Google Earth mashups (http://www.airnow.gov/index.cfm?action=google_earth.main). Not Given Processing is done at the AIRNow Data Management Center, using Oracle and other custom code. QA/QC and data integration are the primary processing steps at the DMC. Public visualization is via animated GIF maps, offered on the AIRNow.Gov website. Dynamic visualization may be accomplished via the Navigator tool on AIRNowTech. In addition, AQI current conditions and forecasts are available as KML and can be used for Google Earth mashups (http://www.airnow.gov/index.cfm?action=google_earth.main). Data processing, QA/QC, integration, and visualization are all performed within the AIRNow system of systems. AIRNow is the primary store for national real-time air quality data, however the raw data exists at the State, local, or tribal agency level. It is also the store for the official air quality forecast that is issued by state/local agencies. AQS is the official store for this same data. Unidata IDD Data System Ben Domenico ben@unidata.ucar.edu NSF, NOAA, and possibly EPA Unidata Internet Data Distribution (IDD) System The Unidata community of over 150 universities has built a system for disseminating real-time earth observations via the Internet. Unlike other systems, which are based on data centers where the information can be accessed, the [http://www.unidata.ucar.edu/software/idd/ Unidata IDD] is designed so a university can request that certain data sets be delivered to computers at their site as soon as they are available from the observing system. The IDD system also allows any site with access to specialized observations to inject the dataset into the IDD for delivery to other interested sites. Many of the 250 participating sites are receiving about 2GB of real-time data per hour via the IDD -- including weather forecasts, weather station obs, data from all NEXRADS, GOES satellite imagery, lightning strike data, aircraft-borne measurements, etc. The IDD recently surpassed FTP in terms of the amount of data delivered on the Internet 2. These datasets range from huge NCEP model ouputs run on an hourly basis to data from NEXRAD radars which come in at a rate of dozens of products per second. Early experiments are underway with the DataFed group for using the underlying Unidata [http://www.unidata.ucar.edu/software/ldm/ Local Data Manager (LDM)]to deliver air quality data in real time Not sure what this means. Most of the operational weather forecasts are available in real-time as are many regional and local forecasts generated at user sites. These can be coupled with plume dispersion, storm surge, hydrological, wild fire, and other models. Weather and climate data and forecasts have many health-related impacts. See above. Not Given Unidata has been a leader in the integration of weather data into the world of OGC and ISO standards via projects such as THREDDS (THematic Real-time Environmental Distributed Data Services) and GALEON (Gateway to Air, Land, Environment, Ocean NetCDF). Unidata also hosted an Interoperability Day at last September's OGC Technical Committee meetings. Not Given The IDD/LDM uses its own well known port and each site configures the software so that only an explicit list of sites can subscribe to the data. Nearly all of Unidata's tools and software are freely available in addition to the data streams. Unidata is completely community-driven. It has a representative Policy Committee and Users Committee. Each meets twice per year to review the status and projected direction of the Unidata community and the program center. This allows the program to be very nimble in response to changing user needs and new technological opportunities. The transition from satellite broadcast to internet data distribution in 1995 is perhaps the best example of this. Once the decision was endorsed by the governing committees, the transition was effected in a matter of months. Keep in mind that the Internet in 1995 was in the early stages of the transition from a research network to a commercial entity. Varies greatly with different data types. Please click on the different types of data in the section above to get details about spatial-temporal coverage. Several different types with many options within each type. Please see: http://www.unidata.ucar.edu/data/ for more details UNIQ3c848ff54d96ef52-html-00000002-QINU The Unidata IDD has been in operation on a 24x7 basis since 1995 when it replaced a real-time data delivery system that had been used a sideband on satellite TV broadcasts that were decoded by special receivers connectd to C-band receivers at user sites. The switch to IDD did away with the subscription fee which made it possible for many more sites to receive a greatly expanded menu data streams. In the last few years, the reach of the IDD has been expanded internationally and it is now being used to ship real time data in all directions among Central and South America, Europe, and a few sites in the Far East. The underlying LDM technology has been adopted by a number of different organizations to collect and disseminate their own data. Among them are the US National Weather Service which has established a collection system for the level II and level III radar data from all the NEXRAD sites. This replace the old tape-based system, greatly reducing the cost while dramatically increasing the reliability and cutting the collection time at NCDC to virtually zero. Using Unidata LDM technology , the Spanish and South Korean Weather Services have established their own internal IDD systems for internal data distribution. The THORPEX TIGGE groups are using this technology to move data among archive centers in China, Europe, and the US. The TIGGE ensemble forecasts amount to about 20 GB per hour moving on a regular basis among these centers. Too numerous to list here. Please click on the different types of data in the section above to get details about parameters. The main goal of the Unidata data systems is to make the data available to users. Data from many different sources are available via the Unidata datastreams in real-time and can be analyzed in an integrated fashion via tools such as the Unidata Integrated Data Viewer Unidata also supports middleware such as the netCDF and the THREDDS Data Server that enable users