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  • 1. Service Oriented Atmospheric Radiances (SOAR): Gridding and Analysis PAPER IDENTIFICATION NUMBER: 1 Services for Multi-Sensor Aqua IR Radiance Data for Climate Studies M. Halem, N. Most, K. Stewart, Y. Yesha. D. Chapman, P. Nguyen, C. Tilmes, Index Terms—Data conversion, On-Demand Data processing, Abstract— The Aqua spacecraft, launched on May 4, 2002, service oriented computing, Web services, carries two well calibrated, independent IR grating spectrometers, AIRS and MODIS, which have been continuously returning upwelling IR spectral radiance I.INTRODUCTION T measurements for over five years. Based on an Aqua Sr. Project Review, estimates of available flight fuel, power and orbital his paper serves to explain our web-based IR radiance projections, assess the life span of the Aqua satellite and these information system and demonstrates how it provides two instruments to be reliable to 2013 [17]. Since launch, these thinning services to improve access and use of such space- instruments have generated petabytes of data, which are based observations in the areas of information management managed and made available by the GES DISC and GSFC and extraction, data reduction, on-demand search and access MODAPS. Agencies such as NOAA, DOD, EPA and USGS use and online processing, analysis and visualization. Service the AIRS data mostly for weather related applications while Oriented Architecture (SOA) has emerged among several MODIS data is used both for studies of weather, oceans and land processes, aerosols, natural and man made disasters and science disciplines and is often referred to as e-science [6] or Earth ecology in addition to some climate related studies. The service-oriented science [7]. SOA and specifically Web Science Investigator-led Processing Systems (SIPS) teams have Service technologies have found utility in the sciences and made many of the desired products derived from these data sets recently has been extended to include an underlying cyber- available either as level 2 products and/or level 3 gridded infrastructure (i.e. computing grids, data storage and product fields. However, no such gridded level 1B data sets are networks) for discovery of algorithms and their execution. In available directly from the SIPS. Thus, one impediment the general community faces in accessing these petabytes of this paper, we specifically address the gridding of level 1B radiance data is storing such large datasets, interpreting the atmospheric radiances, a computational challenging satellite multi formatted data and transforming it into helpful datasets data integration problem of high scientific relevance to for climate research needs. The Service Oriented Atmospheric understanding global climate change. Radiance (SOAR) system has been designed to bridge these gaps and overcome the challenges of bringing this rich data source to the science community, by delivering applications that II.THE SOAR GRIDDED DATA process these valuable radiance data into standard spatial- temporal grids as well as user-defined criteria on demand. SOAR can serve this community with aggregated, enriched and A.Satellite Sensor Operation thinned gridded data sets provided with access to the data on US polar orbiting Earth looking satellites from NASA, demand, with query and subsetting capabilities across many NOAA and the DOD have collected and archived petabytes dimensions. In addition, SOAR provides online user specified of data from operational and research satellites for over three visualization and analysis requests, all accessible via a Web decades. These data are stored at distributed archives in a browser. The utility of SOAR is exposed via Web Service routines, using the SOAP protocol. The Web Service library variety of formats and comprise one of the longest and supporting technologies (AXIS, PostgreSQL, Tomcat) continuous satellite climate data records available today. reside on a UMBC Client Server, which interfaces to and The SOAR system [19,20,21] has initially focused on the invokes algorithms on the Process Server, a high-performance AIRS, AMSU and MODIS sensors flying aboard the Aqua compute cluster and storage system. These servers are polar orbiting satellite but is applicable to the gridding of connected to the sensor data stores at GSFC via a high speed other level 1B instrument radiances as well. Both AIRS and fiber optic network connection[10Gb/s], providing reliable and MODIS measure the emitted radiation from the visible, near fast on-demand access