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Abstract— The Aqua spacecraft, launched on May 4, 2002, carries two well calibrated, independent IR grating spectrometers, AIRS and MODIS, which have been continuously returning upwelling IR spectral radiance measurements for over five years. Based on an Aqua Sr. Project Review, estimates of available flight fuel, power and orbital projections, assess the life span of the Aqua satellite and these two instruments to be reliable to 2013 [17]. Since launch, these instruments have generated petabytes of data, which are managed and made available by the GES DISC and GSFC MODAPS. Agencies such as NOAA, DOD, EPA and USGS use the AIRS data mostly for weather related applications while MODIS data is used both for studies of weather, oceans and land processes, aerosols, natural and man made disasters and Earth ecology in addition to some climate related studies. The Science Investigator-led Processing Systems (SIPS) teams have made many of the desired products derived from these data sets available either as level 2 products and/or level 3 gridded product fields. However, no such gridded level 1B data sets are available directly from the SIPS. Thus, one impediment the general community faces in accessing these petabytes of radiance data is storing such large datasets, interpreting the multi formatted data and transforming it into helpful datasets for climate research needs. The Service Oriented Atmospheric 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 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 thinned gridded data sets provided with access to the data on demand, with query and subsetting capabilities across many dimensions. In addition, SOAR provides online user specified visualization and analysis requests, all accessible via a Web browser. The utility of SOAR is exposed via Web Service routines, using the SOAP protocol. The Web Service library and supporting technologies (AXIS, PostgreSQL, Tomcat) reside on a UMBC Client Server, which interfaces to and invokes algorithms on the Process Server, a high-performance compute cluster and storage system. These servers are connected to the sensor data stores at GSFC via a high speed fiber optic network connection[10Gb/s], providing reliable and fast on-demand access to a vast on-line library of AIRS and current monthly MODIS source data.

Manuscript received January 31, 2008. This work was supported in part by the National Aeronautics and Space Administration ACCESS grant 06GG34A and University of Maryland Baltimore Campus (UMBC).

Dr. Milton Halem is a research professor with the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore Campus, Baltimore MD, and emeritus at NASA Goddard Space Flight Center, phone: 410-455-2862 fax: 410-455-3969 e-mail: halem@umbc.edu.

Mr. Neal Most is V.P. Operations with Innovim, a NASA contractor in Greenbelt, MD., e-mail: nmost@innovim.com.

Mr. Kevin Stewart is a senior systems developer with Innovim, e-mail: kstewart@innovim.com.

Mr David Champan is a computer science graduate student at UMBC, e-mail: dchapm2@umbc.edu.

Ms. Phuong Nguyen is a computer science graduate student at UMBC, e-mail: phuong3@umbc.edu.

Mr. Curt Tilmes is with NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA e-mail: curt.tilmes@nasa.gov.

Dr. Yelena Yesha is a Verizon Professor with the Department of Computer Science and Electrical Engineering at UMBC, e-mail: yeyesha@umbc.edu.

Index Terms—Data conversion, On-Demand Data processing, service oriented computing, Web services,

I. INTRODUCTION

his paper serves to explain our web-based IR radiance information system and demonstrates how it provides

thinning services to improve access and use of such space-based observations in the areas of information management and extraction, data reduction, on-demand search and access and online processing, analysis and visualization. Service Oriented Architecture (SOA) has emerged among several science disciplines and is often referred to as e-science [6] or service-oriented science [7]. SOA and specifically Web Service technologies have found utility in the sciences and recently has been extended to include an underlying cyber-infrastructure (i.e. computing grids, data storage and networks) for discovery of algorithms and their execution. In this paper, we specifically address the gridding of level 1B atmospheric radiances, a computational challenging satellite data integration problem of high scientific relevance to understanding global climate change.

