Distributed Active Archive Center Research Articles

Data-Enabled Field Experiment Planning, Management, and Research Using Cyberinfrastructure

AbstractIn the spring of 2013, NASA conducted a field campaign known as Iowa Flood Studies (IFloodS) as part of the Ground Validation (GV) program for the Global Precipitation Measurement (GPM) mission. The purpose of IFloodS was to enhance the understanding of flood-related, space-based observations of precipitation processes in events that transpire worldwide. NASA used a number of scientific instruments such as ground-based weather radars, rain and soil moisture gauges, stream gauges, and disdrometers to monitor rainfall events in Iowa. This article presents the cyberinfrastructure tools and systems that supported the planning, reporting, and management of the field campaign and that allow these data and models to be accessed, evaluated, and shared for research. The authors describe the collaborative informatics tools, which are suitable for the network design, that were used to select the locations in which to place the instruments. How the authors used information technology tools for instrument monitoring, data acquisition, and visualizations after deploying the instruments and how they used a different set of tools to support data analysis and modeling after the campaign are also explained. All data collected during the campaign are available through the Global Hydrology Resource Center (GHRC), a NASA Distributed Active Archive Center (DAAC).

The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project – Part 2: Environmental driver data

Abstract. Ecosystems are important and dynamic components of the global carbon cycle, and terrestrial biospheric models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. Improving TBM skills, and quantifying and reducing their estimation uncertainties, pose significant challenges. The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal multi-scale and multi-model intercomparison effort set up to tackle these challenges. The MsTMIP protocol prescribes standardized environmental driver data that are shared among model teams to facilitate model–model and model–observation comparisons. This paper describes the global and North American environmental driver data sets prepared for the MsTMIP activity to both support their use in MsTMIP and make these data, along with the processes used in selecting/processing these data, accessible to a broader audience. Based on project needs and lessons learned from past model intercomparison activities, we compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land use and land cover change (LULCC), C3 / C4 grasses fractions, major crops, phenology and soil data into a standard format for global (0.5° × 0.5° resolution) and regional (North American: 0.25° × 0.25° resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by improving the quality, and/or changing their spatial and temporal coverage, and resolution. The resulting standardized model driver data sets are being used by over 20 different models participating in MsTMIP. The data are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov) to provide long-term data management and distribution.

A SOAP Web Service for accessing MODIS land product subsets

Remote sensing data from satellites have provided valuable information on the state of the earth for several decades. Since March 2000, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board NASA’s Terra and Aqua satellites have been providing estimates of several land parameters useful in understanding earth system processes at global, continental, and regional scales. However, the HDF-EOS file format, specialized software needed to process the HDF-EOS files, data volume, and the high spatial and temporal resolution of MODIS data make it difficult for users wanting to extract small but valuable amounts of information from the MODIS record. To overcome this usability issue, the NASA-funded Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics at Oak Ridge National Laboratory (ORNL) developed a Web service that provides subsets of MODIS land products using Simple Object Access Protocol (SOAP). The ORNL DAAC MODIS subsetting Web service is a standard based way of serving satellite data that exploits a fairly established and popular Internet protocol to allow users access to massive amounts of remote sensing data. The Web service provides MODIS land product subsets up to 201 × 201 km in a non-proprietary comma delimited text file format. Users can programmatically query the Web service to extract MODIS land parameters for real time data integration into models, decision support tools or connect to workflow software. Information regarding the MODIS SOAP subsetting Web service is available on the World Wide Web (WWW) at http://daac.ornl.gov/modiswebservice.

