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NASA NTRSDate not supplied

Importance of Radiative Transfer Models in Atmospheric Remote Sensing

Radiative transfer models (RTMs) play a significant role in the development of satellite instruments for remote sensing applications. These models simulate electromagnetic radiation's propagation through the atmosphere, providing valuable insights into atmosphere-radiation interactions. RTMs facilitate the optimization of satellite instrument designs, ensuring their ability to measure targeted atmospheric and surface properties accurately. Moreover, they aid in simulating instrument’s measurements under various atmospheric conditions, enabling calibration and validation processes to enhance data quality and reliability. RTMs are extensively used in the Observing System Simulation Experiments (OSSE), to generate synthetic observations. By incorporating RTMs into OSSE, we can assess the potential impact of future satellite missions, sensor configurations, and data assimilation techniques. This approach allows for the optimization of satellite instruments and constellations and the evaluation of their influence on weather forecasting, climate monitoring, and other Earth science applications. Another crucial application area of RT models is data assimilation, where they play a fundamental role in combining satellite observations with numerical models to improve atmospheric and environmental predictions. RTMs provide the link between observed radiances and atmospheric parameters, enhancing the accuracy of numerical models and generating more reliable forecasts for weather events, air quality assessments, and climate projections. Moreover, adapting RT models to capture the intricate radiation interactions within the Planetary Boundary Layer will significantly contribute to improving weather forecasting and climate change projections. Current community radiative transfer (RT) models are primarily developed and optimized for operational data assimilation of satellite observations. These models excel at assimilating satellite data into numerical weather prediction models to improve forecast accuracy. However, their focus on data assimilation limits their suitability for other important applications, such as satellite instrument development, OSSE, and Planetary Boundary Layer (PBL) studies. Moreover, for PBL studies, RT models need to be adapted to capture the intricate radiation interactions within this crucial atmospheric layer. Developing RT models that can represent the PBL's unique characteristics, such as surface interactions, will contribute significantly to understanding and predicting weather phenomena, air quality, and climate dynamics. This abstract provides a comprehensive overview of the current status of RT models and highlights their limitations concerning satellite instrument development, OSSE, and PBL studies. Addressing these shortcomings requires concerted efforts to enhance RT models' capabilities and expand their applications beyond data assimilation. By investing in research and development to improve these

Isaac Moradi↗

NASA NTRSDate not supplied

Leveraging Observing System Simulation Experiments for Satellite Instrument Design: Advancements and Challenges

This abstract highlights NASA's OSSE framework, developed at the Global Modeling and Assimilation Office, which has become a widely adopted tool for evaluating the impact of new observations on weather forecasts. The framework involves simulating realistic observations by introducing errors to mimic real-world scenarios. By utilizing advanced radiative transfer modeling, the accuracy of simulated observations is significantly improved, enhancing the reliability of experimental outcomes. The talk focuses on recent progress and challenges in three key areas. Firstly, it explores the techniques used to simulate synthetic observations, a crucial aspect of OSSE experiments. Secondly, the presentation also discusses the challenges of examining the observation errors that need to be added to the perfect observations to generate realistic simulated observations. Lastly, the assimilation of synthetic observations and their impact on forecast skills is evaluated, providing valuable insights for optimizing current and future observing systems for improving the weather forecasts. By sharing recent advancements and acknowledging challenges, this presentation seeks to inspire researchers to leverage the OSSE framework in weather forecasting. NASA's OSSE techniques hold promise for a better understanding of the atmosphere and the development of more accurate and reliable weather predictions.