to access data from remote servers via standard protocols for analysis and display using tools other than those provided by Unidata. Many of these are tools such as ArcGIS and IDL that are used in the GIS and Decision Support Realm Not Given Many Unidata sites have elaborate processing systems, but the central Unidata dissemination facilities deliver the data in its original form. Unidata supports a suite of analysis and visualization tools (GEMPAK, McIDAS, Integrated Data Viewer) that can be employed by users for analysis and visualization. Unidata also supports middleware such as the netCDF and the THREDDS Data Server that enable users to access data from remote servers via standard protocols for analysis and display using tools other than those provided by Unidata. Unidata has been involved in large integration projects such as GEOSS and the NSF ITR project called LEAD (Linked Environments for Atmospheric Discovery) that incorporate the Unidata suite of data systems and tools into end-to-end systems. Not Given GeoWeb Ravi Nair nair.ravi@epa.gov EPA Environmental Geoweb Service (GeoWeb) The Environmental Geoweb Service (Geoweb) is a service that locates, indexes, and presents geographically-referenced scientific data so that EPA scientists can perform cross-disciplinary searches for data of relevance to their work, and can publish data from their research that might benefit other scientists. The Geoweb Service has been designed and developed within the EPA and is currently in an early stage of production use. The Geoweb Server searches for and indexes geographically-referenced scientific data made public on the servers of organizations outside of the EPA, or on selected data servers within the EPA network. The outside sources are currently concentrated at NASA, NOAA, and the Center for Ocean-Land-Atmosphere. The inside sources include Community Mesoscale Air Quality data. The Geoweb Service has the potential to index any geographically-referenced data published on servers inside or outside of the EPA. User connection to search the index is only available from workstations on the EPA internal network. The Geoweb Server normally performs its dataset indexing at a weekly interval. The Server is instructed to web-crawl an adjustable list of scientific data servers. Metadata about the files found on the indexed servers is brought back to EPA’s Geoweb Server and assembled into an xml database. This database contains all computer-obtainable metadata about each indexed file, and the location of the file on the Internet. The data files themselves remain on the servers where originally published. Currently, about 700,000 scientific files are indexed. Users of the Geoweb Service connect to it at http://gds.rtpnc.epa.gov. They can follow links to specific data sources found on the home page, or perform searches for data by providing search terms. The searches execute quickly because the metadata being searched is all local to the Geoweb Server. The user is presented with a search results list, similar to Google or Yahoo search results. The user can then view the metadata associated with each dataset, or initiate a visualization of the data contents. The visualizations are performed using the World Wind Geo tool, based on NASA’s World Wind, and developed here at EPA; or by using Google Earth, NASA’s World Wind, or ArcGIS Explorer. The reading of metadata and the visualization of the data correctly referenced to the World Wind, Google Earth, or ArcGIS globe is supported for all data set types. Data served from OPeNDAP servers can also be subsetted by geographic bounding boxes, date, or time and the data subset can then be downloaded across the Internet to the user’s workstation. Not Given Not Given Not Given Not Given Not Given Not Given Not Given As of February, 2008, the Geoweb Service is on the EPA Intranet at http://gds.rtpnc.epa.gov. The Geoweb application is operable in the early stages of production use. A User’s Guide is linked to the home page at the URL above. The User’s Guide includes several two-minute videos illustrating the use of the Geoweb tools. Those outside of the EPA network do not have access. Not Given Not Given Not Given Not Given n/a Not Given All of the files indexed by the Geoweb Service provide metadata so that the user can understand the content, and are copied from their source to the user’s workstation when a visualization is requested. The visualization is performed on the user’s workstation within the World Wind Geo tool, within Google Earth, etc. Some of the files indexed by the Geoweb Service are served by OPeNDAP servers (Open-source Project for a Network Data Access Protocol) operated by NASA, NOAA, etc. Web browsers can work with OPeNDAP servers to identify a subset of the data, based on the variables in the file; and then download just that subset of data to the user’s workstation. Not Given Not Given Not Given Not Given A user of the Geoweb connects to the Server using a web browser such as Firefox or Internet Explorer. This permits searching for data files and viewing of metadata about each file. After a file of interest is located, the data can be visualized on-screen provided that a tool such as World Wind Geo, Google Earth, NASA’s World Wind, or ArcGIS Explorer are installed on the workstation. For a survey of map visualization tools for use within the EPA, see http://intranet.epa.gov/rtpgis/mapvizsvcs.html. The World Wind Geo tool is a free download from http://code.google.com/p/worldwindrcp/. Google Earth Pro is available within the EPA via Agency-wide license. See http://intranet.epa.gov/rtpgis/mapvizsvcs.html for information on obtaining the license. NASA’s World Wind is a free download from http://worldwind.arc.nasa.gov/. ArcGIS Explorer is a free download from http://www.esri.com/software/arcgis/explorer/index.html. Not Given Not Given DataFed Rudy Husar rhusar@me.wustl.edu Washington University DataFed DataFed is Web services-based software that non-intrusively mediates between autonomous, distributed data providers and users. DataFed is designed in accordance with the GEOSS architecture; It provides standard interfaces to heterogeneous distributed data, fosters data integration and use with processing web services and tools, and collects metadata and user-feedback on