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Small satellite space operations

CTA Space Systems has played a premier role in the development of the 'lightsat' programs of the 80's and 90's. The high costs and development times associated with conventional LEO satellite design, fabrication, launch, and operations continue to motivate the development of new methodologies, techniques, and generally low cost and less stringently regulated satellites. These spacecraft employ low power 'lightsat' communications (versus TDRSS for NASA's LEO's) and typically fly missions with payload/experiment suites that can succeed, for example, without heavily redundant backup systems and large infrastructures of personnel and ground support systems. Such small yet adaptable satellites are also typified by their very short contract-to-launch times (often one to two years). This paper reflects several of the methodologies and perspectives of our successful involvement in these innovative programs and suggests how they might relieve NASA's mounting pressures to reduce the cost of both the spacecraft and their companion mission operations. It focuses on the use of adaptable, sufficiently powerful yet inexpensive PC-based ground systems for wide ranging user terminal (UT) applications and master control facilities for mission operations. These systems proved themselves in successfully controlling more than two dozen USAF, USN, and ARPA satellites at CTA/SS. UT versions have linked with both GEO and LEO satellites and functioned autonomously in relay roles often in remote parts of the world. LEO applications particularly illustrate the efficacy of these concepts since a user can easily mount a lightweight antenna, usually an omni or helix with light duty rotors and PC-based drivers. A few feet of coax connected to a small transceiver module (the size of a small PC) and a serial line to an associated PC establishes a communications link and together with the PC constitute a viable ground station. Applications included geomagnetic mapping; spaceborne solid state recorder validation; global store-and-forward data communications for both scientific and military purposes such as Desert Storm; UHF transponder services for both digital data and voice using a constellation; remote sensor monitoring of weather and oceanographic conditions; classified payloads; and UHF spectrum surveillance. Ground processing has been accomplished by automatic unattended or manual operation. Management of multiple assets highlights the relative ease with which two constellations totaling nine satellites were controlled from one system including constellation station keeping. Our experience in small end-to-end systems including concurrent design, development, and testing of the flight and operational ground systems offers low cost approaches to NASA scientific satellite operations of the 1990's.

Reiss, Keith↗

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗

Community Requirements Meta-Analysis: Characterizing Needs and Opportunities for HPDF

This High Performance Data Facility (HPDF) Project is creating a new scientific user facility to provide advanced infrastructure for data-intensive science, supporting the DOE’s Office of Science (SC) community. HPDF’s mission is to enable and accelerate scientific discovery by delivering state-of-the-art data management infrastructure, capabilities, and tools. This meta-analysis examines the needs of the breadth of the SC community, captured in publicly available community reports or mission documents. The meta-analysis identifies and provides initial characterization of fifteen core requirements for the HPDF Project team to consider during the conceptual design phase. The fifteen requirements illustrate how scientific work among SC communities requires modern, seamless user experiences across the ASCR Ecosystem to advance the use of large volumes of heterogeneous data. The scientific community requires support for the missing middle of compute between local and HPC to interactively and collaboratively use growing datasets. Data producers and end users will benefit from enhanced data catalogs and portals that improve data access through advanced search of well curated data. The fifteen requirements are examined here organized across five themes for discussion. Examples in each theme illustrate the array of scientific needs that convey the important role that the fully realized and operational High Performance Data Facility will be able to play as an integral part of the evolving ASCR Ecosystem. Our amalgamated data tables from ESnet reports demonstrate ranges to the volumes of data HPDF must be concerned with, but limitations are inherent to this meta-analysis (see Key Challenges & Limitations). Feedback and validation of these requirements along with additional details and emergent community requirements will be gathered through user research and design activities.