Isaac Moradi↗

DOE OSTI2019

A New Study on Characterization and Properties of Natural Fibers Obtained from Olive Tree (Olea europaea L.) Residues

Nowadays, fibers extracted from natural resources have a wide range of promising applications, including the prospect to be used as reinforcing material in polymer composites. In this context, the purpose of this study has been to extract fibers from different parts of the olive tree (leaves, small and large branches) and characterize their physico-chemical, thermal, and morphological properties using advanced equipment. Olive leaf (OL) fiber showed asymmetrical size distribution, as compared to those extracted from small olive branches (OSS), and big olive branches (OBS). The OL fiber exhibited 64.1% crystallinity, which is lower than that of the OSS fiber—with 65.4% crystallinity. Thermal analysis revealed that the OBS and OSS fibers are more thermally stable, compared to the OL fibers. The obtained results concluded that olive tree fibers can be suitable for being used as reinforcement material to develop polymer composites for various lightweight applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Formation of Inorganic Sulfate and Volatile Nonsulfated Products from Heterogeneous Hydroxyl Radical Oxidation of 2-Methyltetrol Sulfate Aerosols: Mechanisms and Atmospheric Implications

Chemical transformation of 2-methyltetrol sulfates (2-MTS), key isoprene-derived secondary organic aerosol (SOA) constituents, through heterogeneous hydroxyl radical ( • OH) oxidation can result in the formation of previously unidentified atmospheric organosulfates (OSs). However, detected OSs cannot fully account for the sulfur content released from reacted 2-MTS, indicating the existence of sulfur in forms other than OSs, such as inorganic sulfates. This work investigated the formation of inorganic sulfates through heterogeneous • OH oxidation of 2-MTS aerosols. Remarkably, high yields of inorganic sulfates, defined as the moles of inorganic sulfates produced per mole of reacted 2-MTS, were observed in the range from 0.48 ± 0.07 to 0.68 ± 0.07. These could be explained by the production of sulfate (SO 4 •- ) and sulfite (SO 3 •- ) radicals through the cleavage of C-O(S) and (C)O-S bonds, followed by aerosol-phase reactions. Additionally, non-sulfated products resulting from bond cleavage were likely volatile and evaporated into gas phase, as evidenced by observed aerosol mass loss (up to 25%) and concurrent size reduction upon oxidation. This investigation highlights the significant transformation of sulfur from its organic to inorganic forms during the heterogeneous oxidation of 2-MTS aerosols, potentially influencing the physicochemical properties and environmental impacts of isoprene-derived SOA.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Open-Source and FAIR Research Software for Proteomics

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

A Hybrid Global Ocean Data Assimilation System at NCEP

Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. Finally, the Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1995

Joint ROSAT-Compton GRO observations of the X-ray bright Seyfert galaxy IC 4329A

We report a simultaneous ROSAT and Gamma Ray Observatory (GRO) observation of the X-ray-bright Seyfert galaxy IC 4329A. For the GRO Oriented Scintillation Spectrometer Experiment (OSSE) detector, we also present the sum of the data for this and earlier observations. The overall spectrum is very well described as a power law with an energy spectral index of approximately 1 absorbed at low energies plus a strong Compton reflection component, typical for Seyfert 1 galaxies. The low energy absorption can be well described by a sum of a neutral column density of approximately 3 x 10(exp 21)sq cm, most of which is associated with the edge-on galactic disk of IC 4329A, plus an edgelike feature at approximately 700 eV; this feature implies either complex absorption (due to additional ionized material, or due to a partial covering), or a soft excess. The data only weakly constrain the presence of a high-energy cutoff in the underlying power law; they are compatible with an exponential cutoff at any energy E(sub c) approximately greater than 100 keV. The relative steepness of the OSSE data, with the power-law energy index of 1.6 +/- 0.2, can be accounted for entirely by the contribution of the high-energy tail of the reflection component when E(sub c) approaches infinity. (We find that the definite cutoff at an energy E(sub c)approximately 130 keV suggested in the recently published analysis of the OSSE data for this subject is due to a data reduction error.) Including nonsimultaneous Ginga observations with 2 keV fluxes matching well that of ROSAT gives us likely broad-band X-ray/gamma-ray spectra of the object from approximately 0.1 keV up to several hundred keV. We also report the ROSAT spectrum of the companion object to the Seyfert galaxy, the elliptical galaxy IC 4329.