datasets. DataFed also provides standards-based data feeds to the NASA Giovanni System. DataFed was used to perform aerosol characterization for the RPO project, [http://datafedwiki.wustl.edu/index.php/FASTNET FASTNET]. DataFed is the main data source supporting the development of [http://datafedwiki.wustl.edu/index.php/2005/6_Exceptional_Events_Project EPA's Exceptional Event Rule]. It is now used in the [[Exceptional_Air_Pollution_Event_Analysis_Community_Workspace| implementation of the EE Rule.]] A current NASA project with [http://groups.google.com/group/nasa-aq-forecast BAMS] uses DataFed to assimilate surface obs. into a forecast model. We are not aware of any formal Air Quality Reanalysis effort; hopefully, thee community will No applications to health studies. However, the datasets mediated through DataFed are suitable for health studies, particularly in conjunction with the 1km-resolution US population data. The [http://capita.wustl.edu/NEISGEI/main.html EPA NEISGEI Project] uses DataFed to integrate and to evaluate multiple emission databases. DataFed was used to prepare an [http://capita.wustl.edu/models3eval/IMPComp/FinalReporModels3.htm evaluation of the CMAQ Aerosol Model with IMPROVE and FRM data]. In the NASA project with [http://groups.google.com/group/nasa-aq-forecast BAMS], DataFed provides surface observations for assimilation into a forecast model. Add HTAP data integration... Since 2004, a major role of DataFed was to participate in [[EPA_Air_Quality_Data_Systems_and_GEOSS_Architecture| interoperability experiments for GEOSS]]. Both the raw input data as well as the processed outputs are accessible through international standard interfaces. This allows the creation of [http://datafedwiki.wustl.edu/index.php/2007-07-25_IGARSS07_Barcelona loosely-coupled network applications] ([http://capita.wustl.edu/capita/capitareports/070725IGARSS07_Barcelona/DataFed_IGARSS07_Barcelona_ER3.pdf Paper-PDF]). The DataFed architecture has been used as a model for demonstrating the [http://datafedwiki.wustl.edu/index.php/2007-01-25_HTAP_Mtg_Geneva "System of Systems" aspect of GEOSS]. The data access and processing services are accessible through the SOAP-WSDL protocol, which is designed to pass through firewalls. At this time there are no access restrictions to these services. The data access, processing and visualization services in DataFed are all composed of reusable Web Services through both SOAP and REST protocols ([http://capita.wustl.edu/capita/capitareports/070725IGARSS07_Barcelona/DataFed_IGARSS07_Barcelona_ER3.pdf Paper-PDF]). For each dataset registered in DataFed there is a "DataSpace" wiki page for the collection of dataset-relavent information, including user feedback (e.g. [[AIRNOW|AirNOW]]). About half of the datasets are global scale, a third are US-scale, while some datasets are for other regions. Most datasets are multi-year in extent. About a ten datasets are near-real-time. [http://datafedwiki.wustl.edu/index.php/Compact_Catalog_-_Alphabetical See Dataset Catalog] The federated data system, DataFed, was in development since 2001 at Washington University, CAPITA, with grants from [http://capita.wustl.edu/capita/researchareas/NSFPropSubm/FinalReport/FinalReport_050629.pdf NSF], [http://capita.wustl.edu/capita/researchareas/NASAReason/ESEPMNASAReasonAbstr.htm NASA], [http://capita.wustl.edu/NEISGEI/main.html EPA] and [http://datafedwiki.wustl.edu/images/c/c4/EM_DataFed_FASTNET_050720.pdf RPOs]. Since 2004, DataFed served both Regulatory and Policy support to EPA. Within CAPITA, DataFed has become a scientific data analysis tool. DataFed provides access to over [http://datafedwiki.wustl.edu/index.php/Compact_Catalog_-_Alphabetical 100+] distributed, air-quality relevant datasets (surface, satellite, and model) which can be explored and analyzed by tools for processing and visualization. [http://datafedwiki.wustl.edu/index.php/FASTNET FASTNET],[http://datafedwiki.wustl.edu/index.php/2005/6_Exceptional_Events_Project EPA Exceptional Event Project], [http://datafedwiki.wustl.edu/index.php/2005/6_Exceptional_Events_Project Interoperability of Web Service-Based Data Access and Processing...] ESTO 2006, [http://datafedwiki.wustl.edu/index.php/CATT Combined Aerosol Trajectory Tool, CATT], [http://capita.wustl.edu/capita/capitareports/070725IGARSS07_Barcelona/DataFed_IGARSS07_Barcelona_ER3.pdf DataFed: Mediated Web Services for Distributed AQ Data Access and Processing] IGARS 2007, [http://datafedwiki.wustl.edu/index.php/2007-01-25_HTAP_Mtg_Geneva Interoperable Info System of Systems for HTAP] HTAP 2007 DataFed is a homogenizer of distributed, heterogeneous datasets through [http://esto.nasa.gov/conferences/ESTC2006/papers/a6p2.pdf data 'wrappers']. As a result all the data mediated in DataFed are accessible through international [http://esto.nasa.gov/conferences/ESTC2006/papers/a6p2.pdf standard data access services, OGC WCS and WMS]. At this time all data access services are free and offered through an open interface. Data consolidation from heterogeneous to homogeneous structure is performed on the fly for most datasets. Many historical datasets are cached at DataFed for fast data access and browsing. DataFed has served the RPOs through the [http://datafedwiki.wustl.edu/index.php/FASTNET FASTNET] and [http://datafedwiki.wustl.edu/index.php/CATT Combined Aerosol Trajectory Tool (CATT)] projects. More recently DataFed supports the decisions for the Exceptional Event Rule for PM2.5 and ozone. Not Given The processing of raw data is performed by reusable web-service components, which include filtering, aggregation, and data fusion services. Data processing applications are created by chaining services using workflow software. The visualization tools for parameter-spatial-temporal browsing are applicable for each dataset in the federated data system. The output data from the processing services are also available for mashups with other popular tools e.g. [http://datafedwiki.wustl.edu/index.php/2007-07-18_ESIP_Demo_OMI_NO2 Google Earth] and GIS software. Data access, processing and visualization are all performed within DataFed. Specific workflow configurations are created from the loosely coupled web services for different scientific analysis or decision-support applications. An example custom workflow