97 MATHEMATICS AND COMPUTING↗

Homomorphic data compression for real time photon correlation analysis

The construction of highly coherent X-ray sources, combined with next-generation detectors that are larger and faster, has enabled new research opportunities across the scientific landscape. Among the techniques that benefit most from these advancements is X-ray photon correlation spectroscopy (XPCS), where faster acquisition unlocks the ability to study faster dynamics within samples. However, faster acquisition on larger detectors also introduces unprecedented challenges for online data processing and offline data storage. Such challenges are particularly prominent for XPCS, where real time analyses require simultaneous calculation of all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The offline compression scheme extends storage capacity by a factor of 40 while preserving key features in the lossy compressed data. Meanwhile, the online compression scheme reduces the computational time to below 1 ms, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

36 MATERIALS SCIENCE↗

Assessment of the first radiances received from the VSSR Atmospheric Sounder (VAS) instrument

The first orderly, calibrated radiances from the VAS-D instrument on the GOES-4 satellite are examined for: image quality, radiometric precision, radiation transfer verification at clear air radiosonde sites, regression retrieval accuracy, and mesoscale analysis features. Postlaunch problems involving calibration and data processing irregularities of scientific or operational significance are included. The radiances provide good visual and relative radiometric data for empirically conditioned retrievals of mesoscale temperature and moisture fields in clear air.

Chesters, D.↗

Configuration and Implementation

The main objectives of a Planetary Data System (PDS) are to curate planetary data - that is to store and maintain complete planetary data sets and the necessary supporting documentation to make them useful - and to facilitate scientific study of these data through improved organization and access. A hardware system, no matter how sophisticated and supportive, cannot replace the live colleague interaction needed for good science.System users with only a general knowledge of a data file or data set will need guidance in determining whether that data is most appropriate to support a theory. The PDS is meant to allow easy access to data, to provide useful presentations of that data, and to facilitate data analysis; however it is not designed to supercede the critical examination and interactions that data analysis requires. The Planetary Data System will be a powerful research tool which allows the entire planetary science community access to a complete data and will facilitate the archiving of pass, current, and future mission data sets.

Source record↗

Auroral imaging from Spacelab 3

A comprehensive set of video and photographic images of the aurora was acquired and analyzed from Spacelab 3. Since each pass of Spacelab covers several hours of local time during an elapsed time of only a few minutes, it was hoped that these images would provide additional insight into the local-time dependence of the auroral morphology. Spacelab 3 was launched on April 29, 1985 and auroral data were obtained during the period April 30 to May 5. Preparations made for the auroral experiment, the quality and quantity of the data obtained, the initial scientific results, and ideas for further inquiry suggested by the preliminary review of the data are discussed.

Hallinan, T. J.↗

Restoration of Apollo Data by the Lunar Data Project/PDS Lunar Data Node: An Update

The Apollo 11, 12, and 14 through 17 missions orbited and landed on the Moon, carrying scientific instruments that returned data from all phases of the missions, included long-lived Apollo Lunar Surface Experiments Packages (ALSEPs) deployed by the astronauts on the lunar surface. Much of these data were never archived, and some of the archived data were on media and in formats that are outmoded, or were deposited with little or no useful documentation to aid outside users. This is particularly true of the ALSEP data returned autonomously for many years after the Apollo missions ended. The purpose of the Lunar Data Project and the Planetary Data System (PDS) Lunar Data Node is to take data collections already archived at the NASA Space Science Data Coordinated Archive (NSSDCA) and prepare them for archiving through PDS, and to locate lunar data that were never archived, bring them into NSSDCA, and then archive them through PDS. Preparing these data for archiving involves reading the data from the original media, be it magnetic tape, microfilm, microfiche, or hard-copy document, converting the outmoded, often binary, formats when necessary, putting them into a standard digital form accepted by PDS, collecting the necessary ancillary data and documentation (metadata) to ensure that the data are usable and well-described, summarizing the metadata in documentation to be included in the data set, adding other information such as references, mission and instrument descriptions, contact information, and related documentation, and packaging the results in a PDS-compliant data set. The data set is then validated and reviewed by a group of external scientists as part of the PDS final archive process. We present a status report on some of the data sets that we are processing.