A Web-Based Subsetting Service for Regional Scale MODIS Land Products

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor has provided valuable information on various aspects of the Earth System since March 2000. The spectral, spatial, and temporal characteristics of MODIS products have made them an important data source for analyzing key science questions relating to Earth System processes at regional, continental, and global scales. The size of the MODIS product and native HDF-EOS format are not optimal for use in field investigations at individual sites (100 × 100 km or smaller). In order to make MODIS data readily accessible for field investigations, the NASA-funded Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics at Oak Ridge National Laboratory (ORNL) has developed an online system that provides MODIS land products in an easy-to-use format and in file sizes more appropriate to field research. This system provides MODIS land products data in a nonproprietary comma delimited ASCII format and in GIS compatible formats (GeoTIFF and ASCII grid). Web-based visualization tools are also available as part of this system and these tools provide a quick snapshot of the data. Quality control tools and a multitude of data delivery options are available to meet the demands of various user communities. This paper describes the important features and design goals for the system, particularly in the context of data archive and distribution for regional scale analysis. The paper also discusses the ways in which data from this system can be used for validation, data intercomparison, and modeling efforts.

Remote sensing of suspended sediment concentration, flow velocity, and lake recharge in the Peace‐Athabasca Delta, Canada

The transport of fine sediment, carried in suspension by water, is central to the hydrology, geomorphology, and ecological functioning of river floodplains and deltas. An extensive new field data set for the Peace‐Athabasca Delta (PAD), Canada quantifies robust positive relationships between in situ suspended sediment concentration (SSC) and remotely sensed visible/near‐infrared reflectance. These relationships are exploited using SPOT and ASTER satellite images to map suspended sediment concentrations across the PAD for four days in 2006 and 2007, revealing strong variations in water sources and flow patterns, including flow reversals in major distributaries. Near‐daily monitoring with 276 MODIS satellite images tracks hydrologic recharge of floodplain lakes, as revealed by episodic infusions of sediment‐rich water from the Athabasca River. The timing and magnitude of lake recharge are linked to springtime water level on the Athabasca River, suggesting a system sensitive to changes in river flow regime. Moreover, recharge timing differentiates lakes that are frequently and extensively recharged from those recharged more rarely. Finally, we present a first estimation of river flow velocity based on remotely sensed SSC, though saturation may occur at velocities >0.6 m/s. Viewed collectively, the different remote sensing methodologies presented here suggest strong value for visible/near‐infrared remote sensing of suspended sediment to assess hydrologic and sediment transport processes in complex flow environments. Field observations including nephelometric turbidity, specific conductivity, water temperature, Secchi disk depth, suspended sediment concentration, and water level are archived at the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (available at http://daac.ornl.gov//HYDROCLIMATOLOGY/guides/PAD.html).

An empirical relationship between PM 2.5 and aerosol optical depth in Delhi Metropolitan

Atmospheric remote sensing offers a unique opportunity to compute indirect estimates of air quality, which are critically important for the management and surveillance of air quality in megacities of developing countries, particularly in India and China, which have experienced elevated concentration of air pollution but lack adequate spatial–temporal coverage of air pollution monitoring. This article examines the relationship between aerosol optical depth (AOD) estimated from satellite data at 5 km spatial resolution and the mass of fine particles ⩽2.5 μm in aerodynamic diameter (PM 2.5) monitored on the ground in Delhi Metropolitan where a series of environmental laws have been instituted in recent years. PM 2.5 monitored at 113 sites were collocated by time and space with the AOD computed using the data from Moderate Resolution Imaging Spectroradiometer (MODIS onboard the Terra satellite). MODIS data were acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (DAAC). Our analysis shows a significant positive association between AOD and PM 2.5. After controlling for weather conditions, a 1% change in AOD explains 0.52±0.202% and 0.39±0.15% change in PM 2.5 monitored within ±45 and 150 min intervals of AOD data. This relationship will be used to estimate air quality surface for previous years, which will allow us to examine the time–space dynamics of air pollution in Delhi following recent air quality regulations, and to assess exposure to air pollution before and after the regulations and its impact on health.