is the [http://datafedwiki.wustl.edu/index.php/CATT Combined Aerosol Trajectory Tool (CATT)]. DataFed is a mediator of data flow between providers and users. It does not primary/official data. RSIG Heidi Paulsen paulsen.heidi@epamail.epa.gov EPA Remote Sensing Information Gateway (RSIG) The RSIG applet allows users to interactively specify, visualize and save a subset of meteorological and air-quality data from a variety of sources including: * NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data * AIRNow data via the AIRNow Gateway * Air Quality System (AQS) data via the Air Quality Data Mart * EPA's Community Multiscale Air Quality (CMAQ) model output * National Environmental Satellite, Data, and Information Service (NESDIS) biomass burning data * UVNet Data subsets are interactively specified by user-selected variable,time-range and lon-lat box and quickly displayed together over a map and animated over time. Images and data may be saved on the user's computer in a variety of formats, including PNG, MPEG, KMZ, ASCII, XDR, NetCDF, and HDF, for later analysis and integration with other applications. The RSIG applet accesses a set of remote OGC-WCS web server applications also developed at EPA and installed at the remote sites that store the data. By operating at the source, these servers and associated subset programs enable fast and convenient streaming of data subsets across the network to achieve an impressive response of about a minute per day's worth of data. (Other applications, such as AirQuest, also benefit by utilizing these powerful server programs.) See also [[Remote Sensing Information Gateway (RSIG)]] Not Given Not Given Not Given Not Given Not Given RSIG uses Open Geospatial Consortium (OGC) Web Mapping Service (WMS) and Web Coverage Service (WCS) standards for specifying data subsets and a supports a variety of standard output formats (ASCII, NetCDF, KMZ, etc.) requested by users to enable data to be saved and input into other applications. Not Given Access outside the EPA is being addressed by installing the WCS servers and downloadable applet on the EPA Portal. This requires an EPA firewall modification to allow http-80 access from external computers to an internal computer used for visualization. RSIG components include an applet and set of documented WCS server applications and powerful subset and converter programs - all of which are freely available and accessible by others. This applet/server/subset method will be updated as needed to access additional data as requested by users and prioritized by the Principal Investigator. Principal Investigator: Dr. Jim Szykman (757) 864-2709. Please send all questions and comments to Jim Szykman or Heidi Paulsen. Not Given *AIRNow ground stations hourly Ozone and PM2.5 measurements. North America, 2003-present.<br /> *AQS ground stations hourly and aggregated Ozone and PM2.5 measurements. North America, 1995-present.<br /> *NESDIS Biomass Burning surface point values of PM2.5, CH4, CO2, CO, N2O, NH3, NOx, SO2, TNMHC. Currently selected week events in August 2005, July 2006, October 2007.<br /> *NASA MODIS-4/6/7 satellite surface-integrated data including: Aerosol Optical Depth, Cloud Optical Thickness, Ozone, Pressure, Temperature, etc. Global 2000-present.<br /> * EPA CMAQ gridded model data including: O3, NO2, CO, PM25, WIND, etc. Currently Eastern US, 2001, August 2004, August 2005.<br /> * UVNet: Twenty-two ground stations in the US measuring Irradiance (W/m2) and UVBio "sunburn" (W/m2), 1996 through June 2004. n/a See above [[Media:Rsig 11072007 300 FINAL READ ONLY.pdf|RSIG Brochure]] Not Given Yes. RSIG visualizes data together over a map. It will also soon have the ability to regrid the data to a specified CMAQ grid to facilitate integration with other applications such as Hierarchical Bayesian/Markov Chain Monte Carlo (HB/MCMC). Not presently. There are plans to enable RSIG to launch other applications such as HB/MCMC and visualize their results. Also, external applications such as AirQuest can utilize RSIG's WCS servers. The primary value-added strengths of RSIG are its ease of use, performance and user-driven custom features. Yes. RSIG does subsetting, reformatting, conversion, and augmenting of data to facilitate visualization and subsequent analysis with other datasets. Examples include: aggregating across multiple files covering the user-specified date/time range, subsetting to a lon-lat box, striding over dense data points for fast visualization rasterization, decoding integer data to reals, lossless compression/decompression to speed-up transfer over the network, units conversion to SI and model-common units, data reordering to match model-common conventions such as ground-up, augmentation with longitude, latitude and elevation for each data value, and projecting and aggregating into model grid cells. Yes. RSIG provides a simple and quick interactive visualization of all data overlaid together on an appropriate map. Not presently. There are plans for HB/MCMC launch and visualize. No. RSIG generally accesses data from other sources (and subsets it at the source). CASTNET Gary Lear lear.gary@epa.gov EPA, NADP/NTN, CAPMoN CASTNET The Clean Air Status & Trends Network (CASTNET) is a rural, regional monitoring network designed primarily to measure seasonal and annual average concentrations and depositions over many years. CASTNET measurements are used to track long-term temporal and spatial trends due to changes in air quality. Data is used to assess the environmental impacts as a result of the U.S. EPA’s emission reduction programs (in particular, the Acid Rain Program (ARP) and the NOx Budget Trading Program (NBP)). In the future, CASTNET is expected to serve as an assessment tool for the Clean Air Interstate Rule (CAIR). Data measured from the CASTNET network is used by universities, ecologists, tribes, as well as federal regulators, as it is the only national long-term, regional, rural monitoring network. More than 50 CASTNET sites have been collecting data consistently for over 15 years and are used extensively for determining trends in air chemistry and deposition. CASTNET data is used by the EPA as an accountability tool for regulated emissions reductions programs. CASTNET data from 35 sites are currently submitted to