to a vast on-line library of AIRS and current monthly MODIS source data. and infra-red spectral regions and AMSU measures the radiance in the microwave regions of the electro-magnetic spectrum. The Aqua satellite, orbiting at 700km, completes one polar orbit every 100 minutes. The two sensors of  Manuscript received January 31, 2008. This work was supported in part by the National Aeronautics and Space Administration ACCESS grant 06GG34A interest scan across the nadir track ata distance of 1200 km and University of Maryland Baltimore Campus (UMBC). providing additional spots of coverage along the satellite’s Dr. Milton Halem is a research professor with the Department of Computer trajectory, as shown in Fig. 1. This scanning thus allows Science and Electrical Engineering at the University of Maryland Baltimore Campus, Baltimore MD, and emeritus at NASA Goddard Space Flight Center, nearly twice daily coverage of the atmospheric radiances for phone: 410-455-2862 fax: 410-455-3969 e-mail: every spot on the Earth except for some gaps at the Equator, Mr. Neal Most is V.P. Operations with Innovim, a NASA contractor in Greenbelt, MD., e-mail: which are subsequently covered over a 3 day cycle. Mr. Kevin Stewart is a senior systems developer with Innovim, e-mail: Mr David Champan is a computer science graduate student at UMBC, e- B.Data Production mail: Ms. Phuong Nguyen is a computer science graduate student at UMBC, e- Satellite data collected from the AIRS and AMSU sensors mail: are available on disks on line at the Goddard Earth Science Mr. Curt Tilmes is with NASA Goddard Space Flight Center, Greenbelt, Data and Information Services Center (GES DISC). The MD 20771, USA e-mail: Dr. Yelena Yesha is a Verizon Professor with the Department of MODIS Level 1B thermal channels are kept on-line only for Computer Science and Electrical Engineering at UMBC, e-mail: a brief time period, for consumption through a web interface,
  • 2. PAPER IDENTIFICATION NUMBER: 2 after which they are removed. When a user orders Level 1B broader community of modelers and climatologists to readily data that is longer on-line, it is re-generated and placed investigate seasonal, annual and short term aspects of climate online for the user to download. This process typically spans variability that have so far been impractical with their several days. The approach SOAR takes eliminates this available resources. activity for the user. On-demand routines have been developed which transform the satellite atmospheric radiance L1b data into global, gridded arrays at 100km by 50km (1 x III.THE USER EXPERIENCE 0.5 degree) resolution. Gridded radiance fields greatly A unique contribution that SOAR offers users is the ability reduces the volume of data, often called thinning or to select subsets from pre-gridded global datasets spatially, reduction, by some form of statistical operation of binning temporally, by frequency and resolution. Fig. 3 depicts data into a grid box. On an ongoing basis, the gridding spatial subsetting. If the request cannot be satisfied with routines are invoked to build our online library of gridded gridded data staged on the SOAR server, the system will atmospheric radiance data. The process cycles automatically retrieve the L1b radiance data real-time from the satellite through each days worth of data. The process starts with archives and generate the requested gridded dataset on- retrieval of level 1b (L1b) radiance data from the AIRS and demand, using the same routines described above. MODIS data archives for a given day and aggregates each A client interface, as seen in Fig. 4, 5 & 6, was developed “granule” of data to produce a days worth of gridded data. with DHTM (PHP and Javascript), has been designed to The original data, which is either at 1 km resolution for assist with constructing and submitting requests and MODIS or 14 km resolution at nadir respectively for AIRS visualizing and analyzing results. These pages interact with and MODIS, is overlaid onto the grid, as depicted in Fig. 2. the web application via web service library calls. The user Each pixel of the level 1b data is geographically and starts by defining a query using the New Request panel, precisely mapped to an element in the grid. Values for each choosing: the target gridded dataset (MODIS or AIRS); the pixel are extracted from the L1b data and applied to the “mode” or gridded value of interest (avg, max, min radiance appropriate grid element. or BT); and the desired output (image or data file). The query also must include the subset