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NASA NTRSDate not supplied

Providing Long-term Solar and Meteorological Resource Information from NASA Research through the GIS-Enabled POWER Web Services Portal

Solar and other renewable energy systems are optimized using geophysical parameters describing information about the solar resource and meteorological environments and how those environments may be changing in time. The Prediction of Worldwide Renewable Energy Resource (POWER) team at the NASA LaRC Research Center deployed its first version of the revitalized POWER geophysical parameter website that employs Esri Geographic Information System (GIS) tools. This web application provides access to both time series and climatological data sets spanning from a few days behind real-time back to the early 1980’s with a spatial resolution of 0.5 x 0.5 degree, thus extending over 30 years. The GIS tools enable, generate and store climatological averages using spatial queries and calculations in a spatial database resulting in greater accessibility to government agencies, industry and individuals. There is an API that provides data through a URL coding and can also invoked inside user software packages. Output data formats now include ASCII, CSV, geoTiff, JSON and .netCDF. Since May 2018, over 10.4 TB of data have been delivered to fulfill over 110 million data requests from 240,000 unique user IPs (https://power.larc.nasa.gov). This presentation provides an overview of this project and the current version of the new POWER web capabilities through to the end usage. Surface solar parameters are adapted from both the NASA/GEWEX (Global and Energy Water Cycle Exchange) SRB (Surface Radiation Budget) project and the CERES (Clouds and Earth’s Radiant Energy System) data products. The meteorological parameters are adapted from NASA’s MERRA-2 (Modern Era Retrospective-analysis for Research and Applications). We review uncertainty of various basic parameters using surface measurements. After the introduction, we specifically discuss various clusters of parameters for equator pointing tilted surfaces that provide for the estimation of optimal tilt angle by month and year. The discussion will emphasize a few examples of how solar and building engineers are using the data products. We will then preview the capabilities of Version 2 featuring examples of hourly solar and meteorological data products, customized user reports and expanded web mapping services from CERES data products. The web services provide a unique, expandable resource for renewable energy systems engineers to design, evaluate and optimize to environments worldwide.

Solar Resource↗
NASA NTRS2019

The Ice, Cloud, and Land Elevation Satellite-2 Mission: A Global Geolocated Photon Product Derived from the Advanced Topographic Laser Altimeter System

The Ice, Cloud, and land Elevation Satellite – 2 (ICESat-2) observatory was launched on 15 September 2018 to measure ice sheet and glacier elevation change, sea ice freeboard, and enable the determination of the heights of Earth’s forests. ICESat-2’s laser altimeter, the Advanced Topographic Laser Altimeter System (ATLAS) uses green (532 nm) laser light and single-photon sensitive detection to measure time of flight and subsequently surface height along each of its six beams. In this paper, we describe the major components of ATLAS, including the transmitter, the receiver and the components of the timing system. We present the major components of the ICESat-2 observatory, including the Global Positioning System, star trackers and inertial measurement unit. The ICESat-2 Level 1B data product (ATL02) provides the precise photon round-trip time of flight, among other data. The ICESat-2 Level 2A data product (ATL03) combines the photon times of flight with the observatory position and attitude to determine the geodetic location (i.e. the latitude, longitude and height) of the ground bounce point of photons detected by ATLAS. The ATL03 data product is used by higher-level (Level 3A) surface-specific data products to determine glacier and ice sheet height, sea ice freeboard, vegetation canopy height, ocean surface topography, and inland water body height.

ICESat-2↗
NASA NTRS1971

Crew interface specification development study for in-flight maintenance and stowage functions

The need and potential solutions for an orderly systems engineering approach to the definition, management and documentation requirements for in-flight maintenance, assembly, servicing, and stowage process activities of the flight crews of future spacecraft were investigated. These processes were analyzed and described using a new technique (mass/function flow diagramming), developed during the study, to give visibility to crew functions and supporting requirements, including data products. This technique is usable by NASA for specification baselines and can assist the designer in identifying both upper and lower level requirements associated with these processes. These diagrams provide increased visibility into the relationships between functions and related equipments being utilized and managed and can serve as a common communicating vehicle between the designer, program management, and the operational planner. The information and data product requirements to support the above processes were identified along with optimum formats and contents of these products. The resulting data product concepts are presented to support these in-flight maintenance and stowage processes.

