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NASA NTRS2011

Graphics Processing Unit (GPU) Acceleration of the Goddard Earth Observing System Atmospheric Model

The Goddard Earth Observing System 5 (GEOS-5) is the atmospheric model used by the Global Modeling and Assimilation Office (GMAO) for a variety of applications, from long-term climate prediction at relatively coarse resolution, to data assimilation and numerical weather prediction, to very high-resolution cloud-resolving simulations. GEOS-5 is being ported to a graphics processing unit (GPU) cluster at the NASA Center for Climate Simulation (NCCS). By utilizing GPU co-processor technology, we expect to increase the throughput of GEOS-5 by at least an order of magnitude, and accelerate the process of scientific exploration across all scales of global modeling, including: The large-scale, high-end application of non-hydrostatic, global, cloud-resolving modeling at 10- to I-kilometer (km) global resolutions Intermediate-resolution seasonal climate and weather prediction at 50- to 25-km on small clusters of GPUs Long-range, coarse-resolution climate modeling, enabled on a small box of GPUs for the individual researcher After being ported to the GPU cluster, the primary physics components and the dynamical core of GEOS-5 have demonstrated a potential speedup of 15-40 times over conventional processor cores. Performance improvements of this magnitude reduce the required scalability of 1-km, global, cloud-resolving models from an unfathomable 6 million cores to an attainable 200,000 GPU-enabled cores.

Putnam, Williama↗

NASA NTRS2020

Cascading toward a Kilometer‐scale GCM: Impacts of a Scale‐aware Convection Parameterization in the Goddard Earth Observing System GCM.

The NASA Goddard Earth Observing System global model is evaluated through a cascade of simulations with increasing horizontal resolution. This model employs a non-hydrostatic dynamical core and includes a scale-aware, deep convection parameterization (DPCP). 40-daysimulations at six resolutions (100km to 3km) with unvarying model formulation were produced. At the highest resolution, extreme experiments were carried out; one with no DPCP, and one with its scale-awareness eliminated. Simulated precipitation, radiative balance, and atmospheric thermodynamic and dynamical variables are well reproduced with respect to both observational and reanalysis data. As model resolution increases, the convective precipitation smoothly transitions from being mostly produced by the convection parameterization to the cloud microphysics parameterization. However, contrary to current thought, these extreme cases argue for maintaining, to some extent, the scale-aware deep convection parameterization even at 3kmscale, as the run relying solely on explicit grid-scale production of rainfall performs more poorly at this resolution.

Saulo Freitas↗

NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Building Capacity to Apply Earth Observations for Coastal and Marine Decision Making

Part of NASA’s Applied Sciences, the DEVELOP Program builds capacity through 10-week interdisciplinary feasibility studies that identify how NASA Earth observations can be integrated into decision making processes. The program is a dual capacity building effort that fosters programmatic participants (early and transitioning career professionals, students, and recent graduates) to apply Earth observations to partner organizations’ decision making needs. The program conducts 50-60 projects annually, with about 10-15% of those focused on coastal and marine community concerns. These projects explore the use of satellite data and model outputs for measuring and mapping shoreline delineation, barrier island transgression, mangrove health and extent, sedimentation and water quality, and tropical cyclone impacts to coastal regions. This presentation will introduce DEVELOP and a series of project case studies, an overview of the organizations engaged and Earth observations utilized, and lessons learned.

Capacity Building↗

NASA NTRS2005

NASA Earth Observations (NEO): Data Access for Informal Education and Outreach

The NEO (NASA Earth Observations) web space is currently under development with the goal of significantly increasing the demand for NASA remote sensing data while dramatically simplifying public access to georeferenced images. NEO will target the unsophisticated, nontraditional data users who are currently underserved by the existing data ordering systems. These users will include formal and informal educators, museum and science center personnel, professional communicators, and citizen scientists and amateur Earth observers. Users will be able to view and manipulate georeferenced browse imagery and, if they desire, download directly or order the source HDF data from the data provider (e.g., NASA DAAC or science team) via a single, integrated interface. NE0 will accomplish this goal by anticipating users expectations and knowledge level, thus providing an interface that presents material to users in a more simplified manner, without relying upon the jargon/technical terminology that make even the identification of the appropriate data set a significant hurdle. NEO will also act as a gateway that manages users expectations by providing specific details about images and data formats, developing tutorials regarding the manipulation of georeferenced imagery and raw data, links to software tools and ensuring that users are able to get the image they want in the format they want as easily as possible.