criteria: frequency Four values and thus four separate datasets are produced range; temporal range and geographic extent. This query is from this extraction process per grid element: maximum stored in the system and queued for execution. Once radiance value, minimum radiance value, average radiance executed, it will extract from the target dataset according to value, computed brightness temperature. This process the given criteria and present the resulting subset in the reduces these global datasets by the magnitude of 3. Two user’s Results listing. years of AIRS L1b radiance data requires 40Tb of storage A variety of outputs are offered. Here we see the most space, where the equivalent SOAR dataset, gridded at 1 x 0.5 common type of results, a .jpg image constructed from a degree resolution requires only 40 GB. This gridded data is subset of the gridded AIRS data. stored online on the SOAR application server and provides an on-demand and reliable source of science data to the user Other output types are available: community. Table 1 shows the data produced and made • Gridded data subset in GeoTIFF format available to-date. • Animation of a multi-day composite images, one day per frame This process is well suited for parallel processing since • Plots of grid values and other analyses each days processing is independent of any other day. Thus • Anomoly image, where the resulting image is SOAR has been designed to run in parallel, with 1 day’s subtracted from the image representing the yearly of worth of data being processed per processor blade. A high- seasonal average performance IBM based power pc cluster with 32 dual processor blades and 14 quad processor blades residing at the Multcore Computational Center at University of Maryland IV.SOAR COMPUTING ARCHITECTURE Baltimore Campus (UMBC) [18] greatly decreases the processing time to grid and archive multi- year data records A.Principal Components of AIRS/AMSU and MODIS radiances. For example, to grid Figure 7 below illustrates the typical Service Oriented 6 days of AIRS data at 1 x 0.5 degree resolution takes 70 Architecture (SOA). It is comprised of three primary minutes on 12 processors and 90 minutes for MODIS data at subsystems: the Client Server, the Directory Server, and the the same resolution. To improve data download speeds Process Server. The underlying Physical Resource Layer between UMBC and the satellite data archives residing at represents the hardware and operating system that supply NASA Goddard Space Flight Center, a high-speed network computing resources to the SOA. has been installed between these two facilities, able to reach The Client Server represents the consumer(s) of the download speeds 440 Mbs. While the fiber optic speeds are published web services. There can be potentially many capable of greater speeds, the limiting factor is the speed of heterogeneous clients utilizing the same web services. One the network out of GSFC. significant advantage of SOA is that the service may be Clearly, the number of such products that can be produced implemented independent of knowing how it will be invoked, from all the combinations of spectral channels will enable the either by a user (e.g. web page) or computer-to-computer
  • 3. PAPER IDENTIFICATION NUMBER: 3 invocation. published into a file share on the same Tomcat instance and Often the web services are “advertised” in a Directory kept there long enough for the requester to retrieve them via Server where clients can discover the provided services and the returned URL. how to interface with them. Several (possibly competing) Currently SOAR does not publish its services to any service providers advertise their services with the Directory Directory Server. Directory Servers are often considered Server (via WSDL) and then clients can query the server (via optional components in SOA. Once the SOAR services are UDDI) at runtime to discover new services or determine the ready for public usage, a suitable Directory Server should be best provider for desired services. brought online. In the mean time, prospective clients and The Process Server is where the actual services (i.e. client developers will be able to access the WSDL published science algorithms and data access) function. These three directly from the Tomcat/Axis provider via the SOAR servers communicate through standards-based XML website. protocols, such as SOAP, WSDL and UDDI. By using these C.Web Mapping and Coverage Services communication standards, the Process Server is able to be developed independently from the eventual