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NASA NTRS2016

The Earth Observing System (EOS) Ground System: Leveraging an Existing Operational Ground System Infrastructure to Support New Missions

The Earth Observer System (EOS) was officially established in 1990 and went operational in December 1999 with the launch of its flagship spacecraft Terra. Aqua followed in 2002 and Aura in 2004. All three spacecraft are still operational and producing valuable scientific data. While all are beyond their original design lifetime, they are expected to remain viable well into the 2020s. The EOS Ground System is a multi-mission system based at NASA Goddard Space Flight Center that supports science and spacecraft operations for these three missions. Over its operational lifetime to date, the EOS Ground System has evolved as needed to accommodate mission requirements. With an eye towards the future, several updates are currently being deployed. Subsystem interconnects are being upgraded to reduce data latency and improve system performance. End-of-life hardware and operating systems are being replaced to mitigate security concerns and eliminate vendor support gaps. Subsystem hardware is being consolidated through the migration to Virtual Machine based platforms. While mission operations autonomy was not a design goal of the original system concept, there is an active effort to apply state-of-the-art products from the Goddard Mission Services Evolution Center (GMSEC) to facilitate automation where possible within the existing heritage architecture. This presentation will provide background information on the EOS ground system architecture and evolution, discuss latest improvements, and conclude with the results of a recent effort that investigated how the current system could accommodate a proposed new earth science mission.

Earth Science Mission Operations (ESMO)↗

NASA NTRS1992

The Earth Observing System

The restructuring of the NASA Earth Observing System (EOS), designed to provide comprehensive long term observations from space of changes occurring on the Earth from natural and human causes in order to have a sound scientific basis for policy decisions on protection of the future, is reported. In response to several factors, the original program approved in the fiscal year 1991 budget was restructured and somewhat reduced in scope. The resulting program uses three different sized launch vehicles to put six different spacecraft in orbit in the first phase, followed by two replacement launches for each of five of the six satellites to maintain a long term observing capability to meet the needs of global climate change research and other science objectives. The EOS system, including the space observatories, the data and information system, and the interdisciplinary global change research effort, are approved and proceeding. Elements of EOS are already in place, such as the research investigations and initial data system capabilities. The flights of precursor satellite and Shuttle missions, the ongoing data analysis, and the evolutionary enhancements to the integrated Earth science data management capabilities are all important building blocks to the full EOS program.

Shaffer, Lisa Robock↗

NASA NTRS2021

Toward a Global Planetary Boundary Layer Observing System: The NASA PBL Incubation Study Team Report

A global Planetary Boundary Layer (PBL) observing system is urgently needed to address fundamental PBL science questions and societal applications related to weather, climate and air quality. This PBL observing system should optimally combine new space-based observations of the PBL thermodynamic structure with complementary surface-based and suborbital assets, while taking advantage of, and helping improve, modeling and data assimilation systems.

Planetary boundary layer↗

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 NTRS1987

Earth Observing System - The earth research system of the 1990's

The Earth Observing Systems' objective of comprehensively studying the earth's change leads to an array of technological and implementational challenges. Included in those challenges are in the in-orbit maintenance of fifty instruments through periodic servicing and the development of an international ground information system which permits rapid access to high quality data. The paper describes these challenges and also discusses potential contributions from international and USA agencies, mission design and payload groupings strategies, as well as design approaches to the spacecraft itself.

Graf, James E.↗

NASA NTRS1990

The 1990 Reference Handbook: Earth Observing System

An overview of the Earth Observing System (EOS) including goals and requirements is given. Its role in the U.S. Global Change Research Program and the International--Biosphere Program is addressed. The EOS mission requirements, science, fellowship program, data and information systems architecture, data policy, space measurement, and mission elements are presented along with the management of EOS. Descriptions of the facility instruments, instrument investigations, and interdisciplinary investigations are also present. The role of the National Oceanic and Atmospheric Administration in the mission is mentioned.