Madejski, G. M.↗

NASA NTRS1995

Observations of a large flare in GX 1+4 with the Compton gamma ray observatory

The pulsating X-ray binary GX 1+4 (4U 1728-24) was observed by Oriented Scintillation Spectrometer Experiment (OSSE) onboard the Compton Gamma Ray Observatory (CGRO) from 9 to 21 September 1993 as a target of oppurtunity after Burst and Transient Source Experiment (BATSE) had detected the onset of a large flare by the greatly increased pulsed flux at the period of approximately 2 min. The total flux in the 40-100 keV range as observed by the OSSE reached its maximum of 83 mCrab on 14/15 September, after which it fell sharply to about 31 mCrab within 2 days. The spectrum is well described by thermal type spectra. The characteristic temperature of the average OSSE spectrum for a thermal Bremsstrahlung model is kT = (35.5 +/- 0.5) keV. A single power law can be ruled out. There is evidence for a hardening of the spectrum with decreasing intensity at the end of the flare. The barycentric pulse period was (120.567 +/- 0.005) s on 5 September. The average spin-down rate as taken from the standard BATSE analysis was dP/dt = 0.0105 s/day, and constant over the time of the flare. A further target of oppurtunity (TOO) observation with the ROSAT Position Sensitive Proportional Counter (PSPC) on 18 September led to the first detection of the source with a reflecting X-ray telescope and to a signifcantly improved position: RA(2000) = 17h 32m 2.1s and DEC(2000) = -24 deg 44 min 44 sec. This position 3.5 sec from V2116 Oph, with a 90% error radius of 8 sec is the most accurate so far obtained with an X-ray instrument, thus confirming the identification with the suspected stellar counterpart.

Staubert, R.↗

NASA NTRS2000

Education and Public Outreach

This Annual Report is a summary of nearly 400 Education and Public Outreach (E/PO) products and activities developed or carried out in FY2000 under NASA's Office of Space Science (OSS) E/PO program. It includes products and activities developed by OSS missions and research programs, innovative space science concepts developed under the Initiative to Develop Education through Astronomy and Space Science (IDEAS) Program, projects initiated under the Minority University Education and Research Partnership Initiative in Space Science, and a number of additional comprehensive or special purpose programs managed by OSS at NASA Headquarters. Taking into account the fact that many of the activities reported involve multiple events that took place in a variety of venues, the total number of E/PO events reported for FY2000 is over 1,500, with events having taken place in all 50 states, the District of Columbia, one US Territory (Guam), and four foreign nations (Australia, Canada, Mexico, and Peru).

Sakimoto, Philip↗

NASA NTRS2002

Observing System Simulation Experiments to Define Lidar Wind Requirements

Since the advent of meteorological satellites in the 1960's, numerous experiments have been conducted in order to evaluate the impact of these and other data on atmospheric analysis and prediction. Such studies have included both OSE'S and OSSE's. 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 available. (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 conducted 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 ( [1], [2], [3]). In addition, OSSE's have been run to evaluate trade-offs in the design of observing systems and observing networks ([4], [5]), and to test new methodology for data assimilation ([6]).

Atlas, Robert↗

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 NTRS2004

The Impact of Current and Future Polar Orbiting Satellite Data on Numerical Weather Prediction at NASA/GSFC

The lack of adequate observational data continues to be recognized as a major factor limiting both atmospheric research and numerical prediction on a variety of temporal and spatial scales. Since the advent of meteorological satellites in the 1960's, a considerable research effort has been directed toward the design of space-borne meteorological sensors, the development of optimal methods for the utilization of these data, (and an assessment of the influence of existing satellite data and the potential influence of future satellite observations on numerical weather prediction. This has included both Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs). OSEs are conducted to evaluate the impact of specific observations or classes of observations on analyses and forecasts. While OSEs are performed with existing data, OSSEs are conducted to evaluate the potential for future observing systems to improve-NWP, as well as to evaluate trade-offs in observing system design, and to develop and test improved methods for data assimilation. At the conference, results from OSEs to evaluate satellite data sets that have recently become available to the global observing system, such as AIRS and Seawinds, and results from OSSEs to determine the potential impact of space-based lidar winds will be presented.