Ward, Kevin↗

NASA NTRSDate not supplied

Combining NASA Earth Observations and Commercial Smallsat Data to Inform Localized Decision Making

NASA's Earth Science Division's DEVELOP Program builds capacity in individuals and partner organizations to research the feasibility of using Earth observations for informed environmental decision making. Employing an internship-like model, DEVELOP conducts 10-week long feasibility studies that are focused on decision-making organizations' environmental concerns. These projects use the vantage point of space to address environmental issues across a broad set of themes, including agricultural monitoring, disaster risk and resilience planning, water resource and coastal management, wildfire cartography, health & air quality, and urban development. Following the establishment of NASA's Commercial Smallsat Data Acquisition (CSDA) Program, DEVELOP began adding commercial smallsat data into a subset of its feasibility projects. This presentation will highlight the program's use of CSDA data and its integration with NASA Earth observing fleet data, showcase example use cases, speak to challenges faced by the DEVELOP team in using CSDA data, and the broad array of thematic and topical applications created by DEVELOP teams.

Lisa Tanh↗

NASA NTRS2011

Land and Atmosphere Near-Real-Time Capability for Earth Observing System

The past decade has seen a rapid increase in availability and usage of near-real-time data from satellite sensors. The EOSDIS (Earth Observing System Data and Information System) was not originally designed to provide data with sufficiently low latency to satisfy the requirements for near-real-time users. The EOS (Earth Observing System) instruments aboard the Terra, Aqua and Aura satellites make global measurements daily, which are processed into higher-level 'standard' products within 8-40 hours of observation and then made available to users, primarily earth science researchers. However, applications users, operational agencies, and even researchers desire EOS products in near-real-time to support research and applications, including numerical weather and climate prediction and forecasting, monitoring of natural hazards, ecological/invasive species, agriculture, air quality, disaster relief and homeland security. These users often need data much sooner than routine science processing allows, usually within 3 hours, and are willing to trade science product quality for timely access. While Direct Broadcast provides more timely access to data, it does not provide global coverage. In 2002, a joint initiative between NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration), and the DOD (Department of Defense) was undertaken to provide data from EOS instruments in near-real-time. The NRTPE (Near Real Time Processing Effort) provided products within 3 hours of observation on a best-effort basis. As the popularity of these near-real-time products and applications grew, multiple near-real-time systems began to spring up such as the Rapid Response System. In recognizing the dependence of customers on this data and the need for highly reliable and timely data access, NASA's Earth Science Division sponsored the Earth Science Data and Information System Project (ESDIS)-led development of a new near-real-time system called LANCE (Land, Atmosphere Near-Real-Time Capability for EOS) in 2009. LANCE consists of special processing elements, co-located with selected EOSDIS data centers and processing facilities. A primary goal of LANCE is to bring multiple near-real-time systems under one umbrella, offering commonality in data access, quality control, and latency. LANCE now processes and distributes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) instruments within 3 hours of satellite observation. The Rapid Response System and the Fire Information for Resource Management System (FIRMS) capabilities will be incorporated into LANCE in 2011. LANCE maintains a central website to facilitate easy access to data and user services. LANCE products are extensively tested and compared with science products before being made available to users. Each element also plans to implement redundant network, power and server infrastructure to ensure high availability of data and services. Through the user registration system, users are informed of any data outages and when new products or services will be available for access. Building on a significant investment by NASA in developing science algorithms and products, LANCE creates products that have a demonstrated utility for applications requiring near-real-time data. From lower level data products such as calibrated geolocated radiances to higher-level products such as sea ice extent, snow cover, and cloud cover, users have integrated LANCE data into forecast models and decision support systems. The table above shows the current near-real-time product categories by instrument. The ESDIS Project continues to improve the LANCE system and use the experience gained through practice to seek adjustments to improve the quality and performance of the system. For example, anGC-compliant Web Map Service (WMS) will be added shortly that will allow users to download geo-referenced MODIS images for arbitrary bounding boxes. Further, an OGC-compliant Web Coverage Service (WCS) will be added later this year that will expedite user access to arbitrary data subsets or re-formatted products. AIRS images are now served through WMS and available in multiple formats (PNG, GeoTIFF, KMZ). NASA has established a LANCE User Working Group to steer the development of the system and create a forum for sharing ideas and experiences that are expected to further improve the LANCE capabilities. The LANCE system has proved a success by satisfying the growing needs of the applications and operational communities for land and atmosphere data in near-real-time. NASA's Earth Sciences Division was able to leverage existing science research capabilities to provide the near-real-time community with products and imagery that support monitoring of disasters in a timely manner.