Source record↗

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 NTRS2018

The Atmosphere-Ocean Interface Layer of NASA's Goddard Earth Observing System Model and Data Assimilation System Volume 51

The Goddard Earth Observing System (GEOS) general circulation model (GCM) includes modules for sea surface temperature (SST) diurnal warming and cool-skin layers. To support the application of a coupled atmosphere-ocean data assimilation capability, the GCM needs to be flexible enough to support both coupled atmosphere ocean general circulation model (AOGCM) and atmosphere-only (AGCM) configurations, with only minor configuration changes at the user interface. This document presents a formulation of an atmosphere-ocean interface layer (AOIL) that serves this purpose. Previous work by Akella et al. (2017) described a version of a model for near-surface temperature variations, including both both diurnal warming and cool-skin effects, that has been used since 2017 in the near-real-time GEOS FP (forward processing) weather analysis and forecasting system. The diurnal cycle of SST in that version of the GEOS atmospheric data assimilation system (ADAS) undergoes a sharp decay in the late afternoon (local time). The updated AOIL presented here includes a modification of the similarity function used in the diurnal warming model. Results from offline model runs illustrate an improvement in the near-surface (less than 0:5m depth) diurnal cycle compared to the original formulation. The new formulation requires minimal parameter tuning, and the improvements are robust across long (several month) simulation periods. This new model formulation, however, retains some deficiences from the previous module, such as a small warm bias in calm wind conditions for water depths below 1m. Our future work would include surface salinification and sea-ice into the AOIL.

Koster, Randal D.↗

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 NTRSDate not supplied

Evaluation of TAO Observation System on ENSO Predictions from the GMAO S2S Forecast System

Since the early 1990’s, the Tropical Atmosphere Ocean (TAO) mooring array has been instrumental for observing Kelvin and Rossby wave propagation which is key for El Nino/Southern Oscillation (ENSO) prediction. However, recent funding and programmatical issues have highlighted the need to rigorously assess the impact of the TAO observing system on ENSO predictions. Therefore, we evaluate the TAO using data assimilation observation denial experiments (also known as Observing System Evaluation or OSE experiments). This presentation will evaluate the TAO observing impact on both reanalyses and prediction of the big 2015 El Nino. We have completed reanalyses for July 2014-Dec 2015 for both the CONTROL experiment (that assimilates all available data) and the NOTAO experiment (that is identical to the CONTROL but withholds all TAO observations) using the best available NASA GEOS-S2S V3 seasonal prediction system. Validation of these reanalyses shows that TAO assimilation generally improves comparisons of temperature and salinity versus gridded in situ observations. Temperature is universally improved above the thermocline near the equator, but the biggest improvement is found in the eastern Pacific, just below the thermocline, where the variation of the thermocline defines ENSO events. In addition, a surprising result is that even with relatively few observations, salinity is improved throughout the equatorial region except near 120oW near the surface. ENSO forecasts were performed that were initialized from these CONTROL and NOTAO reanalyses. For the 9-month forecasts which were initialized in January, July, and October 2015, the NINO3.4 SST shows that the CONTROL forecasts are warmer and closer to observations than the NOTAO forecasts. We will show that upwelling and shoaling of the mixed layer amplifies the ENSO signal due to TAO assimilation. Prior to (after) April 2015, this upwelling is caused by relatively stronger Rossby (Kelvin) waves in the CONTROL than in the NOTAO experiments.

E. Hackert↗

NASA NTRS1985

NASA plans for spaceborne lidar - The Earth Observing System

The use of laser techniques to observe the earth's atmosphere and surface from space is examined. An Earth Observing System is proposed as the platform for performing lidar observations. The objectives and capabilities of the Lidar Atmospheric Sounder and Altimeter (LASA) are described. The measurement techniques for collecting temperature, pressure, and water vapor profiles, the vertical distribution of ozone in the stratosphere and troposphere, and atmospheric particulates profiles are discussed. The LASA requirements for altimetry and retroranging are considered. The Doppler Lidar Wind Sensor which provides data on wind velocity is analyzed. These laser systems with increased spatial resolution and accuracy form an efficient information system for studying earth science developments.

Curran, R. J.↗

DOE OSTI2026

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Atmospheric methane (CH4) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and enables early detection of climate‐driven feedbacks from wetlands, fires, permafrost, agriculture, and fossil‐fuel emissions.

Ciais, P↗

NASA NTRS2019

Uncertainty of Observation Impact Estimation in an Adjoint Model Investigated with an Observing System Simulation Experiment

Adjoint models are often used to estimate the impact of different observations on short-term forecast skill. A common difficulty with the evaluation of short term forecast quality is the choice of verification fields. The use of self-analysis fields for verification is typical but incestuous, and introduces uncertainty due to biases and errors in the analysis field. In this study, an observing system simulation experiment (OSSE) is used to explore the uncertainty in adjoint model estimations of observation impact. The availability of the true state for verification in the OSSE framework in the form of the Nature Run allows calculation of the observation impact without the uncertainties present in self-analysis verification. These impact estimates are compared to estimates calculated using self-analysis verification. The Global Earth Observing System version 5 (GEOS-5) forecast model with Gridpoint Statistical Interpolation (GSI) is used with the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE capability. The adjoint model includes moist processes, with total wet energy selected as the norm for evaluation of observation impacts. The results show that there are measurable but small discrepancies in the adjoint model estimation of observation impact. In general, observations of temperature and winds tend to have overestimated impacts with self-analysis verification, while observations of humidity and moisture-affected observations tend to have underestimated impacts. The small magnitude of the differences in impact estimates supports the robustness of the adjoint method of estimating observation impacts.