Carl, J. G.↗
NASA NTRSDate not supplied

Novel Hourly-Resolved Global Cloud Property Composite from Operational Satellites Imagers

Numerous applications in satellite remote sensing of surface properties, atmospheric state, composition, and radiation require accurate knowledge of the location and characteristics of clouds. Operational satellite imager radiances are valuable for cloud detection and for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. This paper describes a new global high-resolution dataset of cloud properties made available for community use that is constructed from analyses of a constellation of meteorological satellite imagers. Data taken from Meteosat-8/9 and -11, Himawari-8/9, GOES-16, and -17/-18, Aqua, Terra, Suomi-NPP and NOAA-20 are processed and composited on a 3-km grid to provide hourly global coverage. A historical multi-year dataset is available to serve various needs including modeling challenges related to cloud evaluations and parameterizations. Near real-time data products are also currently available between 60N and 60S. Efforts are underway to operationalize polar orbiting satellite cloud detection methods for low-latency applications over polar regions. The cloud detection and retrieval algorithms have been developed over many years to support NASA weather and climate programs such as the Clouds and Earth’s Radiant Energy System (CERES). To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. A brief description of the methods highlighting the unique aspects of the SatCORPS data products will be presented along with information on their status and availability.

SatCORPS↗
NASA NTRS2011

Generic, Extensible, Configurable Push-Pull Framework for Large-Scale Science Missions

The push-pull framework was developed in hopes that an infrastructure would be created that could literally connect to any given remote site, and (given a set of restrictions) download files from that remote site based on those restrictions. The Cataloging and Archiving Service (CAS) has recently been re-architected and re-factored in its canonical services, including file management, workflow management, and resource management. Additionally, a generic CAS Crawling Framework was built based on motivation from Apache s open-source search engine project called Nutch. Nutch is an Apache effort to provide search engine services (akin to Google), including crawling, parsing, content analysis, and indexing. It has produced several stable software releases, and is currently used in production services at companies such as Yahoo, and at NASA's Planetary Data System. The CAS Crawling Framework supports many of the Nutch Crawler's generic services, including metadata extraction, crawling, and ingestion. However, one service that was not ported over from Nutch is a generic protocol layer service that allows the Nutch crawler to obtain content using protocol plug-ins that download content using implementations of remote protocols, such as HTTP, FTP, WinNT file system, HTTPS, etc. Such a generic protocol layer would greatly aid in the CAS Crawling Framework, as the layer would allow the framework to generically obtain content (i.e., data products) from remote sites using protocols such as FTP and others. Augmented with this capability, the Orbiting Carbon Observatory (OCO) and NPP (NPOESS Preparatory Project) Sounder PEATE (Product Evaluation and Analysis Tools Elements) would be provided with an infrastructure to support generic FTP-based pull access to remote data products, obviating the need for any specialized software outside of the context of their existing process control systems. This extensible configurable framework was created in Java, and allows the use of different underlying communication middleware (at present, both XMLRPC, and RMI). In addition, the framework is entirely suitable in a multi-mission environment and is supporting both NPP Sounder PEATE and the OCO Mission. Both systems involve tasks such as high-throughput job processing, terabyte-scale data management, and science computing facilities. NPP Sounder PEATE is already using the push-pull framework to accept hundreds of gigabytes of IASI (infrared atmospheric sounding interferometer) data, and is in preparation to accept CRIMS (Cross-track Infrared Microwave Sounding Suite) data. OCO will leverage the framework to download MODIS, CloudSat, and other ancillary data products for use in the high-performance Level 2 Science Algorithm. The National Cancer Institute is also evaluating the framework for use in sharing and disseminating cancer research data through its Early Detection Research Network (EDRN).

Foster, Brian M.↗
NASA NTRS2005

A Collaborative Decision Environment for UAV Operations

NASA is developing Intelligent Mission Management (IMM) technology for science missions employing long endurance unmanned aerial vehicles (UAV's). The IMM groundbased component is the Collaborative Decision Environment (CDE), a ground system that provides the Mission/Science team with situational awareness, collaboration, and decisionmaking tools. The CDE is used for pre-flight planning, mission monitoring, and visualization of acquired data. It integrates external data products used for planning and executing a mission, such as weather, satellite data products, and topographic maps by leveraging established and emerging Open Geospatial Consortium (OGC) standards to acquire external data products via the Internet, and an industry standard geographic information system (GIs) toolkit for visualization As a Science/Mission team may be geographically dispersed, the CDE is capable of providing access to remote users across wide area networks using Web Services technology. A prototype CDE is being developed for an instrument checkout flight on a manned aircraft in the fall of 2005, in preparation for a full deployment in support of the US Forest Service and NASA Ames Western States Fire Mission in 2006.