AIRNow. The remainder of network sites are planned for submission in 2008. Real-time ozone measurements being sent to AirNow determine “ozone action days” or days when it is unhealthy for children, elderly and those with weak respiratory systems to go outside. CASTNET data is used for ecological health studies, less so for human health. CASTNET has been and is continuing to be used as an assessment tool for emission reduction programs such as the Acid Rain Program and the NOx Budget Trading Program. CASTNET data is also used extensively for validation and development of atmospheric models, including CMAQ. Not Given Data is available within the EPA firewall via SQL*Net and ODBC drivers. Data are available via the Web as prepackaged data sets and a data query wizard. Not Given Database is behind EPA firewall. Not Given Comments via EPA’s web site comments. CASTNET sites are located throughout the United States, with the best spatial coverage in the East. Many CASTNET sites have been collecting data for 15+ years. The filter pack collects weekly samples of particulate and gaseous data while the ozone monitor records hourly averages at each site. All data can be found/downloaded on the CASTNET website at http://www.epa.gov/castnet, while real-time ozone concentrations from 35 sites are submitted to AirNow and can be viewed on the AirNow website. Developed from the National Dry Deposition Network (NDDN), CASTNET was established in 1991 under the Clean Air Act Amendments. CASTNET provides nationwide data for determining relationships between changes in emissions and any subsequent changes in air quality, atmospheric deposition, and ecological effects. The network has been a key assessment tool for tracking changes in SO2, NOx and O3 after implementation of the Acid Rain Program, NOx State Implementation Plan, and the NOx Budget Trading Program. Weekly samples – SO2, HNO3, SO42-, NO3-, NH4+, Ca2+, Mg2+, K+, Na+ and Cl- 1-hr samples – Ozone, temperature (2m & 9m), wind speed, wind direction, sigma theta, solar radiation, relative humidity, precipitation, surface wetness http://www.epa.gov/castnet/library All data is provided on the CASTNET website after it is QA’ed. Ozone data is provided to the public through the CASTNET website and AirNow Not Given Not Given Not Given Data is managed in an Oracle 10g database Concentration maps for the Eastern and CONUS regions are shown on the CASTNET website. Deposition trends plots, changes in meteorology, and ozone concentrations are shown for each site on the CASTNET website. Additional publications with supporting visualization and analysis are also available on the website. Not Given Not Given ESIP Air Quality Cluster esip-aqcluster@rtpnet.org NASA, NOAA, EPA are strategic partners ESIP The Federation of Earth Science Information Partners (ESIP) is a consortium of over 100 organizations in government academia, and industry that collect, disseminate, interpret and apply Earth observation information. ESIP serves as a forum for fostering and sustaining collaborations and interoperability that aim to improve processes for the use of earth observation information in research, education, management and policy. ESIP "clusters" are domain-specific focus groups that are intended to promote informal, easy exchange of information among organizations. The ESIP Air Quality Cluster aims to connect air quality data consumers and providers by: * bringing people and ideas together on how to deliver ES data to AQ researchers, managers and other users/consumers * facilitate and demonstrate the information flow of from data providers to air quality consumer [[Image:ESIP-Decadal_Survey_Mapping.jpg|ESIP in light of Decadal Survey]] Not Given Not Given The Public Health Applications in Remote Sensing (PHAiRS) project is an active ESIP participant and its connection to DataFed for access to air quality data was facilitated by ESIP: [[PHAiRS_-_DataFed_Interaction]]. Not Given ESIP does not focus on a particular application area but its organizational framework, diverse participation, and information science and technology expertise form an environment to support air quality applications. ESIP promotes the use of standards for data interoperability. The ESIP semantic web cluster is exploring new technologies for enhancing data access and interoperability. Not Given Not Given Use of wiki, portals, catalogs. Guidance on best practices. Promotes web services and service oriented architecture approaches to reuse. Wikis. Metadata. Semantic web (ontologies). Depends on data source (global to regional and historical to nea real time). ESIP is not a data system in the sense of producing or directly serving data. However, many of its members are data producers. ESIP serves as broker for earth science data ranging from satellite imagery to surface observations and models. ESIP provides a forum to aggregate and synthesize metadata for dataset descriptions, discussions about dataset applications and tools for analyzing and visualizing data. See [http://wiki.esipfed.org/index.php/AQ_Dataset_Metadata_Workspaces DataSpaces] *1998 - ESIP Formed by NASA in Response to a NRC Recommendation for "Community Involvement" in EOSDIS. *2003 – Evolved Plan to Become a Broad-Based Inter-Disciplinary Collaborative Forum (Cyberinfrastructure) for the Earth Science Information Community. *2004 - NOAA/NESDIS Becomes Second Strategic Partner. *2005 - Air Quality Cluster formed *2007 – EPA/ORD becomes Third Strategic Partner *2003-2007 – Membership Grows from 27 to 106 Entities. Broad range See activity pages, presentations and concept papers at http://wiki.esipfed.org/index.php/Air_Quality_Cluster Promote the integration and flow of Earth science data from collection and analysis to end-use. Metadata aggregator. Provide domain-specific context for data where metadata and other contextual information about earth science data is available to help understand how data are created and used. Not Given Not Given Not Given Not Given Promote the integration and flow of Earth science data from collection and analysis to end-use. No. Although many of its members provide that service. NEISGEI Stefan Falke stefan.falke@gmail.com EPA, NASA NEISGEI NEISGEI is part of an effort to foster global air emissions networks that provide access to distributed