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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1985

Results of an observing system simulation experiment based on the proposed Windsat instrument

Two fraternal twin experiments were conducted as part of this study. A data impact experiment using real data (runs 1 and 2) was conducted to assess the impact that rawin wind observations have on both a 5-day assimilation and a single 5-day forecast generated at the end of each assimilation. An observing system simulation experiment (OSSE) using simulated observations (runs 3, 4, and 5) was conducted in order to first calibrate the OSSE results and second to use this calibration to estimate the 'real world' impact from the contribution of global 3-dimensional wind profiles generated from a space-based lidar system known as Windsat. Each of the three runs in the seond experiment were also 5-day assimilation runs with a 5-day forecast initialized from the last 6-hour update cycle of the assimilation. The data impact study revealed a consistent positive impact when rawin winds were added back into an otherwise complete FGGE data set. Both the 6-hour and 5-day forecasts were improved at all levels, in both hemispheres, and for both the wind and the geopotential height fields. Similar results were obtained from the two parallel simulation runs, 3 and 4. Together with the results from runs 1 and 2, calibration coefficients were generated so as to 'correct' the results determined from the addition of Windsat winds (run 5). The Windsat simulation showed a positive improvement in all cases studied. Even though only the tropics were enhanced with these wind observations, hemispheric rms errors were decreased in both the assimilation and 5-day forecast. The 6-hour forecasts of zonal wind from the assimilation run were improved by as much as 50 pct. on the average, and the single forecast showed an average improvement of near 30 pct. Even though these calibrated values are considered too optimistic, the skill of the forecast generated from this run extended the useful forecast period by 18-24 hours.

Arnold, Charles P., Jr.↗
NASA NTRS2002

Recent Observing System Simulation Experiments at the NASA DAO

Since the advent of meteorological satellites in the 1960's, numerous experiments have been conducted in order t o evaluate the impact of these and other data on atmospheric analysis and prediction. Such studies have included both OSE'S (Observing System Experiments) and OSSE's (Observing System Simulation Experiments). The OSE's were conducted to evaluate the impact of specific observations or classes of observations on analyses and forecasts. Such experiments have been performed for selected types of conventional data and for various satellite data sets as they became avail- able. (See for example the 1989 ECMWF/EUMETSAT workshop proceedings on "The use of satellite data in operational numerical weather prediction" and the references contained therein.) The OSSE's were con- ducted to evaluate the potential for future observing systems to improve Numerical Weather Prediction (NWP) and to plan for the Global Weather Experiment and more recently for EOS. In addition, OSSE's have been run t o evaluate trade-offs in the design of observing systems and observing net-works, and to test new methodology for data assimilation.

Atlas, Robert↗
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 NTRS2003

Observing System Simulation Experiments to Determine the Potential Impact of Space-Based Lidar Wind Profiles on Weather Prediction

Observing system simulation experiments (OSSE's) provide an effective means to evaluate the potential impact of a proposed observing system, as well as to determine tradeoffs in their design, and to evaluate data assimilation methodology. Great care must be taken to ensure realism of the OSSE's, and in the interpretation of OSSE results. All of the OSSE's that have been conducted to date have demonstrated tremendous potential for space-based wind profile data to improve atmospheric analyses, forecasts, and research. This has been true for differing data assimilation systems, analysis methodology, and model resolutions. OSSE's clearly show much greater potential for observations of the complete wind profile than for single-level wind data or observations of the boundary layer alone.

Atlas, Robert↗
NASA NTRS2023

The Gmao Hybrid 4d-Envar Observing System Simulation Experiment Framework.

This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Data assimilation↗
NASA NTRS1985

Design of a WINDSAT observing system simulation experiment

The following topics are addressed: (1) motivation for the National Meteorological Center (NMC) simulation project; (2) history of the NMC simulation project; (3) experimental design of the WINDSAT observing system simulation experiment; (4) preparation of the simulated observations; and (5) strengths and weaknesses of the experimental design.

Dey, Clifford H.↗
NASA NTRS1993

An observing system simulation experiment for the Laser Atmospheric Wind Sounder (LAWS)

The present observing system-simulation experiments evaluate the potential of a Laser Atmospheric Wind Sounder (LAWS) instrument for 5-day forecasting, using a primitive-equation multilevel spectral global circulation model. A 55-deg-inclined and a 98-deg sun-synchronous orbit are examined, by adding LAWS wind profiles into a global 4D data-assimilation system, and comparing both the analyses and forecasts to a control experiment. The 4D data-assimilation system consists of a multivariate optimum interpolation analysis and a nonlinear, normal-mode intialization, using the aforementioned global circulation model.

