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All sourcesNASA NTRSDOE OSTI
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 NTRS2018

Aerosol and Trace Gas OSSE Capabilities at Goddard

In this talk we discuss OSSE (Observation System Simulation Experiments) capabilities at Goddard for aerosol and trace gas studies. We review exisitng Nature Runs (NR - a simulation of nature), observation simulation capabilities and several examples of retrieval OSSEs. We conclude with a summary of other OSSE activities of relevance, and a list of websites where documentation, data files and web visualizations can be found.

Da Silva, Arlindo↗
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 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 NTRS2016

Characterizing background error with the NASA GMAO OSSE

Understanding the characteristics of background error is vital to the design and performance of data assimilation systems. However, the true state of the atmosphere is unknown, and qualitative determination of background error is thus a challenge. We present an exploration of background error characteristics using the NASA Global Modeling and Assimilation Office Observing System Simulation Experiment framework, where the presence of the Nature Run allows direct calculation of the background error. The behavior of the data assimilation system (DAS) can be compared with the character of the background error, and performance of the DAS will be investigated in a series of both idealized and realistic experiments.

Nature Run↗
DOE OSTI2016

On the feasibility of monitoring carbon monoxide in the lower troposphere from a constellation of northern hemisphere geostationary satellites: Global scale assimilation experiments (Part II)

In this paper, we describe the second phase of an Observing System Simulation Experiment (OSSE) that utilizes the synthetic measurements from a constellation of satellites measuring atmospheric composition from geostationary (GEO) Earth orbit presented in part I of the study. Our OSSE is focused on carbon monoxide observations over North America, East Asia and Europe where most of the anthropogenic sources are located. Here we assess the impact of a potential GEO constellation on constraining northern hemisphere (NH) carbon monoxide (CO) using data assimilation. We show how cloud cover affects the GEO constellation data density with the largest cloud cover (i.e., lowest data density) occurring during Asian summer. We compare the modeled state of the atmosphere (Control Run), before CO data assimilation, with the known “true” state of the atmosphere (Nature Run) and show that our setup provides realistic atmospheric CO fields and emission budgets. Overall, the Control Run underestimates CO concentrations in the northern hemisphere, especially in areas close to CO sources. Assimilation experiments show that constraining CO close to the main anthropogenic sources significantly reduces errors in NH CO compared to the Control Run. We assess the changes in error reduction when only single satellite instruments are available as compared to the full constellation. We find large differences in how measurements for each continental scale observation system affect the hemispherical improvement in long-range transport patterns, especially due to seasonal cloud cover. Finally, a GEO constellation will provide the most efficient constraint on NH CO during winter when CO lifetime is longer and increments from data assimilation associated with source regions are advected further around the globe.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

On the Feasibility of Monitoring Carbon Monoxide in the Lower Troposphere from a Constellation of Northern Hemisphere Geostationary Satellites: Global Scale Assimilation Experiments (Part II)

This paper describes the second phase of an Observing System Simulation Experiment (OSSE) that utilizes the synthetic measurements from a constellation of satellites measuring atmospheric composition from geostationary (GEO) Earth orbit presented in part I of the study. Our OSSE is focused on carbon monoxide observations over North America, East Asia and Europe where most of the anthropogenic sources are located. Here we assess the impact of a potential GEO constellation on constraining northern hemisphere (NH) carbon monoxide (CO) using data assimilation. We show how cloud cover affects the GEO constellation data density with the largest cloud cover (i.e., lowest data density) occurring during Asian summer. We compare the modeled state of the atmosphere (Control Run), before CO data assimilation, with the known 'true' state of the atmosphere (Nature Run) and show that our setup provides realistic atmospheric CO fields and emission budgets. Overall, the Control Run underestimates CO concentrations in the northern hemisphere, especially in areas close to CO sources. Assimilation experiments show that constraining CO close to the main anthropogenic sources significantly reduces errors in NH CO compared to the Control Run. We assess the changes in error reduction when only single satellite instruments are available as compared to the full constellation. We find large differences in how measurements for each continental scale observation system affect the hemispherical improvement in long-range transport patterns, especially due to seasonal cloud cover. A GEO constellation will provide the most efficient constraint on NH CO during winter when CO lifetime is longer and increments from data assimilation associated with source regions are advected further around the globe.

Geo Stationary Constellation↗
DOE OSTI2022

Experimental Studies of Low-Load Limit in a Stoichiometric Micro-Pilot Diesel Natural Gas Engine

While operating at light loads, diesel pilot-ignited natural gas engines with lean premixed natural gas suffer from poor combustion efficiency and high methane emissions. This work investigates the limits of low-load operation for a micro-pilot diesel natural gas engine that uses a stoichiometric mixture to enable methane and nitrogen oxide emission control. By optimizing engine hardware, operating conditions, and injection strategies, this study focused on defining the lowest achievable load while maintaining a stoichiometric equivalence ratio and with acceptable combustion stability. A multi-cylinder diesel 6.7 L engine was converted to run natural gas premix with a maximum diesel micro-pilot contribution of 10%. With a base diesel compression ratio of 17.3:1, the intake manifold pressure limit was 80 kPa(absolute). At a reduced compression ratio of 15:1, this limit increased to 85 kPa, raising the minimum stable load. Retarding the combustion phasing, typically used in spark-ignition engines to achieve lower loads, was also tested but found to be limited by degraded diesel ignition at later timings. Reducing the pilot injection pressure improved combustion stability, as did increasing pilot quantity at the cost of lower substitution ratios. The lean operation further reduced load but increased NOx and hydrocarbon emissions. At loads below the practical dual-fuel limit, a transition to lean diesel operation will likely be required with corresponding implications for the aftertreatment system.

