1. 05/23/2006 SM23A-06 1 A Virtual Observatory for the Ionosphere-Mesosphere- Thermosphere Community D. Morrison, M. Weiss, R. Daley, L. Immer, S. Nylund, J.-H. Yee, and E. Talaat (Johns Hopkins University, Applied Physics Laboratory 11100 Johns Hopkins Rd. Laurel MD, 20723: 240-228-4172) J. Russell (Hampton University) R. Heelis (Univ. Texas at Dallas) J. Kozyra (Univ. of Michigan) D. Bilitza, R. McGuire, and R. Candey (NASA Goddard Spaceflight Center) P. Fox (HAO/NCAR)

2. 05/23/2006 2SM23A-06 Virtual ITM Observatory The Virtual ITM Observatory (VITMO) is one of the recently awarded domain specific NASA virtual observatories (VxO). The Virtual ITM Observatory (VITMO) is one of the recently awarded domain specific NASA virtual observatories (VxO). VITMO will focus on data covering the ITM region as well as providing connections to the energy drivers for this region (principally solar and magnetospheric). VITMO will focus on data covering the ITM region as well as providing connections to the energy drivers for this region (principally solar and magnetospheric).

3. 05/23/2006 3SM23A-06 What is a VxO? An observatory is a location used for observing terrestrial and/or celestial events. An observatory is a location used for observing terrestrial and/or celestial events. A virtual observatory is a system that allows an observer to work as if they were at the observatory. A virtual observatory is a system that allows an observer to work as if they were at the observatory. The VO does this typically by allowing the observer to access multiple data files (“observations”) from multiple sources of the object of interest. The VO does this typically by allowing the observer to access multiple data files (“observations”) from multiple sources of the object of interest. The VO allows access to “different instruments” to study a given system or phenomena. The VO allows access to “different instruments” to study a given system or phenomena. A VxO is a virtual observatory that focuses on a particular domain such as solar, magnetospheric, or ITM. A VxO is a virtual observatory that focuses on a particular domain such as solar, magnetospheric, or ITM.

4. 05/23/2006 4SM23A-06 Virtual Observatory Concepts Discovery of Data “one-stop shopping” for the ITM community Browsing - more than data query, enhanced by use of meta-data Different views as data use differs between users Delivery of / access to readers, and other tools ITM is a heterogeneous data community with diverse file formats and granularity of observables Distributed Access Data providers have an integral role to provide data access, description, and meta-data Adaptable framework responsive to community needs and requests Comprehensive and historical

5. 05/23/2006 5SM23A-06 ITM is Diverse in Observables An ITM VO must facilitate correlative study between multiple parameters from multiple sources and cross-discipline studies.

6. 05/23/2006 6SM23A-06 ITM Domain Differences Data covering the ITM region has a number of differences from data in other domains. Data covering the ITM region has a number of differences from data in other domains. The ITM region is observed by ground based remote sensing instruments, satellite based remote sensing instruments, and in- situ satellite instruments. The ITM region is observed by ground based remote sensing instruments, satellite based remote sensing instruments, and in- situ satellite instruments. There are external drivers in solar radiation and the solar wind and magnetospheric particle inputs. There are external drivers in solar radiation and the solar wind and magnetospheric particle inputs. A Virtual Observatory that covers the ITM region needs to deal with the large diversity of data types in the study of this region. A Virtual Observatory that covers the ITM region needs to deal with the large diversity of data types in the study of this region. A heterogeneous data format environment also exists here. A heterogeneous data format environment also exists here. Solar imagery is typically in FITS files, ground based data are typically ASCII or NetCDF, in-situ satellite data are commonly in CDF format, remote sensing satellite data are typically HDF, NetCDF, or CDF. Solar imagery is typically in FITS files, ground based data are typically ASCII or NetCDF, in-situ satellite data are commonly in CDF format, remote sensing satellite data are typically HDF, NetCDF, or CDF. Non-NASA data sources (i.e. NSF, NOAA, DoD) are important contributors to the ITM domain. Non-NASA data sources (i.e. NSF, NOAA, DoD) are important contributors to the ITM domain.