Selection of the automated thresholding algorithm for the Multi-angle Imaging SpectroRadiometer Radiometric Camera-by-Camera Cloud Mask over land

The Radiometric Camera-by-Camera Cloud Mask (RCCM) is archived at the NASA Langley Distributed Active Archive Center as one of the standard products from the Multi-angle Imaging SpectroRadiometer (MISR) mission. The RCCM algorithm applied over land surfaces uses an Automated Threshold Selection Algorithm (ATSA) to derive thresholds that are applied to a cloud masking test to determine whether a given image pixel is clear or contains cloud. In this article, we established a framework for the selection of ATSA and the cloud masking tests, which is not only suitable for the RCCM over land, but cloud detection for other satellite missions. Using this framework, we have undertaken the largest comparison of existing histogram-based ATSAs (16 in total) and applied them to four cloud masking tests that can be constructed from the MISR radiances, namely the red channel bidirectional reflectance function (BRF), the standard deviation (STDV) of the red channel BRF, the normalized difference vegetation index (NDVI), and a parameter D that is constructed by optimizing the information from NDVI and red channel BRF for cloud detection. The cloud masking tests and ATSAs are applied to 35 MISR scenes from six snow-free land surface types. To evaluate their performance, reference cloud masks are constructed for the 35 scenes using interactive, supervised learning, visualization software. Independent of the ATSA and as a single cloud masking test, D performed the best in terms of having the lowest misclassification rate using the best possible threshold, the highest bimodal rate in the shape of the histograms derived from the 35 scenes, and the least sensitivity to errors in the choice of threshold. Of the 16 ATSAs, the methods of Li and Lee [Li, C.H., and Lee, C.K., (1993). Minimum cross-entropy thresholding. Pattern Recognition, 26(4), 617-625.] and Pal and Bhandari [Pal, N. R., and Bhandari, D., (1993). Image thresholding: some new techniques. Signal Processing, 33, 139–158.] performed the best when applied to D, with essentially unbiased performance and a root mean square of 15% when compared to cloud masks using the best possible thresholds. It is recommended that increased performance of the RCCM-land algorithm can be had through an increase in the space–time sampling used to generate histograms of D and the addition of a STDV cloud masking test to improve the detection of small cumulus clouds.

Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover

AIRS was launched on EOS Aqua on 4 May 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU are twice daily global fields of atmospheric temperature‐humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an RMS error of 1 K, and layer precipitable water with an RMS error of 20%, in cases with up to 80% effective cloud cover. The basic theory used to analyze AIRS/AMSU/HSB data in the presence of clouds, called the at‐launch algorithm, was described previously. Prelaunch simulation studies using this algorithm indicated that these results should be achievable. Some modifications have been made to the at‐launch retrieval algorithm as described in this paper. Sample fields of parameters retrieved from AIRS/AMSU/HSB data are presented and validated as a function of retrieved fractional cloud cover. As in simulation, the degradation of retrieval accuracy with increasing cloud cover is small and the RMS accuracy of lower‐tropospheric temperature retrieved with 80% cloud cover is about 0.5 K poorer than for clear cases. HSB failed in February 2003, and consequently, HSB channel radiances are not used in the results shown in this paper. The AIRS/AMSU retrieval algorithm described in this paper, called version 4, become operational at the Goddard DAAC (Distributed Active Archive Center) in April 2003 and is being used to analyze near‐real time AIRS/AMSU data. Historical AIRS/AMSU data, going backward from March 2005 through September 2002, is also being analyzed by the DAAC using the version 4 algorithm.

EOS MLS Science Data Processing System: a description of architecture and capabilities

The Earth Observing System (EOS) Microwave Limb Sounder (MLS) is an atmospheric remote sensing experiment led by the Jet Propulsion Laboratory of the California Institute of Technology. The objectives of the EOS MLS are to learn more about stratospheric chemistry and causes of ozone changes, processes affecting climate variability, and pollution in the upper troposphere. The EOS MLS is one of four instruments on the National Aeronautics and Space Administration (NASA) EOS Aura spacecraft launched on July 15, 2004, with an operational period extending at least 5 years after launch. This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major components-the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products. The Science Investigator-led Processing System is developed and operated by Raytheon Information Technology and Scientific Services of Pasadena under contract with Jet Propulsion Laboratory.