Murphy, Kevin J.↗

NASA NTRS1993

Orbit determination and orbit control for the Earth Observing System (EOS) AM spacecraft

Future NASA Earth Observing System (EOS) Spacecraft will make measurements of the earth's clouds, oceans, atmosphere, land and radiation balance. These EOS Spacecraft will be part of the NASA Mission to Planet Earth. This paper specifically addresses the EOS AM Spacecraft, referred to as 'AM' because it has a sun-synchronous orbit with a 10:30 AM descending node. This paper describes the EOS AM Spacecraft mission orbit requirements, orbit determination, orbit control, and navigation system impact on earth based pointing. The EOS AM Spacecraft will be the first spacecraft to use the TDRSS Onboard Navigation System (TONS) as the primary means of navigation. TONS flight software will process one-way forward Doppler measurements taken during scheduled TDRSS contacts. An extended Kalman filter will estimate spacecraft position, velocity, drag coefficient correction, and ultrastable master oscillator frequency bias and drift. The TONS baseline algorithms, software, and hardware implementation are described in this paper. TONS integration into the EOS AM Spacecraft Guidance, Navigation, and Control (GN&C) System; TONS assisted onboard time maintenance; and the TONS Ground Support System (TGSS) are also addressed.

Herberg, Joseph R.↗

NASA NTRS2018

Impacts of NASA's Earth Observations on Subseasonal and Seasonal Forecasts

NASA's suite of Earth-observing satellites provides a unique view of many processes on Earth, with relevance on timescales ranging from hours to weeks and even years. NASA's observations span all parts of the Earth system: atmospheric, ocean, land and cryosphere, and include physical, chemical and biological components. This presentation explores use of NASA observations in extended-range prediction, from many days to months, using the Goddard Earth Observing System (GEOS) assimilation and predictive modeling capabilities. The skill of a weather forecast is linked to the fidelity of the initialization process (data assimilation) and the realistic representation of "fast" physical processes in the model. At longer time horizons, the slower "feedback" processes in the Earth System begin to take a more prominent role in the accuracy of the forecast. Simultaneously, forecast-skill attribution transitions away from feature-based metrics that emphasize smaller scales (e.g., representations of fronts and vortices) to metrics that emphasize the statistical distributions of large-scale features (e.g., ENSO diagnostics and teleconnections). This presentation will summarize studies performed using the GEOS-S2S (subseasonal to seasonal) system that explore the impacts of NASA observations on the fidelity of the forecasts. The GEOS-S2S system is configured for the atmosphere-ocean-land-ice model and is initialized using in-situ and space-based observations, including atmospheric aerosols and ozone which are not typically analyzed in such systems. The GEOS-S2S model routinely includes aerosol feedbacks, which systematically impact the realism of the forecasts, providing a first example of how suitable NASA observations impact the performance of the GEOS-S2S system. Studies in which a stratospheric chemistry module is activated in the GEOS-S2S system allow the impacts of ozone radiative feedbacks to be isolated. Space-based observations of sea-surface temperature and altimetry are routinely analyzed for the initialization of the GEOS-S2S system; recent advances allow the use of NASA's sea-surface salinity data, which are shown to impact the long-range skill of the forecasts.

Pawson, Steven↗

NASA NTRS2019

The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response

Information is a critical resource in disaster response scenarios. Data regarding the geographic extent, severity, and socioeconomic impacts of a disaster event can help guide emergency responders and relief operations, particularly when delivered within hours of data acquisition. Information from remote observations provides a valuable tool for assessing conditions "on the ground" more quickly and efficiently. Here, we evaluate the social value of a near real-time flood impact system using a disaster response case study, and quantify the Value of Information (VOI) of satellite-based observations for rapid response using a hypothetical flooding disaster in Bangkok, Thailand. MODIS imagery from NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system is used to produce operational estimates of inundation depths and economic damages.These rapid Earth observations are coupled with a decision-analytical model to inform decisions on emergency vehicle routing. Emergency response times from vehicles routed using flood damage data are compared with baseline routes without the benefit of advance information on road conditions. Our results illustrate how the application of near real-time Earth observations can improve the response time and reduce potential encounters with flood hazards when compared with baseline routing strategies. Results indicate a potential significant economic benefit (i.e.,millions of dollars) from applying near real-time Earth observations for improved flood disaster response and management.

emergency response↗

NASA NTRS1987

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements.

