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Standard products from the five sensors on NASA's Earth Observing System's (EOS) Terra satellite are being used world-wide for earth science research and applications. This paper describes the evolution of the Terra data systems over the last decade in which the distributed systems that produce, archive and distribute high quality Terra data products were scaled by two orders of magnitude.
The Atmospheric Sciences Data Center (ASDC) at NASA s Langley Research Center houses over 700 data sets related to Earth s radiation budget, clouds, aerosols and tropospheric chemistry. These data sets are produced to increase academic understanding of the natural and anthropogenic perturbations that influence global climate change. The Mentoring and inquirY using NASA Data on Atmospheric and earth science for Teachers and Amateurs (MY NASA DATA) project has been established to systematically support educational activities at all levels of formal and informal education by reducing these large data holdings to microsets that will be easily explored and understood by the K-12 and the amateur scientist communities
Ground processing of data from the Multi-angle Imaging SpectroRadiometer (MISR) instrument, part of NASA's Earth Observing System (EOS), uses new and unique science algorithms because MISR's multi-camera observing method has not previously been used anywhere.
The applications of multisensor satellites for earth sciences studies in the next two decades are discussed in terms of instrumentation and expected technology developments. Passive microwave and IR sounders with better resolution than currently available are forecast for the mid- to late-1980's, enabling improved understanding of the coupling between the atmosphere, ocean, land, and the biosphere. The global troposphere will be examined for sources, sinks, and fluxes of its chemistry, and interactions among radiation, chemistry, and dynamics in the upper atmosphere. The Landsat 4 Thematic Mapper, along with imagers, radars, and spectrometers carried aloft on the Shuttle are being used and tested to characterize the relative utility of various parts of the electromagnetic spectrum. Hydrologic studies with spacecraft imagery have led to the development of moisture models that will give improved predictive ability to soil moisture, snow cover variations, and the distribution and magnitude of global precipitation and evapotranspiration.
Entering the new millennium, there is no doubt that scientists expand greatly their scientific knowledge of the Earth system by utilizing unique capabilities from the vantage points of space. These global satellite observations include the NASA/NOAA Pathfinder and other ongoing data analysis projects, the Earth Observing System (EOS) program, the Earth System Science Pathfinders (ESSP) small research satellite missions, multi-agency planning for the National Polar-orbiting Operational Environmental Satellite (NPOESS), and other international satellite missions. However, using satellite remotely sensed data alone cannot explore fully the physical processes and energetic balance involved in our changing climate. To close the loop, the ground-based remote sensing and airborne in situ measurements are required. This talk provides an overview of the general strategy of Earth remote sensing for a column closure approach and discusses necessary instrumentation.
We present a straightforward and low-cost approach to publish variables archived at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) to the Earth System Grid Federation (ESGF). An ESGF publication requires a single standard-name variable aggregated over time to facilitate data inter-comparison. It also contains significant metadata to enable searching in ESGF. We look up standard names on high demand in ESGF search history, and using OPeNDAP and NcML technologies we aggregate the corresponding variables available in the GES DISC archive with augmented metadata required by CMIP6 and obs4MIPs Data Specification version 2.1. At this writing 10 variables from a standard product of the Atmospheric Infrared Sounder along with the Tech Notes are published in ESGF by NASA Center for Climate Simulation (NCCS). Users can view, analyze, and subset remotely, and download these aggregated variables via links in any ESGF node after searching. We plan to work on and publish more variables and data from different NASA missions and experiments in our archive.
The NASA's Earth Observing System Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) has continued to operate with satisfactory performance since its launch in May 2002, exceeding its nominal six-year design lifetime. Its continuous Earth observations have been used to generate many science data products for studies of the Earth's, system. MODIS has 36 spectral bands; 20 reflective solar bands and 16 thermal emissive bands (TEBs). All TEB observations are made at 1-km nadir spatial resolution with spectral wavelengths from 3.7 to 14.4 pm. Primary applications of MODIS TEB 'include surface, cloud, and atmospheric temperatures, water vapor, and cloud top altitude. MODIS TEB on-orbit calibration uses a quadratic algorithm with its calibration coefficients derived using an onboard blackbody (BB). This paper will present Aqua MODIS TEB on-orbit calibration, characterization, and performance over its six-year mission. Examples of instrument thermal behavior, BB temperature stability, detector short-term stability, and changes in long-term response (or system gain) will be presented. Comparisons will also be made with Terra MODIS, launched in December 1999. On-orbit results show that Aqua MODIS and its focal plane temperatures have behaved normally. BB temperature has remained extremely stable with typical scan -to-scan variations of less than +/-0.15 mK. Most TEB detectors continue to exceed their specified signal-to-noise ratio requirements, exhibiting excellent short-term stability and calibration accuracy. Excluding a few noisy detectors, either identified prelaunch or occurring postlaunch, on-orbit changes in TEB responses have been less than 0.5% on an annual basis. By comparison; the overall Aqua TEB performance has been better than that of Terra MODIS,
The research proposed in this contract concerning investigations of existing and planned Earth Science and Applications Division (ESAD) data management systems and research into utilities for the access and display of scientific data products was completed. A summary of this work is provided.