03 NATURAL GAS↗
NASA NTRS2003

Tropospheric Wind Observations with Doppler Lidars

Simpson Weather Associates has continued its efforts on the tools which operate the DLSM's s line-of-sight output products file. Due to the long model run times which were revealed in last month's benchmark tests, four model options for the LSM's atmospheric inputs are now included in the LSM. The user can choose to either spatially and/or temporally interpolate the inputs or use inputs closest to the DWL shot. A problem in the terrain assignment from the GADS was corrected. A database conversion tool was added to convert DWL line-of-sight ASCII files into database format for the graphic display models. Efforts have continued in simulating DWL horizontal wind components using the DAO ficcm Nature Run. Experiments 2 and 3 (40 days worth) were completed and sent to the DAO for their use in bracketing OSSE experiments. In addition, in order to investigate differences in the way regional (MM5) and global scale (ECMWF T213) models treat features such as clouds and subgrid scale variability, a gridded Mesoscale Atmospheric Data Set (MADS) was created and ready to be incorporated into simulation experiments.

Source record↗
DOE OSTI2020

Horizontal Gene Transfer to a Defensive Symbiont with a Reduced Genome in a Multipartite Beetle Microbiome

Symbiotic mutualisms of bacteria and animals are ubiquitous in nature, running a continuum from facultative to obligate from the perspectives of both partners. The loss of functions required for living independently but not within a host gives rise to reduced genomes in many symbionts. Although the phenomenon of genome reduction can be explained by existing evolutionary models, the initiation of the process is not well understood. Here, we describe the microbiome associated with the eggs of the beetle Lagria villosa, consisting of multiple bacterial symbionts related to Burkholderia gladioli, including a reduced-genome symbiont thought to be the exclusive producer of the defensive compound lagriamide. We show that the putative lagriamide-producing symbiont is the only member of the microbiome undergoing genome reduction and that it has already lost the majority of its primary metabolism and DNA repair pathways. The key step preceding genome reduction in the symbiont was likely the horizontal acquisition of the putative lagriamide lga biosynthetic gene cluster. Unexpectedly, we uncovered evidence of additional horizontal transfers to the symbiont’s genome while genome reduction was occurring and despite a current lack of genes needed for homologous recombination. These gene gains may have given the genome-reduced symbiont a selective advantage in the microbiome, especially given the maintenance of the large lga gene cluster despite ongoing genome reduction.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1985

Experiments on the Assimilation of Patches of Data Using a Barotropic Model

Experiments were performed to examine the impact of adopting various procedures in the assimilation of patches of data during the progress of a numerical forecast. A barotropic model described by Takacs and Balgovind, 1983 is used for the experiments. The model is global and its discretization is similar to the GLA Fourth Order GCM. The effect of orography is included in the model. The experiments are of an identical twin kind. The nature run starts from global fields of wind (u and v) and height (h) at 300 mb. They are real data of January, 1979 OOZ which were interpolated from the adjacent sigma levels of the GLA GCM and then balanced by the bounded derivative method. From this initial state a 6 day forecast is performed. At intervals of 3 hours this information is saved over sectors of 60 width in longitude, from the south pole to the north pole, to simulate data coverage and observational frequency of a polar orbiting satellite.

Semazzi, F. H. M.↗
NASA NTRS2004

Diagnostic Studies of Large-Scale Weather Systems in the GEOS General Circulation Model

During the period covered by this report, the PI has contributed in an advisory capacity to various aspects of the evaluation of the nature run, including the development of research plans, the derivation of diagnostic procedures, and the interpretation of the results of the evaluation. Computing and data analysis have been performed by Dr. Jusem in consultation with Dr. Atlas. The PI's contribution to this effort has been rendered primarily by means of scheduled biweekly phone discussions with Dr. Jusem, supplemented by electronic mailings and postings on the Web.

Keyser, Daniel↗
NASA NTRS2008

Chemical OSSEs in Global Modeling and Assimilation Office (GMAO)

This presentation will summarize ongoing 'chemical observing system simulation experiment (OSSE)' work in the Global Modeling and Assimilation Office (GMAO). Weather OSSEs are being studied in detail, with a 'nature run' based on the European Centre for Medium-Range Weather Forecasts (ECMWF) model that can be sampled by a synthesized suite of satellites that reproduces present-day observations. Chemical OSSEs are based largely on the carbon-cycle project and aim to study (1) how well we can reproduce the observed carbon distribution with the Atmospheric Infrared Sounder (AIRS) and Orbiting Carbon Observatory (OCO) sensors and (2) with what accuracy can we deduce surface sources and sinks of carbon species in an assimilation system.

