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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 NTRS2018

Some General and Fundamental Requirements for Designing Observing System Simulation Experiments (OSSEs)

The intent of this white paper is to inform WMO projects and working groups, together with the broader weather research and general meteorology and oceanography communities, regarding the use of Observing System Simulation Experiments (OSSEs). This paper is not intended to be either a critical or cursory review of past OSSE efforts. Instead, it describes some fundamental, but often neglected, aspects of OSSEs and prescribes important caveats regarding their design, validation, and application. Well designed, properly validated, and carefully conducted OSSEs can be invaluable for examining, understanding, and estimating impacts of proposed observing systems and new data assimilation techniques. Although significant imperfections and limitations should be expected, OSSEs either profoundly complement or uniquely provide both qualitative and quantitative characterizations of potential analysis of components of the earth system.

WMO↗

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↗

NASA NTRS2018

Observing System Simulation Experiments as Tools for Investigating the Behavior of Data Assimilation Systems

Data assimilation systems (DAS) are difficult to evaluate in part because there is limited independent data to use for verification of performance. In an Observing System Simulation Experiment (OSSE), the full true state is known exactly, in the form of the Nature Run. The availability of this truth allows the investigation of DAS characteristics in the OSSE framework that are not quantifiable in the real world. The synthetic observations can also be manipulated to test configurations that range from idealized to highly realistic. A sampling of OSSE investigations into the behavior of 3DVar and 4DEnVar DAS and adjoint observation impact estimation tools will be illustrated using the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE.

Prive, Nikki C.↗

NASA NTRS1989

Leader personality and crew effectiveness - A full-mission simulation experiment

A full-mission simulation research study was completed to assess the impact of individual personality on crew performance. Using a selection algorithm described by Chidester (1987), captains were classified as fitting one of three profiles along a battery of personality assessment scales. The performances of 23 crews led by captains fitting each profile were contrasted over a one and one-half day simulated trip. Crews led by captains fitting a positive Instrumental-Expressive profile (high achievement motivation and interpersonal skill) were consistently effective and made fewer errors. Crews led by captains fitting a Negative Expressive profile (below average achievement motivation, negative expressive style, such as complaining) were consistently less effective and made more errors. Crews led by captains fitting a Negative Instrumental profile (high levels of competitiveness, Verbal Aggressiveness, and Impatience and Irritability) were less effective on the first day but equal to the best on the second day. These results underscore the importance of stable personality variables as predictors of team coordination and performance.

Chidester, Thomas R.↗

NASA NTRSDate not supplied

Tools for Performing SBG hyperspectral Observing System Simulation Experiment

One of NASA’s Decadal Survey mission, Surface Biology and Geology (SBG), will include a hyperspectral remote sensing imager, which has a very high spatial resolution and a wide spectral coverage (from UV to Near IR). Unprecedented large data volumes will be generated by the SBG hyperspectral instrument. Before the launch of the new satellite, an Observing System Simulation Experiment (OSSE) can be used to study different designs of the new satellite system. One of the key components in an OSSE study is a radiative transfer model (RTM) or forward model. In this presentation, we will describe a Principal Component-based Radiative Transfer Model (PCRTM) which is capable of simulating atmospheric (TOA) radiance or reflectance spectra from far IR to visible and UV spectral regions (50 wavenumber to 30000 wavenumber) quickly and accurately. Multiple scattering from multiple layers of clouds/aerosols are included in the model. The PCRTM has a very good accuracy relative to reference line-by-line radiative transfer models (LBLRTM), and it saves 3-4 orders of magnitude computational time relative to LBLRTM or MODTRAN. The PCRTM model has been successfully used to analyze large volumes of data from hyperspectral sensors such as AIRS, CrIS, and IASI. It has also been used to perform OSSE studies for the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission. Another useful tool for the OSSE is surface Bidirectional Reflectance Distribution Function (BRDF) database. It is very crucial for the SBG OSSE to include realistic BRDF spectra. Currently, most of the surface reflectance spectra such as those in the ECOSIS and ECOSTRESS are measured at specific observation geometries. We have developed a hyperspectral bidirectional reflectance (HSBR) model which combines Ross-Li BRDF model with the existing reflectance spectral libraries using a principal component analysis. This HSBR model can provide realistic BRDF spectra under various observation conditions. It can also be used to generate realistic BRDF spectra using measurements from multi-band imagers or spectrometers such as MODIS or VIIRS.