Source record↗

NASA NTRS1998

Statistical Treatment of Earth Observing System Pyroshock Separation Test Data

The Earth Observing System (EOS) AM-1 spacecraft for NASA's Mission to Planet Earth is scheduled to be launched on an Atlas IIAS vehicle in June of 1998. One concern is that the instruments on the EOS spacecraft are sensitive to the shock-induced vibration produced when the spacecraft separates from the launch vehicle. By employing unique statistical analysis to the available ground test shock data, the NASA Lewis Research Center found that shock-induced vibrations would not be as great as the previously specified levels of Lockheed Martin. The EOS pyroshock separation testing, which was completed in 1997, produced a large quantity of accelerometer data to characterize the shock response levels at the launch vehicle/spacecraft interface. Thirteen pyroshock separation firings of the EOS and payload adapter configuration yielded 78 total measurements at the interface. The multiple firings were necessary to qualify the newly developed Lockheed Martin six-hardpoint separation system. Because of the unusually large amount of data acquired, Lewis developed a statistical methodology to predict the maximum expected shock levels at the interface between the EOS spacecraft and the launch vehicle. Then, this methodology, which is based on six shear plate accelerometer measurements per test firing at the spacecraft/launch vehicle interface, was used to determine the shock endurance specification for EOS. Each pyroshock separation test of the EOS spacecraft simulator produced its own set of interface accelerometer data. Probability distributions, histograms, the median, and higher order moments (skew and kurtosis) were analyzed. The data were found to be lognormally distributed, which is consistent with NASA pyroshock standards. Each set of lognormally transformed test data produced was analyzed to determine if the data should be combined statistically. Statistical testing of the data's standard deviations and means (F and t testing, respectively) determined if data sets were significantly different at a 95-percent confidence level. If two data sets were found to be significantly different, these families of data were not combined for statistical purposes. This methodology produced three separate statistical data families of shear plate data. For each population, a P99.1/50 (probability/confidence) per-separation-nut firing level was calculated. By using the binomial distribution, Lewis researchers determined that this pernut firing level was equivalent to a P95/50 per-flight confidence level. The overall envelope of the per-flight P95/50 levels led to Lewis' recommended EOS interface shock endurance specification. A similar methodology was used to develop Lewis' recommended EOS mission assurance levels. The available test data for the EOS mission are significantly larger than for a normal mission, thus increasing the confidence level in the calculated expected shock environment. Lewis significantly affected the EOS mission by properly employing statistical analysis to the data. This analysis prevented a costly requalification of the spacecraft's instruments, which otherwise would have been exposed to significantly higher test levels.

McNelis, Anne M.↗

NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗

NASA NTRS1985

The Earth Observing System - A multidisciplinary system for the long-term acquisition of earth science data from space

The development of the Earth Observing System (EOS) requirements is examined. An envisioned, EOS is to be NASA's multidisciplinary approach to acquisition of earth science data in the 1990s. The rationale and assumptions for the EOS are presented, together with a discussion of anticipated operations and use environments. Planned instrumentation concepts are discussed, involving a broad array of multiuse sensors operating across the electromagnetic spectrum from the ultraviolet to the microwave and including both active and passive techniques. Concepts for the near-polar orbiting observatory system, and essentially permanent man-serviced and -tended facility, are presented, along with concepts for information capture, processing and distribution and for common data-base referencing of acquired data. Milestones are indicated that are to be passed before a commitment to program execution can be made later in this decade.