Rohaly, G. D.↗
NASA NTRS2021

Observing System Simulation Experiments Investigating Atmospheric Motion Vectors and Radiances from a Constellation of 4–5-μm Infrared Sounders

A set of observing system simulation experiments (OSSEs) was performed to investigate the utility of a constellation of passive infrared spectrometers, strategically designed with the aim of deriving the three-dimensional retrievals of the horizontal wind via atmospheric motion vectors (AMVs) from instruments with the spectral resolution of an infrared sounder. The instrument and constellation designs were performed in the context of the Midwave Infrared Sounding of Temperature and humidity in a Constellation for Winds (MISTiC Winds). The Global Modeling and Assimilation Office OSSE system, which includes a full suite of operational meteorological observations, served as the control. To illustrate the potential impact of this observing strategy, two experiments were performed by adding the new simulated observations to the control. First, perfect (error free) simulated AMVs and radiances were assimilated. Second, the data were made imperfect by adding realistic modeled errors to the AMVs and radiances that were assimilated. The experimentation showed beneficial impacts on both the mass and wind fields, as based on analysis verification, forecast verification, and the assessment of the observations using the forecast sensitivity to observation impact (FSOI) metric. In all variables and metrics, the impacts of the imperfect observations were smaller than those of the perfect observations, although much of the positive benefit was retained. The FSOI metric illustrated two key points. First, the largest impacts were seen in the middle troposphere AMVs, which is a targeted capability of the constellation strategy. Second, the addition of modeled errors showed that the assimilation system was unable to fully exploit the 4.3-μm carbon dioxide absorption radiances.

Will McCarty↗
NASA NTRS2020

Observing System Simulation Experiments Investigating Atmospheric Motion Vectors and Radiances from a Constellation of 4-5 μm Infrared Sounders

A set of Observing System Simulation Experiments (OSSEs) was performed to investigate the utility of a constellation of passive infrared spectrometers, strategically designed with the aim of deriving the three-dimensional retrievals of the horizontal wind via atmospheric motion vectors (AMVs) from instruments with the spectral resolution of an infrared sounder. The instrument and constellation designs were performed in the context of the Midwave Infrared Sounding of Temperature and humidity in a Constellation for Winds, or MISTiC Winds. The Global Modeling and Assimilation Office OSSE system, which includes a full suite of operational meteorological observations, served as the control. To illustrate the potential impact of this observing strategy, two experiments were performed by adding the new simulated observations to the control. First, perfect (error-free) simulated AMVs and radiances were assimilated. Second, the data were made imperfect by adding realistic modeled errors to the AMVs and radiances that were assimilated. The experimentation showed beneficial impacts on both the mass and wind fields, as based on analysis verification, forecast verification, and the assessment of the observations using the Forecast Sensitivity to Observation Impact (FSOI) metric. In all variables and metrics, the impacts of the imperfect observations were smaller than those of the perfect observations, though much of the positive benefit was retained. The FSOI metric illustrated two key points. First, the largest impacts were seen in the middle troposphere AMVs, which is a targeted capability of the constellation strategy. Second, the addition of modeled errors showed that the assimilation system was unable to fully exploit the 4.3 μm carbon dioxide absorption radiances.

Will McCarty↗
NASA NTRS2018

Observing System Simulation Experiments (OSSEs): Relevance to ACE and A-CCP

In this talk we present an overview of Observing System Simulation Experiments (OSSEs) of relevance to the Aerosol-Cloud-Ecosystems (ACE) pre-formulation study and to the Aerosols and Cloud, Convection & Precipitation (A-CCP) Designated Observables recommended by the 2017 Decadal Survey. We start by defining the scope of OSSEs that are applicable to climate and atmospheric composition missions, followed by a theoretical discussion of retrieval error chacterizations. We conclude some examples of retrieval OSSEs and a summary of other OSSE activities of relevance.

Da Silva, Arlindo↗
NASA NTRS2020

Building a standardized Observing System Simulation Experiment (OSSE) framework for Mars