Xu Liu↗

NASA NTRS2022

Development of a “Nature Run” for Observing System Simulation Experiments (OSSEs) for Snow Mission Development

Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an observing system simulation experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010–20. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from −0.036 to −0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain–snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

Melissa L. Wrzesien↗

NASA NTRS2018

A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

In the near future, there will be several new instruments measuring atmospheric composition from geostationary orbit over North America, East Asia, and Europe. This constellation of satellites will provide high resolution, time resolved measurements of trace gases and aerosols for monitoring air quality and tracking pollution sources. This paper describes a detailed, fast, and accurate (less than 1.0% uncertainty) method for calculating synthetic top of the atmosphere (TOA) radiances from a global simulation with a mesoscale free running model, the GEOS-5 Nature Run, for remote sensing instruments in geostationary orbit that measure in the ultraviolet-visible spectral range (UV-Vis). Generating these synthetic observations is the first step of an Observing System Simulation Experiment (OSSE), a framework for evaluating the impact of a new observation or algorithm. This paper provides details of the model sampling, aerosol and cloud optical properties, surface reflectance modeling, Rayleigh scattering calculations, and a discussion of the uncertainties of the simulated TOA radiance. An application for the simulated TOA radiance observations is demonstrated in the manuscript. Simulated TEMPO (Tropospheric Emissions: Monitoring of Pollution) and GOES-R (Geostationary Operational Environmental Satellites) observations were used to show how observations from the two instruments could be combined to facilitate aerosol type discrimination. The results demonstrate the viability of a detailed instrument simulator for radiance measurements in the UV-Vis that is capable of accurately simulating high resolution, time-resolved measurements with reasonable computational efficiency.

Castellanos, Patricia↗

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 NTRS1984

Observing system simulation experiments related to space-borne LIDAR wind profiling. I - Forecast impacts of highly idealized observing systems. II - Sensitivity to atmospheric and instrumental influences

It is pointed out that extended-range numerical weather prediction requires complete global and accurate three-dimensional fields of temperature, moisture, and winds as a function of pressure throughout the troposphere, lower stratosphere, and at the earth's surface. Recent developments in the use of incoherent optical radar (LIDAR) systems indicate the feasibility of obtaining pressure as a function of height with presumably very high vertical resolution and accuracy. In evaluating the feasibility of an employment of spaceborne LIDAR systems as an aid for obtaining data required for weather prediction, simulation studies could provide important information. The present investigation is concerned with the conduction of such simulation experiments. It is found that the results of a composite system of temperature and pressure show significant improvements over those of temperature only. The effects of simulating several sources of LIDAR wind error for two different representations of nature are also examined.

Dlouhy, R.↗

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 NTRS1989

Modifications of comet materials by the sublimation process: Results from simulation experiments

An active comet like comet Halley loses by sublimation a surface layer of the order of 1 m thickness per perihelion passage. In situ measurements show that water ice is the main constituent which contributes to the gas emission although even more volatile species (CO, NH3, CH4, CO2 etc.) have been identified. Dust particles which were embedded in the ices are carried by the sublimating gases. Measurements of the chemical composition of cometary grains indicate that they are composed of silicates of approximate chondritic composition and refractory carbonaceous material. Comet simulation experiments show that significant modifications of cometary materials occur due to sublimation process in near surface layers which have to be taken into account in order to derive the original state of the material.

Gruen, E.↗

NASA NTRS2023

Impacts of Marine Surface Pressure Observations From A Spaceborne Differential Absorption Radar Investigated With an Observing System Simulation Experiment

A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multi-channel differential absorption radar (DAR) to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration (NASA) for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root mean square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-hour forecast temperatures for spaceborne surface pressures on par with rawinsondes and aircraft, and greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the southern hemisphere extratropics.

N.C. Privé↗

NASA NTRSDate not supplied

Observing System Simulation Experiments to Determine the Impact of Spaceborne Differential Absorption Radar Measurements of Marine Surface Pressure on Numerical Weather Prediction

Surface air pressures over marine regions can potentially be measured by estimating the total atmospheric column oxygen content with differential absorption radar (DAR). A demonstration instrument, the Microwave Barometric Radar and Sounder (MBARS) has been funded by NASA for airborne missions in 2024 to show proof of concept. In this presentation, the potential use of such marine surface pressure observations from a spaceborne DAR platform to improve numerical weather prediction is examined using the NASA/GMAO global Observing System Simulation Experiment. Several aspects of the instrument are explored, including contamination by heavy precipitation and the expected error characteristics of the observations. Different orbit and scan configurations are compared, including various constellations of nadir smallsats and A-Train-type scanning configurations. The impacts of marine surface pressure observations are put into context with the current global observing network with a Forecast Sensitivity Observation Impact tool.