Broome, D. R., Jr.↗

NASA NTRS2008

NASA Earth Observations Informing Renewable Energy Management and Policy Decision Making

The NASA Applied Sciences Program partners with domestic and international governmental organizations, universities, and private entities to improve their decisions and assessments. These improvements are enabled by using the knowledge generated from research resulting from spacecraft observations and model predictions conducted by NASA and providing these as inputs to the decision support and scenario assessment tools used by partner organizations. The Program is divided into eight societal benefit areas, aligned in general with the Global Earth Observation System of Systems (GEOSS) themes. The Climate Application of the Applied Sciences Program has as one of its focuses, efforts to provide for improved decisions and assessments in the areas of renewable energy technologies, energy efficiency, and climate change impacts. The goals of the Applied Sciences Program are aligned with national initiatives such as the U.S. Climate Change Science and Technology Programs and with those of international organizations including the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). Activities within the Program are funded principally through proposals submitted in response to annual solicitations and reviewed by peers.

Eckman, Richard S.↗

NASA NTRS1998

Earth Observing System: Science Objectives and Challenges

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. In this presentation I will describe the key areas of scientific uncertainty in understanding climate and global change, and follow that with a description of the EOS goals, objectives, and scientific research elements that comprise the program (instrument science teams and interdisciplinary investigations). Finally, I will describe how scientists and policy makers intend to use EOS data to improve our understanding of key global change uncertainties, such as: (i) clouds and radiation, including fossil fuel and natural emissions of sulfate aerosol and its potential impact on cloud feedback, (ii) man's impact on ozone depletion, with examples of ClO and O3 obtained from the UARS satellite during the Austral Spring, and (iii) volcanic eruptions and their impact on climate, with examples from the eruption of Mt. Pinatubo.

King, Michael D.↗

NASA NTRS1999

Earth Observing System: Science Objectives and Challenges

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. In this presentation we review the key areas of scientific uncertainty in understanding climate and global change, and follow that with a description of the EOS goals, objectives, and scientific research elements that comprise the program (instrument science teams and interdisciplinary investigations). Finally, I will describe how scientists and policy makers intend to use EOS data improve our understanding of key global change uncertainties, such as: (i) clouds and radiation, including fossil fuel and natural emissions of sulfate aerosol and its potential impact on cloud feedback, (ii) man's impact on ozone depletion, with examples of ClO and O3 obtained from the UARS satellite during the Austral Spring, and (iii) volcanic eruptions and their impact on climate, with examples from the eruption of Mt. Pinatubo.

King, Michael D.↗

NASA NTRSDate not supplied

A Scenario-Based Approach to Assess Continuity Gaps in Earth Observations

Decision analysis processes outlined in systems engineering references, such as the Systems Engineering Body of Knowledge [1] or the NASA Systems Engineering Handbook [2], recommend following a series of steps to support decision-making for engineering applications. These steps are consistent across the literature and typically involve defining objectives, defining relevant criteria against which candidate alternatives can be assessed, selecting an evaluation method, assessing alternatives, and making a recommendation based on this assessment. These approaches are well-suited to assess systems for which sets of common objectives and constraints can be identified. A vast body of literature describes their application to numerous engineering problems, and many methods have been developed since the middle of the 20th century to support the assessment of candidate alternatives for such systems [3, 4, 5, 6]. These approaches, however, show some limitations when decisions pertain to systems of systems. These collections of individual systems typically do not share a common set of objectives and constraints, tend to be highly complex, and the definition of assessable, high-value candidate alternatives poses a challenge due to interdependencies. The formulation, development, operation, and funding of Earth observing missions are such that gaps may occur between missions, therefore impacting the continuity of measurements. These Earth observing mission architectures constitute systems of systems for which a common set of objectives and constraints can be challenging to define. When tasked with assessing gaps that may occur for a series of spaceborne missions that address similar science parameters, the authors therefore proposed to depart from the traditional objective- and criteria-based approach and instead adopted a scenario-based approach. This approach does not require a prioritized set of common objectives; rather, it assesses the impact of possible decisions on the system-of-systems and characterizes its possible future states. It also offers the flexibility to adjust assumptions, update inputs, and refine supporting models over time, while enabling the rapid, early identification of challenges and key decision points. By simulating the impact of potential decisions on the entirety of the system-of-systems, the approach enables the synchronization of multiple decisions that typically occur at the level of the individual system. This paper provides an overview of the approach that was developed to assess continuity gaps for a series of operational and planned spaceborne Earth observing missions, a description of its application to the problem at hand, and a discussion of currently known limitations.

scenario earth observations continuity gaps↗

NASA NTRSDate not supplied

A Scenario-Based Approach to Assess Continuity Gaps in Earth Observations

scenario earth observations continuity gaps↗