T

II.THE SOAR GRIDDED DATA

A. Satellite Sensor Operation

US polar orbiting Earth looking satellites from NASA, NOAA and the DOD have collected and archived petabytes of data from operational and research satellites for over three decades. These data are stored at distributed archives in a variety of formats and comprise one of the longest continuous satellite climate data records available today. The SOAR system [19,20,21] has initially focused on the AIRS, AMSU and MODIS sensors flying aboard the Aqua polar orbiting satellite but is applicable to the gridding of other level 1B instrument radiances as well. Both AIRS and MODIS measure the emitted radiation from the visible, near 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 interest scan across the nadir track ata distance of 1200 km providing additional spots of coverage along the satellite’s trajectory, as shown in Fig. 1. This scanning thus allows nearly twice daily coverage of the atmospheric radiances for every spot on the Earth except for some gaps at the Equator, which are subsequently covered over a 3 day cycle.

B. Data Production

Satellite data collected from the AIRS and AMSU sensors are available on disks on line at the Goddard Earth Science Data and Information Services Center (GES DISC). The

Service Oriented Atmospheric Radiances (SOAR): Gridding and Analysis Services for Multi-Sensor Aqua IR

Radiance Data for Climate StudiesM. Halem, N. Most, K. Stewart, Y. Yesha. D. Chapman, P. Nguyen, C. Tilmes,

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MODIS Level 1B thermal channels are kept on-line only for a brief time period, for consumption through a web interface, after which they are removed. When a user orders Level 1B data that is longer on-line, it is re-generated and placed online for the user to download. This process typically spans several days. The approach SOAR takes eliminates this 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 0.5 degree) resolution. Gridded radiance fields greatly reduces the volume of data, often called thinning or reduction, by some form of statistical operation of binning data into a grid box. On an ongoing basis, the gridding routines are invoked to build our online library of gridded atmospheric radiance data. The process cycles automatically through each days worth of data. The process starts with retrieval of level 1b (L1b) radiance data from the AIRS and MODIS data archives for a given day and aggregates each “granule” of data to produce a days worth of gridded data. The original data, which is either at 1 km resolution for MODIS or 14 km resolution at nadir respectively for AIRS and MODIS, is overlaid onto the grid, as depicted in Fig. 2. Each pixel of the level 1b data is geographically and precisely mapped to an element in the grid. Values for each pixel are extracted from the L1b data and applied to the appropriate grid element.

Four values and thus four separate datasets are produced from this extraction process per grid element: maximum radiance value, minimum radiance value, average radiance value, computed brightness temperature. This process reduces these global datasets by the magnitude of 3. Two years of AIRS L1b radiance data requires 40Tb of storage space, where the equivalent SOAR dataset, gridded at 1 x 0.5 degree resolution requires only 40 GB. This gridded data is stored online on the SOAR application server and provides an on-demand and reliable source of science data to the user community. Table 1 shows the data produced and made available to-date.

This process is well suited for parallel processing since each days processing is independent of any other day. Thus SOAR has been designed to run in parallel, with 1 day’s worth of data being processed per processor blade. A high-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 Baltimore Campus (UMBC) [18] greatly decreases the processing time to grid and archive multi- year data records of AIRS/AMSU and MODIS radiances. For example, to grid 6 days of AIRS data at 1 x 0.5 degree resolution takes 70 minutes on 12 processors and 90 minutes for MODIS data at the same resolution. To improve data download speeds between UMBC and the satellite data archives residing at NASA Goddard Space Flight Center, a high-speed network has been installed between these two facilities, able to reach download speeds 440 Mbs. While the fiber optic speeds are

capable of greater speeds, the limiting factor is the speed of the network out of GSFC.

Clearly, the number of such products that can be produced from all the combinations of spectral channels will enable the broader community of modelers and climatologists to readily investigate seasonal, annual and short term aspects of climate variability that have so far been impractical with their available resources.