N C Prive↗

NASA NTRS1986

The earth observing system

This paper summarizes concepts for an Earth Observing System (EOS) for the 1990s that will provide the observational capabilities and an information system needed to understand how the earth works as a system. The concept diverges somewhat from past practices in that it considers EOS as an information system, where mission operation, EOS data bases and information about other relevant data sets are tied together by an information network. Three EOS instrument packages were chosen on the basis of synergistic groupings of instruments to make simultaneous observations of selected phenomena over a variety of wavelengths.

Hartle, R. E.↗

NASA NTRS2010

Using MERRA Gridded Innovations for Quantifying Uncertainties in Analysis Fields and Diagnosing Observing System Inhomogeneities

MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. The characterization of uncertainty in reanalysis fields is a commonly requested feature by users of such data. While intercomparison with reference data sets is common practice for ascertaining the realism of the datasets, such studies typically are restricted to long term climatological statistics and seldom provide state dependent measures of the uncertainties involved. In principle, variational data assimilation algorithms have the ability of producing error estimates for the analysis variables (typically surface pressure, winds, temperature, moisture and ozone) consistent with the assumed background and observation error statistics. However, these "perceived error estimates" are expensive to obtain and are limited by the somewhat simplistic errors assumed in the algorithm. The observation minus forecast residuals (innovations) by-product of any assimilation system constitutes a powerful tool for estimating the systematic and random errors in the analysis fields. Unfortunately, such data is usually not readily available with reanalysis products, often requiring the tedious decoding of large datasets and not so-user friendly file formats. With MERRA we have introduced a gridded version of the observations/innovations used in the assimilation process, using the same grid and data formats as the regular datasets. Such dataset empowers the user with the ability of conveniently performing observing system related analysis and error estimates. The scope of this dataset will be briefly described. We will present a systematic analysis of MERRA innovation time series for the conventional observing system, including maximum-likelihood estimates of background and observation errors, as well as global bias estimates. Starting with the joint PDF of innovations and analysis increments at observation locations we propose a technique for diagnosing bias among the observing systems, and document how these contextual biases have evolved during the satellite era covered by MERRA.

da Silva, Arlindo↗

NASA NTRS2015

Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

OSSE↗

NASA NTRS1984

Earth Observing System, Conclusions and Recommendations

The following Earth Observing Systems (E.O.S.) recommendations were suggested: (1) a program must be initiated to ensure that present time series of Earth science data are maintained and continued. (2) A data system that provides easy, integrated, and complete access to past, present, and future data must be developed as soon as possible. (3) A long term research effort must be sustained to study and understand these time series of Earth observations. (4) The E.O.S. should be established as an information system to carry out those aspects of the above recommendations which go beyond existing and currently planned activities. (5) The scientific direction of the E.O.S. should be established and continued through an international scientific steering committee.

Source record↗

NASA NTRS2020

Evaluation of adjoint-based observation impacts as a function of forecast length using an Observing System Simulation Experiment

Adjoints of numerical weather prediction models may be employed for Forecast Sensitivity to Observation (FSO) in order to monitor the contribution of ingested observation data on short-term forecast skill. However, the calculation of short-term forecast error is difficult due to the lack of a truly independent dataset for verification. In an Observing System Simulation Experiment framework, the Nature Run is able to provide a true and complete verification dataset and allows accurate evaluation of short term forecast errors. In this work, an OSSE developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office is used to explore the impact of observational data on forecasts in the 6 to 48 hour range. An adjoint of the Global Earth Observing System model is employed to compare the observation impacts estimated using both self-analysis verification and the true Nature Run verification. Self-analysis verification is found to inflate the estimated forecast error growth during the early forecast period, resulting in overestimations of observation impacts, particularly in the 6-12 hour forecast range. By 48 hours, the self-analysis verification estimates of forecast error and observation impacts more closely match the true values. The fraction of beneficial observations is also overinflated at short forecast times when self-analysis verification is used. The progression of impacts of an individual observation or data type depends on the character of the growth of the initial condition error that each observation affects.

numerical weather prediction↗