Atlas, Robert↗

NASA NTRS2013

Ionospheric Simulation System for Satellite Observations and Global Assimilative Modeling Experiments (ISOGAME)

ISOGAME is designed and developed to assess quantitatively the impact of new observation systems on the capability of imaging and modeling the ionosphere. With ISOGAME, one can perform observation system simulation experiments (OSSEs). A typical OSSE using ISOGAME would involve: (1) simulating various ionospheric conditions on global scales; (2) simulating ionospheric measurements made from a constellation of low-Earth-orbiters (LEOs), particularly Global Navigation Satellite System (GNSS) radio occultation data, and from ground-based global GNSS networks; (3) conducting ionospheric data assimilation experiments with the Global Assimilative Ionospheric Model (GAIM); and (4) analyzing modeling results with visualization tools. ISOGAME can provide quantitative assessment of the accuracy of assimilative modeling with the interested observation system. Other observation systems besides those based on GNSS are also possible to analyze. The system is composed of a suite of software that combines the GAIM, including a 4D first-principles ionospheric model and data assimilation modules, an Internal Reference Ionosphere (IRI) model that has been developed by international ionospheric research communities, observation simulator, visualization software, and orbit design, simulation, and optimization software. The core GAIM model used in ISOGAME is based on the GAIM++ code (written in C++) that includes a new high-fidelity geomagnetic field representation (multi-dipole). New visualization tools and analysis algorithms for the OSSEs are now part of ISOGAME.

Pi, Xiaoqing↗

NASA NTRS2014

Applied Meteorology Unit (AMU) Quarterly Report First Quarter FY-14

NASA's LSP and other programs at Vandenberg Air Force Base (VAFB) use wind forecasts issued by the 30th Operational Support Squadron (30 OSS) to determine if they need to limit activities or protect property such as a launch vehicle due to the occurrence of warning level winds at VAFB in California. The 30 OSS tasked the AMU to provide a wind forecasting capability to improve wind warning forecasts and enhance the safety of their customers' operations. This would allow 30 OSS forecasters to evaluate pressure gradient thresholds between pairs of regional observing stations to help determine the onset and duration of warning category winds. Development of such a tool will require that solid relationships exist between wind speed and the pressure gradient of one or more station pairs. As part of this task, the AMU will also create a statistical climatology of meteorological observations from the VAFB wind towers.

Forecasting↗

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 NTRS2021

Origins Survey Spectrometer: revealing the hearts of distant galaxies and forming planetary systems with far-IR spectroscopy

The Origins Survey Spectrometer (OSS) is a multi-purpose far-IR spectrograph for Origins. Operating at the photon background limit, OSS covers the 25- to 588-μm wavelength range instantaneously at a resolving power (R) of 300 using six logarithmically spaced grating modules. Each module couples at least 30 and up to 100 spatial beams simultaneously, enabling true [three-dimensional (3D)] spectral mapping. In addition, OSS provides two high-resolution modes. The first inserts a long-path Fourier-transform spectrometer (FTS) into a portion of the incoming light in advance of the grating backends, enabling R up to 43,000 × [λ/112 μm] , while preserving the grating-based sensitivity for line detection. The second incorporates a scanning etalon in series with the FTS to provide R up to 300,000 for the 100-to 200-μm range.

Charles M. Bradford↗

NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗

NASA NTRSDate not supplied

Open-Source Science-led Development of the AOS Mission Science Data System (SDS)

David M. Giles↗