Using TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States from 1998 to 2002

Abstract Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.

Mobile Agents for Distributed and Heterogeneous Information Retrieval

The heterogeneous, distributed and voluminous nature of many government and corporate data sources impose severe constraints on meeting the diverse requirements of users who analyze the data. Additionally, communication bandwidth limitations, time constraints, and multiple data formats impose further restrictions on users of these distributed data sources. In this paper, we present an Agent-based Complex QUerying and Information Retrieval Engine (ACQUIRE) for large, heterogeneous, and distributed data sources. ACQUIRE acts as a softbot or interface agent by presenting users with a view of a single, unified, homogenous data source, against which users can pose high-level declarative queries. ACQUIRE translates each such user query into a set of sub-queries by employing a combination of planning and traditional database query optimization techniques. ACQUIRE then spawns a set of mobile agents corresponding to these sub-queries, which in turn retrieve the data from various distributed data sources by dynamically optimizing the retrieval strategy as it is carried out. These mobile agents carry with them data-processing code that can be executed at the remote site, thus reducing the size of data returned by the agent. When all mobile agents have returned, ACQUIRE filters and merges the retrieved data and presents the results to the user. While the system is still very much a work in progress, current validation experiments on simulated NASA Distributed Active Archive Centers (DAACs) have demonstrated that complex queries can be effectively decomposed and retrieved by this approach.

The Nimbus 7 LIMS version 6 radiance conditioning and temperature retrieval methods and results

The Nimbus 7 Limb Infrared Monitor of the Stratosphere (LIMS) experiment operated for just over 7 months in 1978/1979, as planned. Its version 5 (V5) dataset was archived for public use in 1983. Subsequently, several important improvements were realized about the LIMS instrument, its forward radiance model, and the line parameters (from HITRAN 92 or 96) for the gases that emit in its broadband spectral channels. In addition, the orbital attitude knowledge for LIMS was refined by comparing the slopes of the observed radiance profiles from its two CO 2 channels within the upper stratosphere through lower mesosphere. This approach also overcomes the usual requirement for absolute radiance calibrations in flight. The band model approximations employed for CO 2 and interfering O 3 and based on HITRAN for the 15-μm region are adequate for accurate retrievals of temperature versus pressure profiles T( p). Consequently, a reprocessed or Version 6 (V6) profile dataset was generated using these improved radiance conditioning methods (or Level 1) and the T( p) retrieval algorithm (or Level 2), and those updates are described briefly. In particular, the approach for V6 used all the radiance data ( 0.375 km vertical spacing), and the V6 retrievals are based on all the measured radiance profiles along an orbit. Precision and accuracy of the V6 T( p) data are improved over that for V5, most noticeably in the mesosphere. Profiles of geopotential height have also been generated as part of the V6 dataset from which one can analyze its horizontal gradients for the wind fields and higher dynamical quantities, rather than mapping the temperature fields first. The entire LIMS V6 profile dataset was archived at the NASA Goddard Distributed Active Archive Center (DAAC) in September 2002. The high quality of these V6 data demonstrates more fully the promise of the broadband, limb-infrared remote sensing technique for operational measurements of middle atmosphere T( p) from Earth-orbiting satellites.

Aqua: an Earth-Observing Satellite mission to examine water and other climate variables

Aqua is a major satellite mission of the Earth Observing System (EOS), an international program centered at the U.S. National Aeronautics and Space Administration (NASA). The Aqua satellite carries six distinct Earth-observing instruments to measure numerous aspects of Earth's atmosphere, land, oceans, biosphere, and cryosphere, with a concentration on water in the Earth system. Launched on May 4, 2002, the satellite is in a Sun-synchronous orbit at an altitude of 705 km, with a track that takes it north across the equator at 1:30 p.m. and south across the equator at 1:30 a.m. All of its Earth-observing instruments are operating, and all have the ability to obtain global measurements within two days. The Aqua data will be archived and available to the research community through four Distributed Active Archive Centers (DAACs).