III. THE USER EXPERIENCE

A unique contribution that SOAR offers users is the ability to select subsets from pre-gridded global datasets spatially, temporally, by frequency and resolution. Fig. 3 depicts spatial subsetting. If the request cannot be satisfied with gridded data staged on the SOAR server, the system will retrieve the L1b radiance data real-time from the satellite archives and generate the requested gridded dataset on-demand, using the same routines described above.

A client interface, as seen in Fig. 4, 5 & 6, was developed with DHTM (PHP and Javascript), has been designed to assist with constructing and submitting requests and visualizing and analyzing results. These pages interact with the web application via web service library calls. The user starts by defining a query using the New Request panel, choosing: the target gridded dataset (MODIS or AIRS); the “mode” or gridded value of interest (avg, max, min radiance or BT); and the desired output (image or data file).

The query also must include the subset criteria: frequency range; temporal range and geographic extent. This query is stored in the system and queued for execution. Once executed, it will extract from the target dataset according to the given criteria and present the resulting subset in the user’s Results listing.

A variety of outputs are offered. Here we see the most common type of results, a .jpg image constructed from a subset of the gridded AIRS data.

Other output types are available: Gridded data subset in GeoTIFF format Animation of a multi-day composite images, one

day per frame Plots of grid values and other analyses Anomoly image, where the resulting image is

subtracted from the image representing the yearly of seasonal average

IV. SOAR COMPUTING ARCHITECTURE

A. Principal Components

Figure 7 below illustrates the typical Service Oriented Architecture (SOA). It is comprised of three primary subsystems: the Client Server, the Directory Server, and the Process Server. The underlying Physical Resource Layer represents the hardware and operating system that supply computing resources to the SOA.

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The Client Server represents the consumer(s) of the published web services. There can be potentially many heterogeneous clients utilizing the same web services. One significant advantage of SOA is that the service may be implemented independent of knowing how it will be invoked, either by a user (e.g. web page) or computer-to-computer invocation.

Often the web services are “advertised” in a Directory Server where clients can discover the provided services and how to interface with them. Several (possibly competing) service providers advertise their services with the Directory Server (via WSDL) and then clients can query the server (via UDDI) at runtime to discover new services or determine the best provider for desired services.

The Process Server is where the actual services (i.e. science algorithms and data access) function. These three servers communicate through standards-based XML protocols, such as SOAP, WSDL and UDDI. By using these communication standards, the Process Server is able to be developed independently from the eventual clients. The web services provided can be accessed independent of the programming languages and operating systems involved.

B. Web Service Implementation

SOAR uses standards-based Web Mapping Services (WMS) and Web Coverage Services (WCS) to allow the user to fine- tune generated images and data files, respectively.   As was mentioned, SOAR allows the user to request images and data files at varying resolutions.  However, often this is not enough.  Some users may need to do things like overlay geographic or political boundaries, zoom in on some anomaly or dynamically resize images to fit their needs.  Standard WMS/WCS products take the images and data files from the SOAR service and allow the user to interactively manipulate them in the above ways, and more.  Currently SOAR is using the Minnesota Map Server as part of the client application.

For SOAR, the traditional SOA is extended slightly, (see Fig 8). SOAR provides a web-based client (implemented in PHP on an Apache Web server) that allows the user to interact through their web browser. The client takes user web interactions and translates them into web service calls, executes them via SOAP and encodes the results to be displayed to the user in HTML. Once a user has an approved account, they can freely request and access SOAR provided atmospheric data.

The requests and responses between the client and web services use the SOAP/XML protocol. However, SOAP/XML is not an efficient or reliable method for transporting large binary data objects, so the actual data and image files are stored in a publicly accessible file share with only the URL passed back in the SOAP response. Since producing these data files can be very computationally intensive, the SOAR web services make use of the computing power of the UMBC Bluegrit Supercomputer Cluster [x2] for much of the data processing. Interactions between the principal components for login, new request and get results

service calls may be seen in Fig. 9.The SOAR process server is an instance of Tomcat that

can be run on the same physical server or a remote one. The web services are implemented predominately in Java using the Apache Axis library for SOAP/WSDL protocols and a Postgres database for persistence. The underlying data processing utilities run in Unix/Linux C/C++ for speed on the host system. The resulting images and data files are then published into a file share on the same Tomcat instance and kept there long enough for the requester to retrieve them via the returned URL.