7. 05/23/2006 7SM23A-06 The Ability to Find the Appropriate Data Will Be a Key Requirement of a VITMO Increasingly satellites in the ITM community will rely on remote sensing instruments – TIMED, DMSP, NPOESS

8. 05/23/2006 8SM23A-06 How the VITMO Will Be Different For a VxO to be truly useful it must go beyond traditional mission data centers. For a VxO to be truly useful it must go beyond traditional mission data centers. It should be able to interrogate multiple data centers, finding products that overlap in time and/or location. It should be able to interrogate multiple data centers, finding products that overlap in time and/or location. It must allow a higher level of search to be performed than previously available. An example of such a complex search would be something like the following: “what near Earth orbiters are observing the auroral region when Bz is negative and the solar wind speed is greater than 400 km / sec.?” Today, this would require a manual process where the researcher would first find plots of the Earth’s magnetic field component (Bz) and plots of solar wind speed from NASA’s CDAWeb (http://cdaweb.nasa.gsfc.gov). After they manually looked for overlap conditions they would utilize these time periods to search known Earth orbiters to find out if they were observing during those time periods. They would then have to manually review summary products to determine if those orbiters were viewing the auroral region during those intervals. VITMO will handle these types of queries automatically. It must allow a higher level of search to be performed than previously available. An example of such a complex search would be something like the following: “what near Earth orbiters are observing the auroral region when Bz is negative and the solar wind speed is greater than 400 km / sec.?” Today, this would require a manual process where the researcher would first find plots of the Earth’s magnetic field component (Bz) and plots of solar wind speed from NASA’s CDAWeb (http://cdaweb.nasa.gsfc.gov). After they manually looked for overlap conditions they would utilize these time periods to search known Earth orbiters to find out if they were observing during those time periods. They would then have to manually review summary products to determine if those orbiters were viewing the auroral region during those intervals. VITMO will handle these types of queries automatically. VITMO will also be able to organize tools, whether plotting, subsetting, or analysis tools by the type of data they are to be applied to as well as the types of operations that are to be performed. If the user requested time series data then tools appropriate for operating on the time series should be presented to them. If they chose images then tools appropriate for images should be made available. If the data were in CDF format, then FITS format data tools need not be presented. Additionally, VITMO will understand that model output can be treated as high level data products and that models should be available, just as tools are, to the end user. VITMO will also be able to organize tools, whether plotting, subsetting, or analysis tools by the type of data they are to be applied to as well as the types of operations that are to be performed. If the user requested time series data then tools appropriate for operating on the time series should be presented to them. If they chose images then tools appropriate for images should be made available. If the data were in CDF format, then FITS format data tools need not be presented. Additionally, VITMO will understand that model output can be treated as high level data products and that models should be available, just as tools are, to the end user.

9. 05/23/2006 9SM23A-06

10. 05/23/2006 10SM23A-06 VITMO Architecture Overview high-level conceptual model of the scientific disciplines and domain data in the region from the Sun to the Earth high-level conceptual model of the scientific disciplines and domain data in the region from the Sun to the Earth Regions – Solar, Near-Earth – F-region, E-region Regions – Solar, Near-Earth – F-region, E-region Features – exist for long periods of time Features – exist for long periods of time Events – Occur at particular points in time Events – Occur at particular points in time description of resource metadata in the Knowledge Base supporting any accessible scientific resource description of resource metadata in the Knowledge Base supporting any accessible scientific resource descriptive metadata includes program, satellite and instrument information, as well as data details like observed phenomena, units of measure, time and spatial coverage, processing level descriptive metadata includes program, satellite and instrument information, as well as data details like observed phenomena, units of measure, time and spatial coverage, processing level structural metadata defines the details for data access and retrieval if selected by the use structural metadata defines the details for data access and retrieval if selected by the use expand the notion of a data resource in our metadata definition to also include databases and computer programs (such as format conversion tools or data models) that generate requested data on demand. Thus, file access, database access, and program invocation details are all included in the resource metadata model. expand the notion of a data resource in our metadata definition to also include databases and computer programs (such as format conversion tools or data models) that generate requested data on demand. Thus, file access, database access, and program invocation details are all included in the resource metadata model.