The Earth Observing One Spacecraft is currently flying The Autonomous Sciencecraft Experiment (ASE) - onboard autonomy software to improve science return. The ASE software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. ASE includes software systems that perform science data analysis, mission planning, and run-time robust execution. In this article we describe the autonomy flight software and how it enables a new paradigm of autonomous science and mission operations.
The Monthly TOA/Surface Averages (SRBAVG) product contains a month of space and time averaged Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SRBAVG is also produced for combinations of scanner instruments. The monthly average regional flux is estimated using diurnal models and the 1-degree regional fluxes at the hour of observation from the CERES SFC product. A second set of monthly average fluxes are estimated using concurrent diurnal information from geostationary satellites. These fluxes are given for both clear-sky and total-sky scenes and are spatially averaged from 1-degree regions to 1-degree zonal averages and a global average. For each region, the SRBAVG also contains hourly average fluxes for the month and an overall monthly average. The cloud properties from SFC are column averaged and are included on the SRBAVG. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-02-01; Stop_Date=2000-03-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree; Horizontal_Resolution_Range=100 km - < 250 km or approximately 1 degree - < 2.5 degrees; Temporal_Resolution=1 month; Temporal_Resolution_Range=Monthly - < Annual].
The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The Terrain data are re-projected to the terrain altitude. In this product, surface data from all cameras will appear in the same geographic location. Thus, this product is the primary input to Level 2 aerosol/surface processing, which requires co-registration of the L1B2 imagery at the surface. Clouds will still be displaced due to their elevation above the surface, but this time with respect to the terrain rather than the ellipsoid. (The mountain location T is now assigned the geographic location at T, and the Cloud at F appears at the geographic location T.) In Level 2 aerosol/surface processing, algorithms are applied to screen out the clouds. Terrain data only exist for MISR blocks containing some land. [Location=GLOBAL LAND] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=563.2 km (cross-track); Longitude_Resolution=140.8 km (along-track).; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=about 15 orbits/day].
The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The Terrain data are re-projected to the terrain altitude. In this product, surface data from all cameras will appear in the same geographic location. Thus, this product is the primary input to Level 2 aerosol/surface processing, which requires co-registration of the L1B2 imagery at the surface. Clouds will still be displaced due to their elevation above the surface, but this time with respect to the terrain rather than the ellipsoid. (The mountain location T is now assigned the geographic location at T, and the Cloud at F appears at the geographic location T.) In Level 2 aerosol/surface processing, algorithms are applied to screen out the clouds. Terrain data only exist for MISR blocks containing some land. [Location=GLOBAL LAND] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=563.2 km (cross-track); Longitude_Resolution=140.8 km (along-track).; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=about 15 orbits/day].
The primary objective of this project is to answer the question: "Can existing, linked infrastructures be used to predict the onset of drought months in advance?" Based on our work, the answer to this question is "yes" with the qualifiers that skill depends on both lead-time and location, and especially with the associated teleconnections (e.g., ENSO, Indian Ocean Dipole) active in a given region season. As part of this work, we successfully developed a prototype drought early warning system based on existing/mature NASA Earth science components including the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5) forecasting model, the Land Information System (LIS) land data assimilation software framework, the Catchment Land Surface Model (CLSM), remotely sensed terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) and remotely sensed soil moisture products from the Aqua/Advanced Microwave Scanning Radiometer - EOS (AMSR-E). We focused on a single drought year - 2011 - during which major agricultural droughts occurred with devastating impacts in the Texas-Mexico region of North America (TEXMEX) and the Horn of Africa (HOA). Our results demonstrate that GEOS-5 precipitation forecasts show skill globally at 1-month lead, and can show up to 3 months skill regionally in the TEXMEX and HOA areas. Our results also demonstrate that the CLSM soil moisture percentiles are a goof indicator of drought, as compared to the North American Drought Monitor of TEXMEX and a combination of Famine Early Warning Systems Network (FEWS NET) data and Moderate Resolution Imaging Spectrometer (MODIS)'s Normalizing Difference Vegetation Index (NDVI) anomalies over HOA. The data assimilation experiments produced mixed results. GRACE terrestrial water storage (TWS) assimilation was found to significantly improve soil moisture and evapotransportation, as well as drought monitoring via soil moisture percentiles, while AMSR-E soil moisture assimilation produced marginal benefits. We carried out 1-3 month lead-time forecast experiments using GEOS-5 forecasts as input to LIS/CLSM. Based on these forecast experiments, we find that the expected skill in GEOS-5 forecasts from 1-3 months is present in the soil moisture percentiles used to indicate drought. In the case of the HOA drought, the failure of the long rains in April appears in the February 1, March 1 and April 1 initialized forecasts, suggesting that for this case, drought forecasting would have provided some advance warning about the drought conditions observed in 2011. Three key recommendations for follow-up work include: (1) carry out a comprehensive analysis of droughts observed over the entire period of record for GEOS-5 forecasts; (2) continue to analyze the GEOS-5 forecasts in HOA stratifying by anomalies in long and short rains; and (3) continue to include GRACE TWS, Soil Moisture/Ocean Salinity (SMOS) and the upcoming NASA Soil Moisture Active/Passive (SMAP) soil moisture products in a routine activity building on this prototype to further quantify the benefits for drought assessment and prediction.
Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA's Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-80s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth Sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, still quite a number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, in hope that may help users better utilize the information in their research and applications.
The Earth Observing System (EOS), the centerpiece of NASA's Earth science program, is a suite of spacecraft and interdisciplinary science investigations dedicated to advancing our understanding of global change. The flagship EOS satellite, Terra (formerly EOS AM-1), scheduled for launch in July 1999, will provide key measurements of the physical and radiative properties of clouds; air-land and air-sea exchanges of energy, carbon, and water; trace gases; and volcanoes. Flying in formation with Terra, Landsat 7 will make global high spatial resolution measurements of land surface and surrounding coastal regions. Other upcoming EOS missions and instruments include QuikSCAT, to collect sea surface wind data; the Stratospheric Gas and Aerosol Experiment (SAGE III), to create global profiles of key atmospheric gases; and the Active Cavity Radiometer Irradiance Monitors (ACRIM) to measure the energy output of the Sun. The second of the major, multi-instrument EOS platforms, PM-1, is scheduled for launch in 2000. Interdisciplinary research projects sponsored by EOS use specific Earth science data sets for a broader investigation into the function of Earth systems. Current EOS research spans a wide range of sciences, including atmospheric chemistry, hydrology, land use, and marine ecosystems. The EOS program has been managed since 1990 by the Goddard Space Flight Center in Greenbelt, Md., for NASA's Office of Earth Science in Washington, D. C. Additional information on the program can be found on the EOS Project Science Office Web site (http://eospso.gsfc.nasa.gov).
Several current NASA programs such as the EOSDIS Core System (ECS) have data processing and data management requirements that call for an integrated planning and scheduling capability. In this paper, we describe the experience of applying advanced scheduling technology operationally, in terms of what was accomplished, lessons learned, and what remains to be done in order to achieve similar successes in ECS and other programs. We discuss the importance and benefits of advanced scheduling tools, and our progress toward realizing them, through examples and illustrations based on ECS requirements. The first part of the paper focuses on the Data Archive and Distribution (DADS) V0 Scheduler. We then discuss system integration issues ranging from communication with the scheduler to the monitoring of system events and re-scheduling in response to them. The challenge of adapting the scheduler to domain-specific features and scheduling policies is also considered. Extrapolation to the ECS domain raises issues of integrating scheduling with a product-generation planner (such as PlaSTiC), and implementing conditional planning in an operational system. We conclude by briefly noting ongoing technology development and deployment projects being undertaken by HTC and the ISTB.
The ability of large information systems to support the changing data requirements of active science projects is being tested in a NASA collaborative study. This paper briefly profiles both the active science project and the large information system involved in this effort and offers some observations about the effectiveness of the project support. This is followed by lessons that are important for those participating in large information systems that need to support active science projects or that make available the valuable data produced by these projects. We learned in this work that it is difficult for a large information system focused on long term data management to satisfy the requirements of an on-going science project. For example, in order to provide the best service, it is important for all information system staff to keep focused on the needs and constraints of the scientists in the development of appropriate services. If the lessons learned in this and other science support experiences are not applied by those involved with large information systems of the EOS (Earth Observing System) era, then the final data products produced by future science projects may not be robust or of high quality, thereby making the conduct of the project science less efficacious and reducing the value of these unique suites of data for future research.
The Office for Remote Sensing of Earth Resources (ORSER) of the Space Science and Engineering Laboratory (SSEL) at The Pennsylvania State University has developed an extensive operational system for processing and analyzing ERTS-1 and similar multispectral data. Specific results obtained by using this system include a study of land use, discrimination between types of forest resources and vegetation, detection of previously unknown geologic faults and correlation of these with known mineral deposits and ground water, mapping of mine spoils in the anthracite region of eastern Pennsylvania, mapping of strip mines and acid mine drainage in Central Pennsylvania, agricultural land use mapping, and detection of gypsy moth infestation.