Nikki Privé↗

NASA NTRS2013

Preliminary Observing System Simulation Experiments for Doppler Wind Lidars Deployed on the International Space Station

NASA Goddard Space Flight Center's Software Systems Support Office (SSSO) is participating in a multi-agency study of the impact of assimilating Doppler wind lidar observations on numerical weather prediction. Funded by NASA's Earth Science Technology Office, SSSO has worked with Simpson Weather Associates to produce time series of synthetic lidar observations mimicking the OAWL and WISSCR lidar instruments deployed on the International Space Station. In addition, SSSO has worked to assimilate a portion of these observations those drawn from the NASA fvGCM Nature Run into the NASA GEOS-DAS global weather prediction system in a series of Observing System Simulation Experiments (OSSEs). These OSSEs will complement parallel OSSEs prepared by the Joint Center for Satellite Data Assimilation and by NOAA's Atlantic Oceanographic and Meteorological Laboratory. In this talk, we will describe our procedure and provide available OSSE results.

Observing System Simulation↗

NASA NTRS1991

Observing system simulation experiments for the laser atmospheric wind sounder using global spectral model

Fundamental to improving the understanding of the total Earth system are increased and improved observations. In the coming decade several spaceborne instrumented platforms will be constructed and implemented. These platforms will, in large, be housing the NASA Earth Observing System (EOS) instrument suite. One of the proposed instruments is a wind profiling system which is currently referred to as the Laser Atmospheric Wind Sounder (LAWS). This instrument will use a CO2 Doppler lidar wind profiler to give wind measurements with a vertical and horizontal resolution which has yet to be seen globally. The LAWS instrument is now a candidate for launch on a NASA EOS-B platform and is fundamental to increasing our understanding of Earth system science. The LAWS data sets will form an integral component of the temporally continuous data base needed for research of the coupled climate systems. This instrument's observations will aid in giving an improved description of the atmospheric circulation, including the transports of energy, momentum, moisture, trace gases, and aerosols. Also, the wind data will be assimilated and used as the initial state for many global forecast models at various operational centers. Results of system simulation experiments are discussed, and future experiments are described.

Rohaly, Gregg↗

NASA NTRS2012

A Regional CO2 Observing System Simulation Experiment Using ASCENDS Observations and WRF-STILT Footprints

Knowledge of the spatiotemporal variations in emissions and uptake of CO2 is hampered by sparse measurements. The recent advent of satellite measurements of CO2 concentrations is increasing the density of measurements, and the future mission ASCENDS (Active Sensing of CO2 Emissions over Nights, Days and Seasons) will provide even greater coverage and precision. Lagrangian atmospheric transport models run backward in time can quantify surface influences ("footprints") of diverse measurement platforms and are particularly well suited for inverse estimation of regional surface CO2 fluxes at high resolution based on satellite observations. We utilize the STILT Lagrangian particle dispersion model, driven by WRF meteorological fields at 40-km resolution, in a Bayesian synthesis inversion approach to quantify the ability of ASCENDS column CO2 observations to constrain fluxes at high resolution. This study focuses on land-based biospheric fluxes, whose uncertainties are especially large, in a domain encompassing North America. We present results based on realistic input fields for 2007. Pseudo-observation random errors are estimated from backscatter and optical depth measured by the CALIPSO satellite. We estimate a priori flux uncertainties based on output from the CASA-GFED (v.3) biosphere model and make simple assumptions about spatial and temporal error correlations. WRF-STILT footprints are convolved with candidate vertical weighting functions for ASCENDS. We find that at a horizontal flux resolution of 1 degree x 1 degree, ASCENDS observations are potentially able to reduce average weekly flux uncertainties by 0-8% in July, and 0-0.5% in January (assuming an error of 0.5 ppm at the Railroad Valley reference site). Aggregated to coarser resolutions, e.g. 5 degrees x 5 degrees, the uncertainty reductions are larger and more similar to those estimated in previous satellite data observing system simulation experiments.

Wang, James S.↗

NASA NTRS1988

Reduced gravity boiling and condensing experiments simulated with the COBRA/TRAC computer code

A series of reduced-gravity two-phase flow experiments has been conducted with a boiler/condenser apparatus in the NASA KC-135 aircraft in order to obtain basic thermal-hydraulic data applicable to analytical design tools. Several test points from the KC-135 tests were selected for simulation by means of the COBRA/TRAC two-fluid, three-field thermal-hydraulic computer code; the points were chosen for a 25-90 percent void-fraction range. The possible causes for the lack of agreement noted between simulations and experiments are explored, with attention to the physical characteristics of two-phase flow in one-G and near-zero-G conditions.

Cuta, Judith M.↗