11. 05/23/2006 11SM23A-06 Coupling in VITMO Architecture a loose coupling between the conceptual model and the resource metadata. a loose coupling between the conceptual model and the resource metadata. A query in the conceptual model identifies the desired parameters, which are then mapped to the related data resources. Data discovery is simplified by performing requests at the conceptual level, permitting users without knowledge of specific mission, or instrument to readily find data of interest. A query in the conceptual model identifies the desired parameters, which are then mapped to the related data resources. Data discovery is simplified by performing requests at the conceptual level, permitting users without knowledge of specific mission, or instrument to readily find data of interest. conceptual model can be readily expanded if new classes of scientific concepts or new relationships between scientific concept classes would improve the model's representation of the Sun to Earth domain. conceptual model can be readily expanded if new classes of scientific concepts or new relationships between scientific concept classes would improve the model's representation of the Sun to Earth domain. a data provider can add resources (of any format, access method, or processing level) simply by providing information that describes the data and its access details. a data provider can add resources (of any format, access method, or processing level) simply by providing information that describes the data and its access details. model eliminates the need for rigid metadata standards or unique, resource-specific integration software model eliminates the need for rigid metadata standards or unique, resource-specific integration software

12. 05/23/2006 12SM23A-06 VITMO Based on Services

13. 05/23/2006 13SM23A-06 Enhanced Search Services Today’s researcher needs to find datasets from different missions that overlap in time and space. Today’s researcher needs to find datasets from different missions that overlap in time and space. Many of today’s satellite missions include remote sensing instruments. Finding cross comparisons between instruments is more difficult because we must be concerned not just with spacecraft location, but also with multiple look-points for the instruments. Many of today’s satellite missions include remote sensing instruments. Finding cross comparisons between instruments is more difficult because we must be concerned not just with spacecraft location, but also with multiple look-points for the instruments. The prime NASA mission for study of the MLT region is the TIMED satellite. All of the instruments on this satellite are remote sensing instruments. Each remote sensing instrument has a different look geometry, some looking as far as 2000 km away from the satellite. The prime NASA mission for study of the MLT region is the TIMED satellite. All of the instruments on this satellite are remote sensing instruments. Each remote sensing instrument has a different look geometry, some looking as far as 2000 km away from the satellite.

14. 05/23/2006 14SM23A-06 Enhanced Search Services Continued AIM will have a fly’s eye imager. AIM will have a fly’s eye imager. The fields-of-view (regard) of these instruments define the extent of the data products. The fields-of-view (regard) of these instruments define the extent of the data products. This location information is not contained in the existing metadata in today’s catalogs. This location information is not contained in the existing metadata in today’s catalogs.

15. 05/23/2006 15SM23A-06 Virtual Metadata Generators VITMO will employ special coincidence calculators that know the viewing geometries of the various instruments. VITMO will employ special coincidence calculators that know the viewing geometries of the various instruments. The TIMED coincidence calculator is an example of a tool which when converted into a web service can be used to augment existing data querying. The TIMED coincidence calculator is an example of a tool which when converted into a web service can be used to augment existing data querying. The TIMED coincidence calculator has recently been imbedded into the TIMED query system. The TIMED coincidence calculator has recently been imbedded into the TIMED query system. These calculators will operate as web services inside the VITMO query system (or by outside query systems). These calculators will operate as web services inside the VITMO query system (or by outside query systems). These calculators generate metadata for the coincidence of interest which is used by the catalog system to answer your query. These calculators generate metadata for the coincidence of interest which is used by the catalog system to answer your query. The metadata generated by this service is discarded after it is used – hence “virtual metadata”. The metadata generated by this service is discarded after it is used – hence “virtual metadata”.

16. 05/23/2006 16SM23A-06 Enhanced Search Results Addition of virtual metadata capabilities allows pre-selection of data for analysis. Addition of virtual metadata capabilities allows pre-selection of data for analysis. VITMO search will deliver results from searches such as “What GUVI data that contains 23-01 MLT during active geomagnetic conditions?” VITMO search will deliver results from searches such as “What GUVI data that contains 23-01 MLT during active geomagnetic conditions?”

17. 05/23/2006 17SM23A-06 Prototype Display The left hand side of the web page allows the user to search or browse for resources (be they data, models, tools, etc) by the particular region of space, time, satellite coincidences or other criteria. After resources are searched for the results are displayed and possible display plots may be shown. If the user selects the “Related Tools and Models” button on the top of the page a display of appropriate tools and models for the data or resources chosen will be presented. This list of tools and models is generated dynamically based on the metadata in the VITMO catalog describing the data, tools, and models available through the VITMO.