clients. The web SOAR employs a Web Mapping Service (WMS) and a services provided can be accessed independent of the Web Coverage Service (WCS) to provide the user with programming languages and operating systems involved. refined facility to adjust, visualize and enhance images of maps produced by SOAR. SOAR produces maps and data at B.Web Service Implementation varying resolutions, however, some users may wish to SOAR uses standards-based Web Mapping Services overlay geographic layers or political boundaries on the map, (WMS) and Web Coverage Services (WCS) to allow the user zoom in on some anomaly or dynamically resize maps to fit to fine- tune generated images and data files, respectively. their analysis needs. Implementations of the OGC standard As was mentioned, SOAR allows the user to request images specifications for Web Coverage Service (WCS) and Web and data files at varying resolutions. However, often this is Mapping Service (WMS) have been included in SOAR, not enough. Some users may need to do things like overlay provide improved map production and visualization features geographic or political boundaries, zoom in on some to the SOAR suite of services. Currently SOAR is using the anomaly or dynamically resize images to fit their needs. Minnesota Map Server as part of the client application. Standard WMS/WCS products take the images and data files D.System Process Workflow from the SOAR service and allow the user to interactively manipulate them in the above ways, and more. Currently Figure 10 shows a typical interaction between the various SOAR is using the Minnesota Map Server as part of the subsystems of SOAR when a user requests new radiance client application. data, in this case as an image. The user interacts with the For SOAR, the traditional SOA is extended slightly, (see system through a standard web browser calling the relevant Fig 8). SOAR provides a web-based client (implemented in web pages on the client server. The user supplies the client PHP on an Apache Web server) that allows the user to server with their authentication information. The client then interact through their web browser. The client takes user logs into the SOAR web services receiving a short-lived web interactions and translates them into web service calls, session key. This session key is then retained by the client executes them via SOAP and encodes the results to be and passed back to the web services with every call to displayed to the user in HTML. Once a user has an approved provide security and transaction demarcation. Once account, they can freely request and access SOAR provided successfully logged in, the user navigates to the new request atmospheric data. form on the client and fills in the necessary information and The requests and responses between the client and web submits it. The client then validates the form and submits it services use the SOAP/XML protocol. However, to the web services for processing. An initial result is SOAP/XML is not an efficient or reliable method for returned to the client and then displayed to the user letting transporting large binary data objects, so the actual data and them know that the request is being processed. The web image files are stored in a publicly accessible file share with services then interact with the Bluegrit Supercomputer only the URL passed back in the SOAP response. Since Cluster to executing the science data algorithms and producing these data files can be very computationally ultimately produce one or more data files and science intensive, the SOAR web services make use of the computing images. These and the completion status of the result are power of the UMBC Bluegrit Supercomputer Cluster [x2] for recorded. Sometime later, after being notified via email, the much of the data processing. Interactions between the user navigates to the client display results page for the principal components for login, new request and get results completed request ID. The client gets the request status from service calls may be seen in Fig. 9. the web services, including the URL(s) for all produced files. The SOAR process server is an instance of Tomcat that These are then displayed to the user. If the results are a can be run on the same physical server or a remote one. The single image, that image is displayed. If the results are web services are implemented predominately in Java using multiple images, those images can be displayed as an the Apache Axis library for SOAP/WSDL protocols and a animation. If the results include data files, then all URL(s) Postgres database for persistence. The underlying data are displayed so the user can then click on them to begin processing utilities run in Unix/Linux C/C++ for speed on the downloading them locally. This is necessary to eventually host system. The resulting images and data files are then free up the file space. Fig. 10 illustrates the internal components of the SOAR