emission inventory data, analysis tools, and an environment for collaboration among researchers, managers, and the interested public. Not Given Not Given Not Given Applies OGC standards for data access and analysis. Creates "mashup" applciations for comparing and reconciling emissions inventories, models, surface observations, and satellite imagery. Not Given OGC standards (WMS, WCS, WFS) Not Given Not explicitly addressed. Modular design for application development. Portal, wikis. Not Given OGC interface access to North American emissions inventories, regional and global emissions models, and activity data (e.g., satellite derived fire locations). Not Given Not Given Standards based access to emissions and related data. Uses DataFed for data registration and access. Not Given Not Given Not Given Web services for data aggregation and analysis. Mashup web applications for spatial, temporal and other charting visualizations and analysis. Not Given NEISGEI provides standardized interfaces to emissions data stores. EPA AIRQuest Data Warehouse Phil Dickerson dickerson.phil@epa.gov EPA EPA AIRQuest Data Warehouse The audience, at present, is INTERNAL EPA analysts, application builders, and data integrators. AIRQuest was constructed as a true data warehouse, built to integrate data from various source data systems, to enable complete air quality analyses from a single system. AIRQuest harvests data from AQS, NEI, and AIRNow, integrating that data spatially and temporally. It also features datasets outside of EPA that are useful for analysis -- meteorological, census, etc. Not Given Not Given Exposure for sensitive populations; analysis Not Given Not Given Not Given Not Given Not Given Not Given Not Given Temporal varies with dataset, but 10 years of AQS, latest NEI inventory, etc. Spatial coverage is entire US at county level AQS, NEI, AIRNow, MODIS, GASP, meterological, census, REMSAD model runs, GIS layers AIRQuest evolved from an OAQPS internal need for access to integrated data, without having to comb through a variety of source data systems. The vision was to develop a single source of high-quality, integrated, and up-to-date data, thereby allowing analysts to do their jobs without having to visit a myriad of sources and perform their own integration. Another important concept was that of "one answer", i.e. a warehouse codified business processes such that each analysis can be done consistently and with reproducible results. All criteria pollutants, standard meterological measurements, complete demographics Not Given Not Given Not Given Not Given Not Given Not Given Not Given None 3D-AQS Dr. Ray Hoff, University of Maryland, Baltimore County; Jill Engel-Cox, Battelle Memorial Institute hoff@umbc.edu; engelcoxj@battelle.org University of Maryland, Baltimore County. Partners include Battelle Memorial Institute, University of Wisconsin, NASA, EPA, NOAA, and CDC. Three Dimensional Air Quality System (3D-AQS) The 3D-AQS project is using a range of remote sensing instruments to expand EPA’s AQS into a three-dimensional system of nation-wide coverage, with the goal of providing information on the vertical and horizontal distribution of pollutants, particularly aerosols and particulates. The 3D-AQS project has three key initiatives. First, the project is improving existing data and visualization methods by developing finer resolution products, quantitative analysis of the satellite data with ground-based data, and creating new visualizations of 3D air quality information. Second, the project integrates satellite and lidar data into EPA’s AirQuest system, a database that merges AQS and AIRNow data with other monitor, model, and socioeconomic data, making the NASA data easily accessible. Third, the Integrating satellite Data from Environmental Applications (IDEA) product, which unites a range of satellite data in near real time, is bing migrated to an operational environment within NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS). IDEA is being enhanced to include data from GASP and the ground-based LIDAR sensors. An end user advisory committee comprised of air quality forecasters and analysts is providing input and advice at each stage of the project. Not Given Not Given Not Given Not Given Not Given Not Given Not Given Not Given Not Given Not Given Not Given Not Given Three year project funded by NASA Applied Sciences Program. Not Given Available at http://alg.umbc.edu/3D-AQS. Also, numerous journal publications. Not Given Not Given Not Given Not Given Not Given Not Given Not Given Not Given NASA Atmospheric Science Data Center Science User and Data Services larc@eos.nasa.gov NASA NASA Atmospheric Science Data Center The Earth Observing System Data and Information System (EOSDIS) manages and distributes data products through the Distributed Active Archive Centers (DAACs). The centers process, archive, document, and distribute data from NASA’s past and current research satellites and field programs. Each center serves one or more specific Earth science disciplines and provides data products, data information, services, and tools unique to its particular science. The Atmospheric Science Data Center is responsible for processing, archival and distribution of NASA Earth science data in the areas of radiation budget, clouds, aerosols, and tropospheric chemistry. It is unique among NASA data centers in the size of its archive, cutting edge computing technology and full range of data services. Anthropogenic emissions monitoring Atmospheric profiling and vertical structure Air quality research and decision support Aerosol plume dispersion Renewable energy/energy management Metadata managed in PostGres POSTGIS open source database Not Given Federal IT security regulation compliant. Redundant firewall with NASA Fully scalable architecture. Direct interaction with user communities translated into ongoing system development requirements. Global. Temporal coverage varies by data set * Clouds and the Earth’s Radiant Energy System (CERES) * Cloud-Aerosol Lidar and Infrared Pathfinder satellite Observations (CALIPSO) * Multi-angle Imaging Specrtroradiometer (MISR) * Measurements of Pollution in the Atmosphere (MOPITT) * Tropospheric Emission Spectrometer (TES) * Stratospheric Aerosol and Gas Experiment (SAGE-III) The Data