MODIS snow-cover products

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. The MODIS snow-mapping algorithms are automated, which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map, which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at 0.05° resolution, with both daily and 8-day composite products. Each pixel of the daily CMG contains fraction of snow cover from 40% to 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02% to 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Terrestrial net primary productivity – A brief history and a new worldwide database

Consistent data on terrestrial net primary productivity (NPP) are urgently needed to constrain model estimates of carbon fluxes and hence to refine our understanding of ecosystem responses to climate change. The NPP data have been collected in a coordinated manner for the past 30 years, but comprehensive summaries are rare. We report on the development and availability of a global NPP database that is suitable for modeling of the terrestrial carbon cycle at global and regional scales, for validation of remote sensing data, and for other applications. These data were obtained from the literature on ecophysiological field work and from detailed consultation with the scientific community. Data on NPP, biomass, and associated environmental variables are now publicly available for 53 detailed study sites, of which more than half have data for belowground biomass or biomass dynamics. Aboveground NPP ranges from 35 to 2320 g m–2a–1 (dry matter) and total NPP from 182 to 3538 g m–2a–1. Well-known but previously unobtainable compilations of data, such as the "Osnabrück Data Set" and the International Biological Program (IBP) Woodlands Data Set, are also incorporated in this database. Preliminary exploration of relationships between NPP and mean annual precipitation and temperature suggests that the new 53-site data collection, as well as the Osnabrück and IBP data, are all consistent with the historic "Miami" statistical model. These data are available from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for biogeochemical dynamics (see http://www.daac.ornl.gov/NPP/).Key words: net primary productivity, grasslands, forests, biogeochemical dynamics, global carbon cycle, model validation.

Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database

The National Oceanic and Atmospheric Administration (NOAA)/NASA Oceans Pathfinder sea surface temperature (SST) data are derived from measurements made by the advanced very high resolution radiometers (AVHRRs) on board the NOAA 7, 9, 11, and 14 polar orbiting satellites. All versions of the Pathfinder SST algorithm are based on the NOAA/National Environmental Satellite Data and Information Service nonlinear SST operational algorithm (NLSST). Improvements to the NLSST operational algorithm developed by the Pathfinder program include the use of monthly calibration coefficients selected on the basis of channel brightness temperature difference (T4–T5). This channel difference is used as a proxy for water vapor regime. The latest version (version 4.2) of the Pathfinder processing includes the use of decision trees to determine objectively pixel cloud contamination and quality level (0–7) of the SST retrieval. The 1985–1998 series of AVHRR global measurements has been reprocessed using the Pathfinder version 4.2 processing protocol and is available at various temporal and spatial resolutions from NASA's Jet Propulsion Laboratory Distributed Active Archive Center. One of the highlights of the Pathfinder program is that in addition to the daily global area coverage fields, a matchup database of coincident in situ buoy and satellite SST observations also is made available for independent algorithm development and validation.

The EOS Prototype Validation Exercise (PROVE) at Jornada: Overview and Lessons Learned