  • 4. PAPER IDENTIFICATION NUMBER: 4 web services that comprise the Process Server. The SOAR radiance data sets have identified and tracked cases of the Web Services Application provides interfaces between the Madden Julian Oscillation in the December/January 2007 application server and the subsystems, allowing them to be time period. reusable and potentially distributed to multiple hosts. The client server communicates with the SOAR web services via SOAP. The Axis package provides the protocol VI.CONCLUSION support and the web service interface handles all request and In this paper, we have described a web-based system data type translation necessary, as well as providing SOAR for gridding IR radiance data sets that reduces the parameter validation and error handling. volume of data by three orders of magnitude. We further The workflow/task scheduler is then responsible for showed that Aqua has two sensors, one with hyper spectral breaking down requests into sub-tasks and then executing resolution (AIRS) and the other with hyper spatial thermal them in order via the utility/gridding manager. The resolution (MODIS), which independently have been workflow is also responsible for checking to see if incoming measuring the OLR over the life of the Aqua satellite, now requests have already been served to other users. If the data five plus years. We have described the software basis for has already been generated, the results are served up to the developing a system to enable users to transparently access, new user, thus saving processing time, which can be large manipulate and perform analytical climate studies with the due to the huge data volumes. services available from this system. Moreover, we have Finally, the Utility/Gridding Interface Manager handles all indicated how this system can be employed to conduct a of the complexity of interfacing with the Bluegrit scheduling variety of climate studies with gridded radiance data sets system (PBS/Torque) and all of the data processing utilities. whose results can be independently validated by comparing Most of these function quickly, typically in less than 30 the observed spectral radiances of one system with the seconds. However, some functions require accessing gridded radiances of the other. external servers for data and then transporting and processing terabytes worth of data so they could take hours or even days to complete. The Bluegrit scheduling system handles the ACKNOWLEDGMENT scheduling of parallel processing routines. The utility manager is then responsible for retrieving the results from the The authors would like to thank Dr. Goldberg, Chief of the Bluegrit scheduler and incorporating the results back into the Climate Research and Applications Division of SOAR database through a standard Data Access Object NESDIS/NOAA for his contributions of atmospheric (DAO) and the accessible file share. radiance gridding algorithms and contract support in developing gridding extensions. We also thank Dr. Wenli Yang, Associate Director of the Center for Spatial Information Science and System (CSISS) at George Mason V.CLIMATE APPLICATIONS University, for his technical support of a Web Coverage Service (WCS) he provided the project. In addition, we want Microwave radiance data sets obtained from operational to acknowledge the contributions of Dr. Jeffrey de la MSU/AMSU sensors on multiple NOAA weather satellites Beaujardiere, currently on the NOAA Integrated Ocean have been used to directly infer the inter-annual global mean Observing System project, formerly of NASA Goddard Space tropospheric warming.