D'Ortenzio, Matthew V.↗
NASA NTRS2023

PACE Technical Report Series, Volume 11: The PACE Postlaunch Airborne eXperiment (PACE-PAX)

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE; https://pace.gsfc.nasa.gov) mission is NASA's latest and greatest effort to study the ocean-atmosphere system and build upon the legacy of previous satellite missions that have revolutionized our understanding of our home planet. By using advanced radiometric and polarimetric instruments to measure ocean color and key atmospheric variables, PACE aims to provide new insights into the complex interactions between the ocean, atmosphere, and biosphere. This information will be critical for improving our ability to model and predict changes in the Earth's climate, as well as to manage and conserve our planet's valuable natural resources. Performance assessments and science data product validation is a critical process for any Earth Science space-borne mission, as it involves assessing and verifying the quality of the data products generated by the mission while it is in orbit, as well as assigning uncertainties to these retrievals. More specifically, validation efforts are essential for assessing the accuracy of instrument data and the derived science products across varied spatial and temporal scales and ensuring their reliability and consistency with the mission's scientific goals and objectives. The overall plan for validation of PACE data is described in “PACE Science Data Product Validation Plan” (see, e.g., https://pace.gsfc.nasa.gov), and among range of post-launch activities, it describes the need and requirements for a post-launch airborne field campaign in support of PACE validation. This volume outlines the plans and requirements of the PACE Postlaunch Airborne eXperiment (PACE-PAX). The main goal of PACE-PAX is to gather validation and assessment data for the PACE mission, using a variety of airborne and ground-based assets. These assets will be coordinated to obtain complementary measurements of key ocean and atmospheric variables, such as ocean color, aerosols, and clouds. By collecting data from multiple sources, PACE-PAX will help to validate and refine the data products generated by the PACE mission, as well as to identify any potential errors or biases in the measurements. This will be essential for ensuring the accuracy and reliability of the PACE data products, and for maximizing the scientific value of the mission.

Kirk D Knobelspiesse↗
NASA NTRS2003

Software for Simulating Remote Sensing Systems

The Application Research Toolbox (ART) is a collection of computer programs that implement algorithms and mathematical models for simulating remote sensing systems. The ART is intended to be especially useful for performing design-tradeoff studies and statistical analyses to support the rational development of design requirements for multispectral imaging systems. Among other things, the ART affords a capability to synthesize coarser-spatial-resolution image-data sets from finer-spatial-resolution data sets and multispectral-image-data products from hyperspectral-image-data products. The ART also provides for synthesis of image-degradation effects, including point-spread functions, misregistration of spectral images, and noise. The ART can utilize real or synthetic data sets, along with sensor specifications, to create simulated data sets. In one example of a typical application, simulated data pertaining to an existing multispectral sensor system are used to verify the data collected by the system in operation. In the case of a proposed sensor system, the simulated data can be used to conduct trade studies and statistical analyses to ensure that the sensor system will satisfy the requirements of potential scientific, academic, and commercial user communities.

Zanoni, Vicki↗
NASA NTRS2003

Software for Simulating Remote Sensing Systems

The Application Research Toolbox (ART) is a collection of computer programs that implement algorithms and mathematical models for simulating remote sensing systems. The ART is intended to be especially useful for performing design-tradeoff studies and statistical analyses to support the rational development of design requirements for multispectral imaging systems. Among other things, the ART affords a capability to synthesize coarser-spatial-resolution image-data sets from finer-spatial-resolution data sets and multispectral-image-data products from hyperspectral-image-data products. The ART also provides for synthesis of image-degradation effects, including point-spread functions, misregistration of spectral images, and noise. The ART can utilize real or synthetic data sets, along with sensor specifications, to create simulated data sets. In one example of a typical application, simulated data pertaining to an existing multispectral sensor system are used to verify the data collected by the system in operation. In the case of a proposed sensor system, the simulated data can be used to conduct trade studies and statistical analyses to ensure that the sensor system will satisfy the requirements of potential scientific, academic, and commercial user communities.