We advocate that the Decadal Survey recommends the NASA Science Mission Directorate to develop a rigorous Observing System Simulation Experiment (OSSE) framework for Mars, to optimize future atmospheric observations. Atmospheric conditions on Mars are a potential hazard source for landing missions. Errors in the estimates of atmospheric density profiles, inadequate knowledge of wind vertical structure and dust concentration as a function of height are likely causes of uncertainty at the landing site on the order of kilometers. An operational real-time weather forecasting capability for Mars would reduce such uncertainties, carrying enormous benefits to future robotic missions, and would be an invaluable prerequisite for human missions.A real-time forecasting capability relies upon three fundamental components: a critical mass of observing systems, a data assimilation system (DAS), and a global forecast model. The DAS allows the model to ingest the data effectively, optimizing the observational information content,and transforming them into a gridded representation of the atmosphere at a given time, called an ‘analysis’. The analysis is the best estimate of the atmospheric state for that time, and also represents a set of ‘initial conditions’ from which a global model can be initialized, to predict a future state of the atmosphere. The connection between analysis and forecast represents the foundation of modern weather forecasting. However, from the point of view of a forecast system,not all observations are equally impactful, partially because of the problem of “observational error correlation”, one important research topic in data assimilation development. For the Earth, partly due to the spontaneous and deregulated development of observations and forecast capabilities worldwide for more than half a century,the use of observations in contemporary operational forecast systems is suboptimal, with many potentially useful data being underutilized. On the contrary, Mars atmospheric scientists are in the unique situation of designing the next-generation observing systems by learning from the experience gathered on the Earth, so as to assure that the future instruments are specifically optimized to give the maximum benefit to a future weather forecast capability.An immensely powerful tool that has been firmly established by atmospheric scientists on the Earth is represented by a properly designed OSSE framework. A realistic OSSE framework cannot only quantify the benefit of future data types, be them surface based or space borne, but can also help design and optimize an entire observational network. Furthermore, OSSEs can provide deep insights into an atmosphere’s behavior, by addressing conceptual problems of its intrinsic predictability and delineating the regions or features of the atmosphere which are more sensitive to additional data and would benefit from a denser sampling. The difficulties posed by OSSEs are fundamentally different for Earth and Mars. For Earth, the enormous data volume imposes a tremendous constraint on any innovation in the observing systems: it is very hard for a single sensor to impact the skill. For Mars, the problem is the opposite: almost any additional instrument will exert some impact. However, OSSEs can help to evaluate the cost/benefit for every sensor and suggest optimal data configuration and density.The purpose of this white paper is to provide an introduction to a rigorously designed OSSE framework, explain the underlying problems and challenges, and engage the Mars community to collaborate with Earth Atmospheric scientists in order to develop a joint-OSSE framework for Mars with the largest consensual basis possible. An OSSE infrastructure would increase the understanding of the Martian atmosphere, would help NASA to optimize instrument specifications and orbit choice, providing the maximium benefit for a given expenditure of resources, and could even help establishing a roadmap for a future real-time weather forecasting capability.

Oreste Reale↗
NASA NTRS2018

Performance and Evaluation of the Global Modeling and Assimilation Office Observing System Simulation Experiment

The National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) has spent more than a decade developing and implementing a global Observing System Simulation Experiment framework for use in evaluting both new observation types as well as the behavior of data assimilation systems. The NASA/GMAO OSSE has constantly evolved to relect changes in the Gridpoint Statistical Interpolation data assimiation system, the Global Earth Observing System model, version 5 (GEOS-5), and the real world observational network. Software and observational datasets for the GMAO OSSE are publicly available, along with a technical report. Substantial modifications have recently been made to the NASA/GMAO OSSE framework, including the character of synthetic observation errors, new instrument types, and more sophisticated atmospheric wind vectors. These improvements will be described, along with the overall performance of the current OSSE. Lessons learned from investigations into correlated errors and model error will be discussed.

OSS↗
NASA NTRS2013

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

Model Error↗
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 NTRS2010

An Atmospheric Science Observing System Simulation Experiment (OSSE) Environment

An atmospheric sounding mission starts with a wide range of concept designs involving measurement technologies, observing platforms, and observation scenarios. Observing system simulation experiment (OSSE) is a technical approach to evaluate the relative merits of mission and instrument concepts. At Jet Propulsion Laboratory (JPL), the OSSE team has developed an OSSE environment that allows atmospheric scientists to systematically explore a wide range of mission and instrument concepts and formulate a science traceability matrix with a quantitative science impact analysis. The OSSE environment virtually creates a multi-platform atmospheric sounding testbed (MAST) by integrating atmospheric phenomena models, forward modeling methods, and inverse modeling methods. The MAST performs OSSEs in four loosely coupled processes, observation scenario exploration, measurement quality exploration, measurement quality evaluation, and science impact analysis.

mission concepts↗
NASA NTRS1991

Simulating thin cirrus clouds in Observing System Simulation Experiments (OSSE) for LAWS

It was previously shown (Wood and Emmitt, 1990, 1991) that, by omitting the contribution of thin cirrus by the Observing System Simulation Experiments, will result in severe misrepresentation of both the frequency and the accuracy of wind observations in the upper troposphere. This paper estimates the presence of optically thin (tau less than 1.0) cirrus clouds by using model soundings in a cirrus cloud model. An example is presented showing the location of cirrus cloud profiles over North America for November 10, 1979, which were generated by the LAWS Simulation Model.

Emmitt, G. D.↗
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↗
NASA NTRSDate not supplied

Impacts of Assimilating Geostationary Infrared Sounders within NASA’s Observing System Simulation Experiment Framework

The National Oceanic and Atmospheric Administration (NOAA) has proposed including a hyperspectral infrared sounder onboard the Geostationary eXtended Observations (GeoXO) platform, scheduled to begin launching in the 2030s. A set of observing system simulation experiments (OSSEs) was preformed to investigate the impact of assimilating data from the GeoXO Sounder (GXS) using the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO) OSSE system. The aim of this study is to identify the global gains achieved by the assimilation of geostationary infrared sounders from a numerical weather prediction perspective. Progress of current experimentation and future directions will be presented.

Erica Mcgrath-Spangler↗