[16] Difficulties in calibrating such Flight Center, for assisting with development and integration measurements across multiple satellites with orbital drifts of a Web Mapping Service (WMS). Finally, we thank the and multiple sensors with degrading performance have led to MODIS and AIRS teams for the information on the several reanalysis of these data to correct for these factors. calibration process. The Aqua satellite with two independent sensors and both systems on the same satellite platform obviates many of these REFERENCES factors. In addition, precise measurements of the Aqua [1] Barry, D.K.: Web Services and Service oriented architectures; The spacecraft have enabled the project to maintain Aqua at a Savvy Managers Guide. Morgan Kaufman Publishers, 2003. constant altitude for more than five years. As a result, we [2] Xerox Enhances productivity with IBM Service Bus Solution and illustrate how these gridded radiances can be used for climate Service Oriented Architecture. October 28, 2005, studies. We have performed a three year average of monthly 2?OpenDocument& Site=wp AIRS data for 2005, 2006 and 2007 for the month of [3] Ensuring the Competitive Edge; Transamerica Life Insurance Company February and compared the differences of any month mean Simplifies Infrastructure with Sun Software, from the three year monthly average. Fig. 11 shows the three [4] Berger, A.K.: Getting an E-Biz Up and Keeping it Running.May 17, year mean monthly radiances for an AIRS window channel 2006 528 and the respective monthly anomalies for the three [5] Sullivan, J.O., D.Edmond, A.H.M. ter Hofstede.: The price of Services.ICSOC- 2005. Proceedings 3rd International Conference months. The results strikingly show the year to year Amsterdam, Netherlands. variances from cold radiances (presumably related to cold [6] Hey, T. A. Trefethen.; Cyberinfrastructure for e-Science. Science, 2005, air) in Feb. 2005 to warm radiances ( i.e. warm air masses) in Vol.308 pp. 817-821. [7] Foster, I.: Service-Oriented Scienc. Science 2005, Vol. 308, pp 814-817 Feb. 2007 in the western Pacific. Similar differences of [8] Geosciences Network opposite sign from warm to cold can be seen in the Indian [9] Network for Earthquake Engineering Ocean. Other climate studies using just the gridded IR
  • 5. PAPER IDENTIFICATION NUMBER: 5 [10] Laser Interferometer Gravitational-Wave Observatory became nationally recognized for Atmospheric and Climate Modeling under his leadership. His personal achievements include more than 100 scientific [11] NRC of National Academies of Science.: Climate Data Records publications in the areas of atmospheric and oceanographic sciences and fromEnvironmental Sciences, National Academies Press 2004 computational and information sciences. He is most noted for his [12] Chahine, M From JPLAIRS Image Archive June 2003, groundbreaking research in simulation studies of space observing systems and for development of four dimensional data assimilation for weather and [13] GrADS Home Page.: Center for Ocean-Land-AtmosphereStudies. 25 climate prediction. Over the years, his achievements have earned him Apr 2006, numerous awards including the NASA Medal for Exceptional Scientific [14] Goldberg,M.D., Y. Qu, L. M. McMillin, W. Wolf, L. Zhou, and M. Achievement, the NASA Medal for Outstanding Leadership, and NASA’s Divarkarla, AIRS near-real-time products and algorithms in support of highest award, the NASA Distinguished Service Medal in 1996. In 1999, Dr. operational numerical weather prediction, IEEE Trans. Geosci. Remote Halem was awarded the honorary Doctor of Law degree from Dalhousie Sensing, vol. 41, pp. 379-389, Feb. 2003. University, in recognition for his contributions to the field of computational [15] SOAR Home Page science. He is also a noted Fine Arts screenprint maker of space images. [16] 1990. Precise Monitoring of Global Temperature Trends from Satellites*. Roy W. Spencer and John R. Christy, Marshall Space Flight Center, Johnson Research Center, Science, 30 March 1990:Vol. 247. no. 4950, pp. 1558 – 1562 [17] Parkinson, C. L.; Platnick, S. E.; Chahine, M. T.; Salomonson,  V. V.; Shibata, A.; Spencer, R.; Wielicki, B.; Gainsborough, J.;  and Graham, S. M., “Aqua Senior Review Proposal,” NASA  GSFC, Greenbelt, MD, pp.84, 2007 [18] Multcore Computational Center at University of Maryland Baltimore Campus (UMBC) [19] Halem M.; Yesha Y.; Tilmes C.; Goldberg M.; Shen S.; Zhou L.           H., “Service Oriented Atmospheric Radiances (SOAR): A           Community Research Tool for the Synthesis of Multi­Sensor           Satellite Radiance Data for Weather and Climate Studies,”          Proceedings of the 3rd International Conference on Web         Information Systems and Technology, Barcelona, Spain,         March 3­6, 2007 [20] Halem, M.;Tilmes, C.; Yesha, Y.; Chapman D.; Goldberg M.;           Zhou L., “A Web Service Tool for the Dynamic Generation of           L1Grids of Coincident AIRS, MODIS, and AMSU Satellite          Sounding Data for Climate Studies”, Proceedings of the           American Geophysical Union, Acapulco, Mexico, May 22­           25, 2007 [21] Halem, M.; Yesha, Y.; Tilmes, C.; Chapman, D.; Nguyen,  