Vicki Zanoni↗
NASA NTRS2019

Collecting and Processing Earth Science Data Metrics at NASA ESDIS

Since the launch of Terra satellite in 1999, the number of Earth Science remote sensing data products created and distributed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS) has increased from a few hundred to nearly ten thousand. NASA's Earth Science Data and Information System (ESDIS) Metrics System (EMS) collects metrics on data ingest, archive, and distribution by its Distributed Active Archive Centers (DAACs) and the Science Investigator-led Systems (SIPS), known as Data Providers. These metrics are critical in helping NASA management as well as data producers in resource planning and gaining a wide range of knowledge of data users and data usage.EMS receives flat files, or log files of data archive, ingest, and distribution either in their raw format, such as Apache web logs, or text files of log records formatted by the Data Providers. Tens of millions of records are processed each day to extract metrics on data products, user information, distribution protocols and services, and so on. The metrics are then made available to designated parties.This presentation provides an overview of the EMS processing workflow and improvement efforts made in recent years to handle ever-increasing number of data records and new metrics requirements, discusses several key steps including mapping log records to data products and identifying user communities along with geo-distribution, and demonstrates typical metrics capabilities produced by the EMS system. Challenges and potential approaches to improve the system are also discussed.

Pan, Jianfu↗
NASA NTRSDate not supplied

Stewardship Best Practices for Improved Discovery and Reuse of Heterogeneous and Cross-Disciplinary Earth System Data

Some of the Earth system data products such as those from NASA airborne and field investigations (a.k.a. campaigns), are highly heterogeneous and cross-disciplinary, making the data extremely challenging to manage. For example, airborne and field campaign measurements tend to be sporadic over a period of time, with large gaps. Data products generated are of various processing levels and utilized for a wide range of inter- and cross-disciplinary research and applications. Data and derived products have been historically stored in a variety of domain-specific standard (and some non-standard) formats and in various locations such as NASA Distributed Active Archive Centers (DAACs), NASA airborne science facilities, field archives, or even individual scientists’ computer hard drives. As a result, airborne and field campaign data products have often been managed and represented differently, making it onerous for data users to find, access, and utilize campaign data. Some difficulties in discovering and accessing the campaign data originate from the incomplete data product and contextual metadata that may contain details relevant to the campaign (e.g. campaign acronym and instrument deployment locations), but tend to lack other significant information needed to understand conditions surrounding the data. Such details can be burdensome to locate after the conclusion of a campaign. Utilizing consistent terminology, essential for improved discovery and reuse, is also challenging due to the variety of involved disciplines. To help address the aforementioned challenges faced by many repositories and data managers handling airborne and field data, this presentation will describe stewardship practices developed by the Airborne Data Management Group (ADMG) within the Interagency Implementation and Advanced Concepts Team (IMPACT) under the NASA’s Earth Science Data systems (ESDS) Program.

best practices↗
NASA NTRS2019

Quantitative Comparison of Proprietary and Open-Source Georeferencing Tools for Use with Astronaut Photography

The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. Astronauts on the International Space Station (ISS) respond to requests from CEO to acquire imagery of scientific and education targets, to include high profile targets in response to activations from the International Charter for Space & Major Disasters (also known as the International Disaster Charter, or IDC) and NASA’s Disasters Program. CEO facilitates the acquisition of astronaut photography in response to IDC events and delivers georeferenced data products to the United States Geological Survey (USGS) for distribution to the disaster community. Using GeoRef, an internal web-based tool developed in collaboration with NASA’s Ames Research Center, CEO generates data packages of georeferenced imagery, uncertainty images for assessing control and tie point accuracy, and metadata documenting raw and processed data. Operational experience with the Georef software identified vulnerabilities to internal code and server errors that can significantly increase time of data production. As such, CEO developed a backup procedure in case the GeoRef software experiences front-end or back-end errors. A system using OSGEO’s open-source QGIS software combined with a semi-automated pipeline using the object-oriented Python language and the Geospatial Abstract Library for generating metadata is quantitatively compared to GeoRef’s data package for quality and productivity. Root Mean Square Error (RMSE) provides a standard measurement of data quality as it relates to ground error. Assessing RMSE measurements generated from georeferenced astronaut photographs acquired with different obliquity and focal length offers a comprehensive accuracy assessment of the software’s transformation algorithms. This assessment will indicate the software's ability to produce data products with the least ground-error or highest data quality regarding ground accuracy. In addition, a comparison of the software’s efficiency in generating a data package that includes georeferenced images, metadata, and uncertainty images for measuring tie/ground point error was performed. Initial results, based on the comparison of three nadir-facing astronaut photographs acquired with a 95mm focal length, reveal the QGIS-based system's average RMSE is 2.36 (pixels) suggesting its georectification system produces data products that meet and perhaps improve upon Georef solution's average RMSE of 32.99 (pixels). However, the QGIS system was unable to reproduce two unique Georef data products, uncertainty images for measuring tie and control point errors and a translated unwrapped image. In addition, the Georef software is designed to accept handheld camera pose information from a hardware component (Geosens) scheduled for deployment on the ISS in late 2018; this information is intended to provide increased accuracy and auto-registration capability for astronaut photographs. Future work is expected to determine the QGIS-based georectification system’s potential as an open-source alternative (and operational backup) to Georef for georeferencing the full range of resolutions and viewing angles unique to handheld digital camera imagery in support of ISS disaster response activities.