Lunar↗

Breaking Barriers: Integrating Geo-Leo Aerosol Data with an Open-Source Approach

The scientific community is still examining the novel data from geostationary satellite observations and evaluating methods for effectively fusing the polar observations with various spatial and temporal resolutions. However, the merged data will present a significant ""Big Data"" challenge, including processing, storage, data discoverability, accessibility, and migration within cloud computing environments. We have developed an open-source package to fuse aerosol optical depths (AOD) products from six satellite sensors in the past four years (2019~2023), and this presentation will update our recent progress. Using this Python-based package, we produced a level 3 global (AOD) product in a quarter-degree spatial resolution every half-hour, fusing the Level 2 AOD data with the Dark Target aerosol retrieval algorithm from six satellites: three geostationary (GOES-16/17 and Himawari-8) with high temporal resolution, and three polar orbiting (TERRA/MODIS, AQUA/MODIS, and SNPP-VIIRS) with global coverage. By integrating these observations, the diurnal cycle of global AOD in this fused product can be characterized at local, regional, and global scales. Furthermore, we are committed to openness and transparency by providing our package and its associated functionalities as open-source. Our dedication to adhering to the FAIR, CARE, and TRUST principles ensures that our users can rely on the integrity and ethical standards of our work. For instance of Interoperability, this package fuses remote sensing products on demand into desired temporal and spatial domains. It can be run in a central processing unit (CPU) or a Graphics processing unit (GPU) mode. This package will empower researchers and practitioners to use satellite and sensor data efficiently in various applications and research.

Xiaohua Pan↗

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Airborne Validation of Spatial Properties Measured by the CALIPSO Lidar

The primary payload onboard the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) satellite is a dual-wavelength backscatter lidar designed to provide vertical profiling of clouds and aerosols. Launched in April 2006, the first data from this new satellite was obtained in June 2006. As with any new satellite measurement capability, an immediate post-launch requirement is to verify that the data being acquired is correct lest scientific conclusions begin to be drawn based on flawed data. A standard approach to verifying satellite data is to take a similar, or validation, instrument and fly it onboard a research aircraft. Using an aircraft allows the validation instrument to get directly under the satellite so that both the satellite instrument and the aircraft instrument are sensing the same region of the atmosphere. Although there are almost always some differences in the sampling capabilities of the two instruments, it is nevertheless possible to directly compare the measurements. To validate the measurements from the CALIPSO lidar, a similar instrument, the Cloud Physics Lidar, was flown onboard the NASA high-altitude ER-2 aircraft during July- August 2006. This paper presents results to demonstrate that the CALIPSO lidar is properly calibrated and the CALIPSO Level 1 data products are correct. The importance of the results is to demonstrate to the research community that CALIPSO Level 1 data can be confidently used for scientific research.

McGill, Matthew J.↗

Open Source Platform for Live Processing of High Data Rate Electron Microscopy (SBIR Phase I Award Topic 15, Subtopic B, Final Scientific / Technical Report)

4D scanning transmission electron microscopy (STEM) is experiencing a revolution both in terms of data size, and data veracity, as are many other areas of experimental data acquisition. This presents enormous opportunities to make new scientific discoveries, but is also hamstrung by aging software that is no longer fit for purpose. Experimental data acquisition platforms must make the same investments in software infrastructure that the simulation community made decades ago to take advantage of high-performance computing (HPC) if they hope to realize the full potential of the latest advances made in detector technology. This requires rethinking the design and deployment of hardware, moving away from small, fixed commodity computing capabilities with closed interfaces to open, modular, scalable systems designed for parallelism and throughput. The technical approach will leverage best-in-class open source technologies, and develop new approaches strategically to build an open source platform for experimental data acquisition targeting high data rate electron microscopy. The project will develop a software platform that uses the power and performance of modern HPC resources to ingest the large volume of data produced by electron microscopes using direct electron detectors. A set of configurable pipelines will be used to enable the processing to be tailored to the problem being investigated with builtin visualization of data at interactive rates. This project will leverage investments made by the DOE and other agencies to provide a powerful software platform for the experimental data community working with most powerful electron microscopes, such as 4D STEM detectors. It will create a platform that serves as a reference for the community. Permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close to where it is generated. This advanced software platform will require customization for each deployment, creating opportunities for software services that are typically offered to extend open source software platforms.