Pawson, Steven↗
NASA NTRS2010

Mission Simulation of Space Lidar Measurements for Seasonal and Regional CO2 Variations

Results of mission simulation studies are presented for a laser-based atmospheric [82 sounder. The simulations are based on real-time carbon cycle process modeling and data analysis. The mission concept corresponds to the Active Sensing of [82 over Nights, Days, and Seasons (ASCENDS) recommended by the US National Academy of Sciences Decadal Survey of Earth Science and Applications from Space. One prerequisite for meaningful quantitative sensor evaluation is realistic CO2 process modeling across a wide range of scales, i.e., does the model have representative spatial and temporal gradients? Examples of model comparison with data will be shown. Another requirement is a relatively complete description of the atmospheric and surface state, which we have obtained from meteorological data assimilation and satellite measurements from MODIS and [ALIPS0. We use radiative transfer model calculations, an instrument model with representative errors ' and a simple retrieval approach to complete the cycle from "nature" run to "pseudo-data" CO2, Several mission and instrument configuration options are examined/ and the sensitivity to key design variables is shown. We use the simulation framework to demonstrate that within reasonable technological assumptions for the system performance, relatively high measurement precision can be obtained, but errors depend strongly on environmental conditions as well as instrument specifications. Examples are also shown of how the resulting pseudo - measurements might be used to address key carbon cycle science questions.

Kawa, Stephan↗
NASA NTRS2011

Simulation Studies of Satellite Laser CO2 Mission Concepts

Results of mission simulation studies are presented for a laser-based atmospheric CO2 sounder. The simulations are based on real-time carbon cycle process modeling and data analysis. The mission concept corresponds to ASCENDS as recommended by the US National Academy of Sciences Decadal Survey. Compared to passive sensors, active (lidar) sensing of CO2 from space has several potentially significant advantages that hold promise to advance CO2 measurement capability in the next decade. Although the precision and accuracy requirements remain at unprecedented levels of stringency, analysis of possible instrument technology indicates that such sensors are more than feasible. Radiative transfer model calculations, an instrument model with representative errors, and a simple retrieval approach complete the cycle from "nature" run to "pseudodata" CO2. Several mission and instrument configuration options are examined, and the sensitivity to key design variables is shown. Examples are also shown of how the resulting pseudo-measurements might be used to address key carbon cycle science questions.

Kawa, Stephan Randy↗
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 NTRS2014

Status of the NASA GMAO Observing System Simulation Experiment

An Observing System Simulation Experiment (OSSE) is a pure modeling study used when actual observations are too expensive or difficult to obtain. OSSEs are valuable tools for determining the potential impact of new observing systems on numerical weather forecasts and for evaluation of data assimilation systems (DAS). An OSSE has been developed at the NASA Global Modeling and Assimilation Office (GMAO, Errico et al 2013). The GMAO OSSE uses a 13-month integration of the European Centre for Medium- Range Weather Forecasts 2005 operational model at T511/L91 resolution for the Nature Run (NR). Synthetic observations have been updated so that they are based on real observations during the summer of 2013. The emulated observation types include AMSU-A, MHS, IASI, AIRS, and HIRS4 radiance data, GPS-RO, and conventional types including aircraft, rawinsonde, profiler, surface, and satellite winds. The synthetic satellite wind observations are colocated with the NR cloud fields, and the rawinsondes are advected during ascent using the NR wind fields. Data counts for the synthetic observations are matched as closely as possible to real data counts, as shown in Figure 2. Errors are added to the synthetic observations to emulate representativeness and instrument errors. The synthetic errors are calibrated so that the statistics of observation innovation and analysis increments in the OSSE are similar to the same statistics for assimilation of real observations, in an iterative method described by Errico et al (2013). The standard deviations of observation minus forecast (xo-H(xb)) are compared for the OSSE and real data in Figure 3. The synthetic errors include both random, uncorrelated errors, and an additional correlated error component for some observational types. Vertically correlated errors are included for conventional sounding data and GPS-RO, and channel correlated errors are introduced to AIRS and IASI (Figure 4). HIRS, AMSU-A, and MHS have a component of horizontally correlated error. The forecast model used by the GMAO OSSE is the Goddard Earth Observing System Model, Version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) DAS. The model version has been updated to v. 5.13.3, corresponding to the current operational model. Forecasts are run on a cube-sphere grid with 180 points along each edge of the cube (approximately 0.5 degree horizontal resolution) with 72 vertical levels. The DAS is cycled at 6-hour intervals, with 240 hour forecasts launched daily at 0000 UTC. Evaluation of the forecasting skill for July and August is currently underway. Prior versions of the GMAO OSSE have been found to have greater forecasting skill than real world forecasts. It is anticipated that similar forecast skill will be found in the updated OSSE.

OSSE↗