Jagge, Amy M.↗
NASA NTRS2015

Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project. The Soil Moisture Active Passive (SMAP) mission will enhance the accuracy and the resolution of space-based measurements of terrestrial soil moisture and freeze-thaw state. SMAP data products will have a noteworthy impact on multiple relevant and current Earth Science endeavors. These include: Understanding of the processes that link the terrestrial water, the energy and the carbon cycles, Estimations of global water and energy fluxes over the land surfaces, Quantification of the net carbon flux in boreal landscapes Forecast skill of both weather and climate, Predictions and monitoring of natural disasters including floods, landslides and droughts, and Predictions of agricultural productivity. To provide these data, the SMAP mission will deploy a satellite observatory in a near polar, sun synchronous orbit. The observatory will house an L-band radiometer that operates at 1.40 GHz and an L-band radar that operates at 1.26 GHz. The instruments will share a rotating reflector antenna with a 6 meter aperture that scans over a 1000 km swath.

L4-SM↗
NASA NTRS2018

Challenges in Development of Online Visualization and Analysis Tools for Satellite Data

Over the years, various online visualization and analysis tools have been developed to facilitate satellite data access and help scientific users around the world to conduct research and develop applications (e.g., data product evaluation, what-if questions, etc.). For those who are new to satellite data products, using them can be a daunting task due to many obstacles in data processing such as data formats, complex data structures, special software packages, unfamiliar terminology, etc., especially when one is not sure whether a dataset is suitable for his/er research project. Even for experienced users, developing software for data processing and analysis can be a costly and time-consuming task. Online visualization tools can overcome many of these difficulties and allow users to focus on scientific questions. For example, Giovanni (the Geospatial Interactive Online Visualization and Analysis Infrastructure, https://giovanni.gsfc.nasa.gov), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), allows access over 1900 satellite and model variables in 82 measurement groups of 8 disciplines without downloading data and software. Main features include basic functions for data analysis and visualization, data provenance, output data in different formats (ASCII, NetCDF, GeoTIFF), and more. Over the years, ~1700 peer-reviewed publications in different disciplines have been benefited from Giovanni in research activities (e.g. initial investigation, what-if questions, product evaluation). Despite the success of online visualization and analysis tools, challenges and new opportunities still exist and more can be done with new requirements and technology. Examples are: a) how to increase the efficiency of dataset search by enhancing intuitive aspects; b) how to facilitate interdisciplinary research; c) how to provide data quality information; d) how to engage users to participate in data quality assessment; and more. NASA Earth Observing System Data and Information System (EOSDIS) satellite-based data products are processed at various levels ranging from Level 0 to Level 4. While most users use data products at higher levels (Level-3 and 4), products at lower levels are still important for case studies, algorithm development, ground validation, etc. In this presentation, we will use Giovanni as an example to present and discuss challenges and near-future opportunities for satellite data online visualization and analysis tools.