Currently SOAR does not publish its services to any Directory Server. Directory Servers are often considered optional components in SOA. Once the SOAR services are ready for public usage, a suitable Directory Server should be brought online. In the mean time, prospective clients and client developers will be able to access the WSDL published directly from the Tomcat/Axis provider via the SOAR website.

C. Web Mapping and Coverage Services

SOAR employs a Web Mapping Service (WMS) and a Web Coverage Service (WCS) to provide the user with refined facility to adjust, visualize and enhance images of maps produced by SOAR.  SOAR produces maps and data at varying resolutions, however, some users may wish to overlay geographic layers or political boundaries on the map, zoom in on some anomaly or dynamically resize maps to fit their analysis needs.  Implementations of the OGC standard specifications for Web Coverage Service (WCS) and Web Mapping Service (WMS) have been included in SOAR, provide improved map production and visualization features to the SOAR suite of services. Currently SOAR is using the Minnesota Map Server as part of the client application.

D. System Process Workflow

Figure 10 shows a typical interaction between the various subsystems of SOAR when a user requests new radiance data, in this case as an image. The user interacts with the system through a standard web browser calling the relevant web pages on the client server. The user supplies the client server with their authentication information. The client then logs into the SOAR web services receiving a short-lived session key. This session key is then retained by the client and passed back to the web services with every call to provide security and transaction demarcation. Once successfully logged in, the user navigates to the new request form on the client and fills in the necessary information and submits it. The client then validates the form and submits it to the web services for processing. An initial result is returned to the client and then displayed to the user letting them know that the request is being processed. The web services then interact with the Bluegrit Supercomputer Cluster to executing the science data algorithms and ultimately produce one or more data files and science images. These and the completion status of the result are recorded. Sometime later, after being notified via email, the user navigates to the client

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display results page for the completed request ID. The client gets the request status from the web services, including the URL(s) for all produced files. These are then displayed to the user. If the results are a single image, that image is displayed. If the results are multiple images, those images can be displayed as an animation. If the results include data files, then all URL(s) are displayed so the user can then click on them to begin downloading them locally. This is necessary to eventually free up the file space.

Fig. 10 illustrates the internal components of the SOAR web services that comprise the Process Server. The SOAR Web Services Application provides interfaces between the application server and the subsystems, allowing them to be reusable and potentially distributed to multiple hosts.

The client server communicates with the SOAR web services via SOAP. The Axis package provides the protocol support and the web service interface handles all request and data type translation necessary, as well as providing parameter validation and error handling.

The workflow/task scheduler is then responsible for breaking down requests into sub-tasks and then executing them in order via the utility/gridding manager. The workflow is also responsible for checking to see if incoming requests have already been served to other users. If the data has already been generated, the results are served up to the new user, thus saving processing time, which can be large due to the huge data volumes.

Finally, the Utility/Gridding Interface Manager handles all of the complexity of interfacing with the Bluegrit scheduling system (PBS/Torque) and all of the data processing utilities. Most of these function quickly, typically in less than 30 seconds. However, some functions require accessing 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 scheduling of parallel processing routines. The utility manager is then responsible for retrieving the results from the Bluegrit scheduler and incorporating the results back into the SOAR database through a standard Data Access Object (DAO) and the accessible file share.