97 MATHEMATICS AND COMPUTING↗

On the New Optical Constants Database (OCdb) and its Importance for the Interpretation of Observational Data

The Optical Constants database(ocdb.smce.nasa.gov) came online in February 2023 and provides complex refractive indices of laboratory-generated organic refractory materials and ices relevant to (exo) planetary and astrophysical environments.The goal of the OCdb is to centralize published optical constants data to facilitate both their access by the scientific community and their use to analyze observational data returned by space missions and ground-based observatories. Computational tools are also under development to facilitate scientific use of the available OCdb optical constants data sets. Investigators generating laboratory optical constants are therefore encouraged to contribute their data to OCdb in order to increase the availability of their data and to enhance the scientific effectiveness of the database. Optical constants are critical input parameters in models (e.g.,radiative transfer, atmospheric, and reflectance spectral models)that are used to simulate the absorption, reflection, and scattering of light due to solid materials present in planetary and astrophysical environments (planets, their satellites, exoplanets, asteroids, comets, protoplanetary disks, etc.), and are key to the compositional interpretation of observations. We will first present the infrastructure of the OCdb and show how to use and contribute to it. We will introduce the two large NASA projects, namely, the Laboratory Astrophysics Directed Work Package and the NASA Center for Optical Constants, that have been instrumental in (i) developing the OCdb, (ii) generating planetary-and astrophysics-relevant ices and organic refractory materials from gas and ice irradiation in the laboratory, and (iii) determining their optical constants for inclusion in OCdb. We will also present two studies that are making use of these optical constants to interpret observations of Titan’s atmosphere and Pluto’s surface. These studies show the importance of measuring optical constants of laboratory-generated materials, and their impact on the models used to analyze and interpret astronomical observations. These studies also demonstrate the essential importance of such a database and the need for optical constants of a broad range of materials and wavelengths to enable the scientific community and to maximize the scientific return from space missions (e.g., Cassini, New Horizons, SOFIA, JWST).

The Optical Constants database↗

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.

life sciences↗

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA "Open Science Data Repositories (OSDR)" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to "big data" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.

omics↗

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001).

54 ENVIRONMENTAL SCIENCES↗

High-speed, cross-continental networks - A necessity

The networking requirements that the Earth Observing System (EOS) program has levied on NASA and the national and international operational networking communities is addressed. The objectives of the EOS program are listed and include the development and operation of an observing system to acquire essential, global earth science data on a long-term, sustained basis and in a manner which maximizes the scientific utility of the data and simplifies data analysis. Network design issues are also discussed, including protocol selection, the Transport Control Protocol/Internet Protocol suite, the Open Systems Interconnect network suite, and the internet evolution. Open and closed network issues are also discussed. A number of networks now in place are assessed including NASNet, and ESNet. These offer possible options and/or solutions to be considered by the NASA Headquarters EOS Data and Information Systems Program.

Wong, C. M.↗

SHADOZ (Southern Hemisphere ADditional OZonesondes): A New Ozonesonde Data Set for the Earth Science Community

In the past several years, new tropical tropospheric ozone data products have been developed from TOMS and other satellites. Global chemical-transport models have been developed for interpretation of satellite data and to predict future ozone levels in the troposphere and stratosphere. However, the lack of ozone profile measurements for validation and evaluation of these data sets and models is critical in regions like the tropics. In 1998 NASA/Goddard Space Flight Center, in partnership with NOAA/CMDL (Climate Monitoring and Diagnostics Lab) and other nations, began a 2-year project to collect weekly ozonesonde measurements at southern hemisphere tropical sites and make the data available to the scientific community at a single electronic location: http://code9lQ.gsfc.nasa.gov/Data services/Shadoz/shadoz hmpq2.htmi A summary of data from the SHADOZ sites will be presented: Ascension Island, Fiji, Tahiti, Galapagos, American Samoa, Natal (Brazil), Reunion Island, Watukosek (Java), Nairobi and Irene, South Africa. SHADOZ is designed to meet other needs: (1) Provide the first climatology of tropical ozone along the equatorial zone for the wave-one pattern in total ozone; (2) Supplement field project observations. (3) Guide algorithm development for future satellite instruments; (4) Train scientists and educators in southern hemisphere tropical locations. From time to time, intensive tropical campaigns are making data available to SHADOZ. Data from the first half of 1999 will include INDOEX (Indian Ocean Experiment), SOWER (Stratospheric Ozone and Water in the Equatorial Region) at Christmas Island (2N, 157W), and a cruise from Norfolk, Virginia to Cape Town and Mauritius on NOAA's RN 'Ronald H Brown.'

Witte, J. C.↗