18. 05/23/2006 18SM23A-06 Uniqueness of the Proposed Approach Unique search capabilities allowing complex overlapping searches to be performed, eliminating much of the tedious work of finding the appropriate data sets Unique search capabilities allowing complex overlapping searches to be performed, eliminating much of the tedious work of finding the appropriate data sets architecture that builds on the latest technologies like web services, but flexible enough that it can accommodate new technologies, not yet available architecture that builds on the latest technologies like web services, but flexible enough that it can accommodate new technologies, not yet available simplified metadata description that allows new data sets and data servers to be easily added simplified metadata description that allows new data sets and data servers to be easily added a design that incorporates more than data, that includes models and tools as well a design that incorporates more than data, that includes models and tools as well a design that builds on existing data centers rather than duplicating their functions a design that builds on existing data centers rather than duplicating their functions an approach that was designed for low long-term maintenance costs in the future an approach that was designed for low long-term maintenance costs in the future a team that represents the major future ITM satellite missions and the major ITM-related data centers and services a team that represents the major future ITM satellite missions and the major ITM-related data centers and services

19. 05/23/2006 19SM23A-06 Halloween 2003 Superstorm as an Example Increasingly satellites in the ITM community will rely on remote sensing instruments – TIMED, DMSP, NPOESS

20. 05/23/2006 20SM23A-06 What will VITMO mean to users? By taking on a more global view of data, VITMO can help the researcher understand the connectedness between the different data sets. By taking on a more global view of data, VITMO can help the researcher understand the connectedness between the different data sets. Example - storm studies - taking the Halloween 2003 superstorm as a case study. Example - storm studies - taking the Halloween 2003 superstorm as a case study. During this event the following things happened: (1) 3 active regions (ARs) produced a total of at least 124 soft X-ray flares and > 60 coronal mass ejections (CMEs) between Oct. 18 and Nov. 5, 2003. (2) 2 CMEs impacted Earth head-on producing huge geomagnetic storms. During this event the following things happened: (1) 3 active regions (ARs) produced a total of at least 124 soft X-ray flares and > 60 coronal mass ejections (CMEs) between Oct. 18 and Nov. 5, 2003. (2) 2 CMEs impacted Earth head-on producing huge geomagnetic storms. Today: Today: A researcher may start with: LASCO images and ACE solar wind data over the broad time range in October and November 2003. If the researcher was interested in auroral impacts from the storm they may have looked for time periods when Kp was between 3 and 9 and Bz was negative. A researcher may start with: LASCO images and ACE solar wind data over the broad time range in October and November 2003. If the researcher was interested in auroral impacts from the storm they may have looked for time periods when Kp was between 3 and 9 and Bz was negative. They might have looked for Polar and GUVI imagery or auroral conductivity maps during those time periods. They might have looked for Polar and GUVI imagery or auroral conductivity maps during those time periods. All of this work would have involved finding and navigating numerous web sites that provide data, indices, and other information for the specific time period. All of this work would have involved finding and navigating numerous web sites that provide data, indices, and other information for the specific time period. Tomorrow – with VITMO: Tomorrow – with VITMO: The researcher will be able to select the needed LASCO images and ACE solar wind data. The researcher will be able to select the needed LASCO images and ACE solar wind data. They will also be able to select auroral data during the time periods when Kp is between 3 and 9 and Bz was negative. They will also be able to select auroral data during the time periods when Kp is between 3 and 9 and Bz was negative. The VITMO, knowing that the researcher was interested in auroral data, would have automatically presented GUVI and Polar instrument products as appropriate. The VITMO, knowing that the researcher was interested in auroral data, would have automatically presented GUVI and Polar instrument products as appropriate. It would then suggest data readers, appropriate for the data format, and tools that the researcher could use to work with or analyze that data. It would then suggest data readers, appropriate for the data format, and tools that the researcher could use to work with or analyze that data.

21. 05/23/2006 21SM23A-06 Resources Supported TIMED, AIM, SNOE, C/NOFS, UARS, SuperDARN, DMSP TIMED, AIM, SNOE, C/NOFS, UARS, SuperDARN, DMSP CDAWeb, SSCWeb, ModelWeb, VSTO CDAWeb, SSCWeb, ModelWeb, VSTO Other VxO’s Other VxO’s NGDC NGDC