Liu, Zhong↗
NASA NTRS2009

Onboard Classification of Hyperspectral Data on the Earth Observing One Mission

Remote-sensed hyperspectral data represents significant challenges in downlink due to its large data volumes. This paper describes a research program designed to process hyperspectral data products onboard spacecraft to (a) reduce data downlink volumes and (b) decrease latency to provide key data products (often by enabling use of lower data rate communications systems). We describe efforts to develop onboard processing to study volcanoes, floods, and cryosphere, using the Hyperion hyperspectral imager and onboard processing for the Earth Observing One (EO-1) mission as well as preliminary work targeting the Hyperspectral Infrared Imager (HyspIRI) mission.

cryosphere↗
NASA NTRS2006

Ground Processing of Data From the Mars Exploration Rovers

A computer program implements the Earth side of the protocol that governs the transfer of data files generated by the Mars Exploration Rovers. It also provides tools for viewing data in these files and integrating data-product files into automated and manual processes. It reconstitutes files from telemetry data packets. Even if only one packet is received, metadata provide enough information to enable this program to identify and use partial data products. This software can generate commands to acknowledge received files and retransmit missed parts of files, or it can feed a manual process to make decisions about retransmission. The software uses an Extensible Markup Language (XML) data dictionary to provide a generic capability for displaying files of basic types, and uses external "plug-in" application programs to provide more sophisticated displays. This program makes data products available with very low latency, and can trigger automated actions when complete or partial products are received. The software is easy to install and use. The only system requirement for installing the software is a Java J2SE 1.4 platform. Several instances of the software can be executed simultaneously on the same machine.

Wright, Jesse↗
NASA NTRS1987

Comparison of North and South American biomes from AVHRR observations

Previous analysis of the North American continent with AVHRR-derived vegetation index measurements showed a strong relation between known patterns of vegetation seasonality, productivity and the spectral vegetation index measurements. This study extends that analysis to South America to evaluate the degree to which these findings extend to tropical regions. The results show that the spectral vegetation index measurements provide a general indicator of vegetation activity across the major biomes of the Western Hemisphere of the earth, including tropical regions. The satellite-observed patterns are strongly related to the known climatology of the continents and may offer a means to improve understanding of global bioclimatology. For example, South America is shown to have a longer growing season with much earlier spring green-up than North America. The time integral of the measurements, computed from 12 composited monthly values, produces a value that is related to published net primary productivity data. However, limited net primary production data does not allow complete evaluation of satellite-observed contrasts between North and South American biomes. These results suggest that satellite-derived spectral vegetation index measurements are of great potential value in improving knowledge of the earth's biosphere.

Goward, Samuel N.↗
NASA NTRS1998

LITE data processing

The Lidar In-space Technology Experiment (LITE) was a payload onboard the Space Shuttle Discovery as part of the September 1994 STS-64 mission. LITE successfully made both atmospheric and surface measurements at the 355 nm, 532 nm, and 1064 nm wavelengths [Winker et al., 1996]. At mission completion, nearly 45 Gbytes of data had been obtained. As the data were collected they were organized into three data sets. The largest of these data sets contained the single-shot full resolution digitized lidar signals for all three wavelengths. The other two data sets contained the instrument status data block (ISDB) and the quick look science data (QLSD). ISDB information was generated once per second and contained timing parameters and engineering data related to the health, status, and configuration of the instrument. The QLSD are 100shot averages of the 355 nm and 532 nm lidar signals and were generated at ten second intervals. As the LITE data were generated, they were output to low-rate and high-rate telemetry streams. The low-rate data contained the ISDB and QLSD. The lowrate data were transmitted to the ground by the Shuttle S-band system through the Tracking and Data Relay Satellite System (TDRSS). The high-rate data contained the full resolution digitized lidar signals for all three wavelengths, the ISDB, and QLSD. The high-rate data were transmitted in real time by the Ku-band system through TDRSS downlink to the LITE operations center at the Johnson Space Center (JSC). High-rate data were only obtained when the orbiter and TDRSS telemetry link coincided with the real-time operations of LITE. The low-rate data were backed up by a Shuttle recorder during periods when the telemetry link was unavailable, and the entire lowrate data stream was transferred to the ground over the course of the mission. A total of 53 hours of lowrate data and 45.5 hours of high-rate data were acquired. All of the LITE raw telemetry data have been archived at the NASA Langley Research Center (LaRC), and the LITE high-rate data have been processed to the level 1 data product. This paper describes the data processing steps required to convert the LITE raw, high-rate telemetry data to the LITE level 1 data product.

Powell, Kathleen A.↗