V. CLIMATE APPLICATIONS

Microwave radiance data sets obtained from operational MSU/AMSU sensors on multiple NOAA weather satellites have been used to directly infer the inter-annual global mean tropospheric warming.[16] Difficulties in calibrating such measurements across multiple satellites with orbital drifts and multiple sensors with degrading performance have led to several reanalysis of these data to correct for these factors. The Aqua satellite with two independent sensors and both systems on the same satellite platform obviates many of these factors. In addition, precise measurements of the Aqua spacecraft have enabled the project to maintain Aqua at a constant altitude for more than five years. As a result, we

illustrate how these gridded radiances can be used for climate studies. We have performed a three year average of monthly AIRS data for 2005, 2006 and 2007 for the month of February and compared the differences of any month mean from the three year monthly average. Fig. 11 shows the three year mean monthly radiances for an AIRS window channel 528 and the respective monthly anomalies for the three months. The results strikingly show the year to year variances from cold radiances (presumably related to cold air) in Feb. 2005 to warm radiances ( i.e. warm air masses) in Feb. 2007 in the western Pacific. Similar differences of opposite sign from warm to cold can be seen in the Indian Ocean. Other climate studies using just the gridded IR radiance data sets have identified and tracked cases of the Madden Julian Oscillation in the December/January 2007 time period.

VI. CONCLUSION

In this paper, we have described a web-based system SOAR for gridding IR radiance data sets that reduces the volume of data by three orders of magnitude. We further showed that Aqua has two sensors, one with hyper spectral resolution (AIRS) and the other with hyper spatial thermal resolution (MODIS), which independently have been measuring the OLR over the life of the Aqua satellite, now five plus years. We have described the software basis for developing a system to enable users to transparently access, manipulate and perform analytical climate studies with the services available from this system. Moreover, we have indicated how this system can be employed to conduct a variety of climate studies with gridded radiance data sets whose results can be independently validated by comparing the observed spectral radiances of one system with the gridded radiances of the other.

ACKNOWLEDGMENT

The authors would like to thank Dr. Goldberg, Chief of the Climate Research and Applications Division of NESDIS/NOAA for his contributions of atmospheric 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 University, for his technical support of a Web Coverage Service (WCS) he provided the project. In addition, we want to acknowledge the contributions of Dr. Jeffrey de la Beaujardiere, currently on the NOAA Integrated Ocean Observing System project, formerly of NASA Goddard Space Flight Center, for assisting with development and integration of a Web Mapping Service (WMS). Finally, we thank the MODIS and AIRS teams for the information on the calibration process.

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REFERENCES

[1] Barry, D.K.: Web Services and Service oriented architectures; The Savvy Managers Guide. Morgan Kaufman Publishers, 2003.

[2] Xerox Enhances productivity with IBM Service Bus Solution and Service Oriented Architecture. October 28, 2005, http://www-306.ibm.com/software/success/cssdb.nsf/CS/SPAT-6FCQB2?OpenDocument& Site=wp

[3] Ensuring the Competitive Edge; Transamerica Life Insurance Company Simplifies Infrastructure with Sun Software, http://www.sun.com/products/soa/success.jsp#1

[4] Berger, A.K.: Getting an E-Biz Up and Keeping it Running.May 17, 2006 http://www.EcommerceTimes.com

[5] Sullivan, J.O., D.Edmond, A.H.M. ter Hofstede.: The price of Services.ICSOC- 2005. Proceedings 3rd International Conference Amsterdam, Netherlands.

[6] Hey, T. A. Trefethen.; Cyberinfrastructure for e-Science. Science, 2005, Vol.308 pp. 817-821.

[7] Foster, I.: Service-Oriented Scienc. Science 2005, Vol. 308, pp 814-817[8] Geosciences Network http://www.geongrid.org/[9] Network for Earthquake Engineering http://it.nees.org[10] Laser Interferometer Gravitational-Wave Observatory

http://www.ligo.caltech.edu/[11] NRC of National Academies of Science.: Climate Data Records

fromEnvironmental Sciences, National Academies Press 2004[12] Chahine, M From JPLAIRS Image Archive June 2003,

http://www.airs.jpl.nasa.gov/Multimedia.[13] GrADS Home Page.: Center for Ocean-Land-AtmosphereStudies. 25

Apr 2006, http://www.iges.org/grads/grads.html.[14] Goldberg,M.D., Y. Qu, L. M. McMillin, W. Wolf, L. Zhou, and M.