Centers were established in 1991 to support the Earth Observing System as part of NASA’s Earth Science Enterprise and the U.S. Global Change Research Program, and is part of the Earth Observing System Data and Information System (EOSDIS). Radiation budget, clouds, aerosols, and tropospheric chemistry Several custom order tools and subset tools. * Data format primarilyin HDF-EOS with integrated metadata. * Metadata managed in PostGres POSTGIS open source database. * Air quality decision support * Renewable energy/energy management decision support Not Given Full high-level processing for CERES, MISR and CALIPSO data sets. Not Given Full science data life cycle from processing to archival and distribution Store next near-online tape silo, online data poo HEI Betty K. Pun, Ph.D pun@aer.com Health Effects Institute HEI Air Quality Database The HEI Air Quality Database focuses on levels of PM2.5 components and gaseous pollutants at and near sites in the EPA's PM2.5 Chemical Speciation Trends Network (STN) and State, Local and Tribal air monitoring stations (SLAMS). The Database was originally made available in association with HEI's Request for Applications 05-1, Studies to Compare Components and Characteristics of Particulate Matter Associated with Health Effects, which was issued in the summer of 2005. The Database is now available to investigators interested in using the information for studies on air quality and health. Currently, the Database contains information on speciated PM components and gaseous pollutants at these sites for the years 2000-2006 and it is anticipated that the information will be updated regularly for the next 2-3 years. Yes, based on existing user community Yes, based on existing user community Intended target audience, e.g., epidemiological studies Yes, based on existing user community Community outreach, international collaboration Not Given Not Given user accounts and password requirements Not Given email United States, 2000-2006 * '''Meta data associated with STN core + supplemental sites:''' #State, County, Site ID # Latitude/longitude # Metropolitan area # Population (census) # Local emissions (National emissions inventory) # Land use/setting/monitoring objective * '''Concentrations and meteorology from AQS:''' # PM2.5 mass, elements, ions, OC/EC (STN network) # Gases (subset of monitoring sites near STN sites): Ozone, CO, SO2, NO2, NOx (processed into daily maximum one-hour concentration, daily maximum 8-hour average concentration, or 24-hour average concentration) (SLAMS/NAMS/PAMS network) # Meteorology: temperature, RH, wind speed (processed into daily maximum one-hour value, daily maximum 8-hour average value, or 24-hour average value), wind direction (direction associated with maximum one-hour wind speed or most frequent compass direction in the 24-hour period) * '''Meteorology from NCDC''' # temperature, dew point, visibility wind speed (processed into daily maximum one-hour value, daily maximum 8-hour average value, or 24-hour average value), wind direction (direction associated with maximum one-hour wind speed or most frequent compass direction in the 24-hour period) The HEI Air Quality Database was originally made available in association with HEI's Request for Applications 05-1, Studies to Compare Components and Characteristics of Particulate Matter Associated with Health Effects, which was issued in the summer of 2005. Since then, the database continues to evolve in response to requests from the health community, for example, data on PM elements and carbon blanks have been made available. The database is updated approximately every six months using the most current data from the Air Quality System and other source databases. see above * B. Pun, C. Seigneur, S.Y. Chen, M. Sze (2005) Creation of an Air Quality Database for Health Effects Studies, HEI Annual Conference, Baltimore, MD, April 2005 * B. Pun, C. Seigneur, S.Y. Chen, M. Sze (2006) Web-based Database for Particulate Matter Health Effects Studies, HEI Annual Conference, San Francisco, CA, April 2006. * Pun, B.K., C. Seigneur, S.Y. Chen, M. Sze (2006) The HEI Air Quality Database, HEI Workshop: Air Quality Data in Health Effects Research, Newton, MA, November 2006. * B. Pun, C. Seigneur, S.Y. Chen, M. Sze (2007) Web-based Database for Particulate Matter Health Effects Studies, HEI Annual Conference, Chicago, IL, April 2007. * Pun, B., C. Seigneur, E. Edgerton, 2004. Creation of an air pollutant database for health effects studies. Phase I report: Collection of pm speciation and related data (can be downloaded from http://hei.aer.com/help.php) * Leslie, M., 2006. Something in the air (Netwatch), Science, 311, 153. Yes, using csv files see dataset information queries on meta data allow users to become familiar with dataset and availability of data Not Given see dataset information maps of sites, interpolated spatial concentration plots, time series graphs and pie charts from the selection of relevant/interesting sites to data download (one query form for several types of relevant information) Not Given CMAQ Norm Possiel none EPA Community Multi-scale Air Quality Model (CMAQ) The CMAQ model is a comprehensive, three-dimensional, grid-based Eulerian air quality model designed to estimate ozone and particulate concentrations and deposition over a broad range of temporal and spatial scales. CMAQ, which is a publically available, peer-reviewed, state-of-the-science model, consists of a number of science attributes that are critical for simulating the oxidant precursors and non-linear organic and inorganic chemical relationships associated with the formation of ozone and sulfate, nitrate, and organic aerosols. CMAQ also simulates the transport and removal of directly emitted particles which are speciated as elemental carbon, crustal material, nitrate, sulfate, and organic aerosols. Additionally, the multi-pollutant version of CMAQ simulates mercury and over 30 toxic volatile organic compounds and metals. CMAQ is being used by the US EPA, State and Regional environmental organizations, universities, and independent research organizations to provide data in support of a wide range of analyses linked to understanding the spatial and temporal patterns of pollutant concentrations and deposition in relation to emissions and meteorology