P.; de La Beaujardiere, J.; Most, N.; Stewart, K.; Bertolli A.,  “SOAR: A System for the Analysis of Atmospheric Radiances,”  Proceedings of the American Geophysical Union, San  Francisco, US Dec. 2007 Dr. Milton Halem acquired his Bachelor's degree in mathematics from the City College of New York, New York City, NY, USA in 1951 and a Ph.D. in mathematics from the Courant Institute of Mathematical Sciences, New York University, New York City, NY, USA in in 1968. He is a Research Professor in the Computer Science and Electrical Engineering Department and Executive Director, Multicore Computational Center of the College of Engineering and Information Technology at the University of Maryland, Baltimore County. His main areas of research interest are computational science, service oriented scientific computing and science information systems, data intensive computing and permanent digital data preservation. In addition, he also holds an Emeritus position as Distinguished Information Scientist in the Earth Sciences Directorate at the NASA Goddard Space Flight Center. Prior to retiring in 2002, he served in the Office of the Director from 1999 to 2002 in the joint capacity as Assistant Director for Information Sciences and Chief Information Officer for the NASA Goddard Space Flight Center, where he provided the strategic information science and technology focus and oversight across the entire Center. In this capacity, he represented Information Sciences at all management and flight mission critical programs and projects at the Center. Prior to this position, he served as Chief of the Earth and Space Data Computing from 1984 to 1999 and was responsible for the management and conduct of one of the world’s most powerful scientific data intensive supercomputing complexes. Under his leadership, the Division became nationally recognized for its research in high performance computing and modeling, advanced information data systems, scientific visualization, and massive data storage management. Dr. Halem headed the Goddard Global Modeling and Simulation Branch soon after joining NASA in 1971 as the GARP Project Scientist. His branch
  • 6. PAPER IDENTIFICATION NUMBER: 6 Fig. 3 Spatial subsetting and monthly averaging of an AIRS window channel data Fig. 1 Cross track scan fov patterns and overlap for two atmospheric radiances instruments, AIRS and AMSU currently flying on the NASA research satellite AQUA. Courtesy of M. Goldberg, NOAA. Level 1b ƒ° I D G R Fig. 4 SOAR request screen Fig. 2 Reduction of satellite imagery in km resolution to a gridded dataset of courser resolution. Table 1 SOAR online gridded data library, as of February 2008 Date Range Spectral Range Intervals Native Grid Resolution Maximum Radiance StorageAverage of Data, Gb) Radiance (Month Radiance Brightness Temperature Maximum Nov '04 - Jun '05; 1° ξ 0.5 ° MODIS Oct - Dec '07 16 channels daily (100 ξ 50 Κµ ) √ √ √ √ 324 οπερατιοναλ 1° ξ 0.5 ° ΑΙΡΣ Νοϖ ∋04 − ∆εχ ∋07 φρεθυενχιεσ δαιλψ (100 ξ 50 Κµ ) √ √ √ √ Table 2 SOAR data size and processing times to grid atmospheric radiance measurements. Fig. 5 SOAR stored results listing Original Data (Mb) Result (Mb) Time (min) Total Time (min) Gridded Download Processing Time (min) MODIS 38,502 67 13.01 2.2 14.38 AIRS 4,000 40 23.3
  • 7. PAPER IDENTIFICATION NUMBER: 7 Fig. 9 SOAR subsystem interaction diagram. User Client Web Service Bluegrit Get login page Login Page (HTML) Submit login login() Session Key Get user results() :sessionKey Result List Welcome Page/Recent Results New Request New Request Form Submit New Request Form Get radiance data() :sessionKey Request Status :requestID Request Status Page :requestID Get raw data Raw Data File Handle Subset/Average Data Condensed Data File Handle Render Data as Image Science Image File Fig. 6 SOAR map image result Get Request Results Get Results(requested) :sessionKey Set Status Request Results Results Display File URL(s) Image/Animation/Data URL(s) Server Context/Resources SOAR Web Services Application SOAP Web Service Workflow/ Utility/Gridding Bluegrit Interface Task Interface (Using Axis) Scheduling Manager Torque/PBS Fig. 7 SOA architecture block diagram Database (Postgres) Web Service Client UDDI Web Service Provider Directory Web Service Lookup Publish Services WSDL Fig. 10 SOAR Process server block diagram HTML SOAP SOAR Bluegrit User SOAR Web (Browser) Supercomputer HTTP Client URLS Services Cluster File Share (Binary Data/Images) Fig. 8 SOAR architecture block diagram Fig. 11 Gridded monthly mean AIRS radiance data