Divarkarla, AIRS near-real-time products and algorithms in support of operational numerical weather prediction, IEEE Trans. Geosci. Remote Sensing, vol. 41, pp. 379-389, Feb. 2003.

[15] SOAR Home Page http://soar.softwarereuse.net/[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) http://mc2.umbc.edu/index.php

[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 Multi-Sensor Satellite Radiance Data for Weather and Climate Studies,” Proceedings of the 3rd International Conference on Web Information Systems and Technology, Barcelona, Spain, March 3-6, 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 became nationally recognized for Atmospheric and Climate Modeling under his leadership. His personal achievements include more than 100 scientific publications in the areas of atmospheric and oceanographic sciences and computational and information sciences. He is most noted for his groundbreaking research in simulation studies of space observing systems and for development of four dimensional data assimilation for weather and climate prediction. Over the years, his achievements have earned him numerous awards including the NASA Medal for Exceptional Scientific Achievement, the NASA Medal for Outstanding Leadership, and NASA’s highest award, the NASA Distinguished Service Medal in 1996. In 1999, Dr. Halem was awarded the honorary Doctor of Law degree from Dalhousie University, in recognition for his contributions to the field of computational science. He is also a noted Fine Arts screenprint maker of space images.

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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.

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

Table 2 SOAR data size and processing times to grid atmospheric radiance measurements.

Fig. 3 Spatial subsetting and monthly averaging of an AIRS window channel data

Fig. 4 SOAR request screen

Fig. 5 SOAR stored results listing

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Date Range Spectral RangeIntervals Native Grid ResolutionStorage (Month of Data, Gb)Average RadianceMaximum RadianceMaximum RadianceBrightness Temperature

MODISNov '04 - Jun '05; Oct - Dec '07 16 channels daily

1° 0.5x °(100 50 )x Km √ √ √ √

AIRS 04- 07Nov ' Dec '324 operationalfrequencies daily

1° 0.5x °(100 50 )x Km √ √ √ √

Original Data (Mb)Gridded Result (Mb)Download Time (min)Processing Time (min)Total Time (min)MODIS 38,502 67 13.01 2.2 14.38AIRS 4,000 40 23.3

Level 1b

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Fig. 6 SOAR map image result

Fig. 7 SOA architecture block diagram

Fig. 8 SOAR architecture block diagram

Fig. 9 SOAR subsystem interaction diagram.

Fig. 10 SOAR Process server block diagram

Fig. 11 Gridded monthly mean AIRS radiance data

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Web ServiceLookup

Publish ServicesWSDL

URLS

Web Service ProviderWeb Service Client

SOAP

UDDIDirectory

HTML

HTTP

SOARClient

User(Browser)

SOARWeb

Services

File Share(Binary

Data/Images)

BluegritSupercomputer

Cluster

User Client Web Service Bluegrit

New Request

Submit New Request FormGet radiance data() :sessionKey

login()Submit login

Session Key

Get login page

Welcome Page/Recent Results

New Request Form

Request Status :requestID

Login Page (HTML)

Result List

Get user results() :sessionKey

Science Image File

Request Status Page :requestID

Get Results(requested) :sessionKey

Get raw data

Raw Data File Handle

Subset/Average Data

Condensed Data File Handle

Render Data as Image

Get Request Results

Request Results

File URL(s)

Set Status

Results Display

Image/Animation/Data URL(s)

SOAP Bluegrit

Torque/PBS

Workflow/Task

Scheduling

SOARWeb ServicesApplication

Web ServiceInterface

(Using Axis)

Utility/GriddingInterfaceManager

ServerContext/Resources

Database(Postgres)