and as part of regulatory programs for evaluating the effectiveness of emissions control strategies for attainment of National Ambient Air Quality Standards. The US EPA’s Office of Air Quality Planning and Standards in collaboration with the Office of Research and Development have recently run CMAQ for the full year of 2002 using a 2002-based air quality modeling platform. The predictions from CMAQ simulations for multiple years provide a means to complement air quality measurements for characterizing trends and for evaluating the effectiveness of control programs as part of “accountability” analyses and dynamic evaluation of the model. The US EPA is building a dataset of annual CMAQ predictions which began with model simulations for 2002. The US EPA and other organizations run CMAQ in near “real-time” to provide daily forecasts of air quality based on model predictions of ozone and PM2.5 The outputs of CMAQ for baseline and emissions control cases are post-processed to create spatial fields of ozone and PM2.5 concentrations in a format for input to health-benefits calculation models. In addition, ozone and PM2.5 from CMAQ are blended with ambient data through the application of various “data fusion” techniques to provide concentration fields for analysis with health effects data. The US EPA conducts operational, diagnostic, and dynamic evaluations of CMAQ predictions using ambient data from various monitoring networks including AQS, CASTNET, NADP, SEARCH, and special field studies having measurements at ground level and aloft. Not Given Not Given Not Given Not Given Not Given Not Given The spatial and temporal coverage of CMAQ applications is user-defined. CMAQ applications by the US EPA typically cover national spatial scales at 36 x 36 km and regional scales at 12 x 12 km resolution. Temporal scales range from episodic to annual. The US EPA 2002 CMAQ dataset is based on modeling at 12 x 12 km resolution for the full year of 2002. The CMAQ modeling system contains both input and output datasets which include: meteorological and emissions inputs, and concentration and deposition outputs. US EPA has performed CMAQ simulations for the full year of 2002 for two modeling domains which together cover the Eastern and Western US along with adjacent portions of Canada and Mexico. This dataset includes the types of parameters listed below at a horizontal resolution of approximately 12 x 12 km and 14 layers in the vertical up to 100mb. CMAQ is a product of the US EPA’s Office of Research and Development and is updated on a regular basis to reflect the current state-of-the-science in atmospheric chemistry and meteorological processes. Meteorological input files for CMAQ contain hourly, gridded data for more than 50 meteorological parameters. Emissions input files for criteria pollutant (e.g., ozone and PM) modeling contain hourly, gridded data for 30 gas and particle pollutant species. The CMAQ output files contain hourly, gridded concentrations for 30 gas and particle species and wet and dry deposition for 18 species. The emissions and meteorological input and CMAQ concentration output files contain data for all vertical layers specified for the particular model simulation. Papers and reports on the development and application of CMAQ by various groups in the modeling community can be found at the following URL: http://www.cmascenter.org/resources/bibliography.cfm?temp_id=99999 Not Given Not Given Not Given Not Given Not Given Not Given Not Given Not Given NARSTO Les Hook, Director; Sig Christensen, Data Management Coordinator hookla@ornl.gov; christensen1@ornl.gov Sponsored by DOE at the Oak Ridge National Laboratory NARSTO Quality Systems Science Center Archive NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. Its primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; its activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available '''through the NARSTO Permanent Data Archive at the Langley DAAC.''' Yes, in places and times where data exist. No. Yes. Yes, in places and times where data exist. NARSTO data in the Data Exchange Standard are in a spreadsheet-compatible .csv (ASCII) self-documenting format at the Langley DAAC. The data should remain available indefinitely. http://eosweb.larc.nasa.gov/PRODOCS/narsto/table_narsto.html Data are located via the Web and are downloaded by FTP. Most users open the data files in a spreadsheet program. Not Given Conventional security issues only; access is via anonymous ftp (from the QSSC) or by user-created accounts (from the Langley DAAC).. The Data Exchange Standard is reasonably flexible and it can be used for a wide variety of air quality data. The Data Exchange Standard and its implementation were guided by a workshop and many conference calls involving data providers and potential data users. North America, 1996 to the present Mainly air quality research data, including data from the EPA Supersites, Phases 1 and 2, and other intensive studies. Established ca 1999 Multiple air quality and particulate matter measurements. The parameters available can be seen in the search tool at http://cdiac.ornl.gov/programs/ameriflux/data_system/narsto_file_search.html This is the primary purpose of the NARSTO Permanent Data Archive This is being developed. Not Given Not Given Data in the Data Exchange Standard format are checked for various aspects of formatting and some content QA, and issues are resolved prior to archiving. Consistent data and metadata reporting requirements. Plots are provided for most currently archived data, and are sometimes generated for previously archived data. Not Given The Langley DAAC, http://eosweb.larc.nasa.gov/PRODOCS/narsto/table_narsto.html DataSystemRef PrimaryDataStorage EndtoEndIntegration DataSystemValueVis DataSystemValueProcess DataSystemValueOther DataSystemValueDecisionSupport DataSystemValueConsolidation DataSystemValueAccess DataSystemParam DataSystemHistory DataSystemDataSets DataSystemCoverage DataSystemArchUserFeedbck DataSystemArchToolsMethods DataSystemArchSecurity DataSystemArchOther DataSystemArchInterop DataSystemAppOther DataSystemAppModelEval DataSystemAppHealth DataSystemAppFcstReAnaly DataSystemAppCharact About PresentationURL DataSystemURL DataSystemName DataSystemAgencies Contactemail ContactName WCS Candidate