The Earth Observing System (EOS) instrument teams must validate the operational products they produce from the Terra spacecraft data. As a pilot for future validation activities, four EOS teams (MODIS, MISR, ASTER, and Landsat-7) and community experts conducted an 11-day field campaign in May 1997 near Las Cruces, NM. The goals of the Prototype Validation Exercise (PROVE) included (1) gaining experience in the collection and use of field data for EOS product validation; (2) developing coordination, measurement, and data-archiving protocols; and (3) compiling a synoptic land and atmospheric data set for testing algorithms. PROVE was held at the USDA-Agricultural Research Service's (ARS) Jornada Experimental Range, an expansive desert plateau hosting a complex mosaic of grasses and shrubs. Most macroscopic variables affecting the radiation environment were measured with ground, air-borne (including AVIRIS and laser altimeter), and space-borne sensors (including AVHRR, Landsat TM, SPOT, POLDER, and GOES). The Oak Ridge Distributed Active Archive Center (DAAC) then used campaign data sets to prototype Mercury, its Internet-based data harvesting and distribution system. This article provides general information about PROVE and assesses the progress made toward the campaign goals. Primary successes included the rapid campaign formulation and execution, measurement protocol development, and the significant collection, reduction, and sharing of data among participants. However, the PROVE data were used primarily for arid-land research and model validation rather than for validating satellite products, and the data were slow to reach the DAAC and hence public domain. The lessons learned included: (1) validation campaigns can be rapidly organized and implemented if there are focused objectives and on-site facilities and expertise; (2) data needs, organization, storage, and access issues must be addressed at the onset of campaign planning; and (3) the end-to-end data collection, release, and publication environment may need to be readdressed by program managers, funding agencies, and journal editors if rapid and comprehensive validation of operational satellite products is to occur.

OSNABRÜCK NET PRIMARY PRODUCTIVITY DATA SET

An extensive compilation of nearly 700 field estimates of net primary productivity of natural and agricultural ecosystems worldwide was synthesized in Germany in the 1970s and early 1980s. The literature cited dates from 1869 to 1982, with the majority of references from the 1960s and 1970s. Although the Osnabruck data set has not been updated since the 1980s, it represents a wealth of information for use in model development and validation. This data paper documents the development of this data set, its contents, and its recent availability on the Internet, both in Ecological Archives on the Ecological Society of America's web site, and in a “mirror” copy at the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics. The version presented here (version 2.0) incorporates amendments suggested by data users over the past two years. Caution is advised in using these data, which necessarily include assumptions that may not be universally applicable to all study sites and records. Nevertheless, we consider these “Class III” data to be useful, even though they may not meet the “Gold Standards” which are being developed for this type of data (mainly because the documentation may be incomplete, not because the numbers themselves are of poor quality).

MISR photogrammetric data reduction for geophysical retrievals

The theoretical concept, based on modern photogrammetric methods, underlying the design of the Multi-angle Imaging SpectroRadiometer (MISR) science data processing system, responsible for the autonomous and continuous georectification of multiangle imagery, is the subject of this paper. The algorithm partitions effort between the MISR Science Computing Facility and the Earth Observing System (EOS) Distributed Active Archive Center (DAAC) in a way that minimizes the amount of processing required at the latter location to rectify and map project remotely sensed data online, as it comes from the instrument. The algorithm deals with the following issues: (1) removal of the errors introduced by inaccurate navigation and attitude data; (2) removal of the distortions introduced by surface topography; (3) attainment of a balance between limited hardware resources, huge data volume and processing requirements, and autonomous and nonstop aspects of the production system.

Stratospheric Aerosol and Gas Experiment II CD-ROM Atlas of Global Monthly Aerosols, Ozone, NO2, Water, Vapor, and Relative Humitidy (1985–1993)

Abstract Individual profile measurements from the Stratospheric Aerosol and Gas Experiment II (SAGE II) instrument aboard the Earth Radiation Budget Satellite have been used to create latitude-longitude maps of monthly mean aerosols, ozone, water vapor, relative humidity and NO2 on up to 14 standard pressure levels. Color maps and gridded data from 1985 through 1993 are available on a CD-ROM that can be obtained from the Distributed Active Archive Center (DAAC) at NASA Langley Research Center. Examples are shown of the ozone hole and related phenomenon, and aerosol loading associated with the Mount Pinatubo volcano. By presenting the data in this visible and easy-to-use format, we hope that it will reach a larger community and result in better understanding of atmospheric aerosols and trace gases. Similar presentations will be generated for SAGE III data, expected to begin in 1998 as part of the Earth Observing System (EOS).