SEARCH · Search NASA

Results for “data model”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27

All sources NASA NTRS DOE OSTI

NASA NTRS2022

L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation and Soil Salinity

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (SM), soil temperature, soil salinity and vegetation, using L-band microwave brightness temperature (TB) observations and retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions, Catchment Land Surface Model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based SM outperformed CLSM SM compared to field data, and forward L-band TB simulations derived from in situ SM and temperature performed better than those derived from CLSM estimates relative to SMOS TB observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the TB simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize TB simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multi-angular SMOS TB data and constraints of CLSM SM and temperature. However, the latter modeled SM was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo and surface roughness resulted in realistic values.

L-Band↗

NASA NTRS2015

The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4: Technical Report Series on Global Modeling and Data Assimilation - Volume 38

Biomass burning is an important source of particulates and trace gases and a major element of the terrestrial carbon cycle. Well constrained emissions from vegetation fires are needed to model direct and indirect effects of biomass burning aerosols, to model homogeneous and heterogeneous chemistry in the atmosphere, and to perform credible Earth system analysis, and climate and air pollution studies. To improve the performance of NASA Goddard Earth Observing System Model (GEOS) in the areas of atmospheric constituent modeling with a focus on biomass burning we developed the Quick Fire Emissions Dataset (QFED). The QFED emissions are based on the fire radiative power (top-down) approach and draw on the cloud correction method developed in the Global Fire Assimilation System (GFAS). Location and fire radiative power of fires are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 fire products (MOD14 and MYD14) and the MODIS Geolocation products (MOD03 and MYD03). QFED strengths are high spatial and temporal resolutions and near-real time availability. Daily mean emissions are available at 0.3125 times 0.25 degrees and in recent versions also at 0.1 times 0.1 degrees. QFED provides emissions of black carbon, organic carbon, sulfur dioxide, carbon monoxide, carbon dioxide, PM2.5, ammonia, nitrogen oxides, methyl ethyl ketone, propylene, ethane, propane, n- and i-butane, acetaldehyde, formaldehyde, acetone and methane. Two QFED product systems are maintained by the NASA Global Modeling and Assimilation Office (GMAO): one that produces near real-time daily emissions used operationally in the GEOS-5 Data Assimilation System, and one that produces an extended historical dataset with daily emissions from March 2000 to the present. The historical dataset also provides monthly mean and monthly climatological emissions.

Koster, Randal D.↗

NASA NTRS1988

Solar maximum mission fine pointing sun sensor dawn and dusk errors flight data and model analysis

SMM flight system control errors occurring at spacecraft dawn and dusk are analyzed. The errors are associated with the fine pointing sun sensor (FPSS), which is a primary component of the SMM attitude control system. It is shown that the source of the FPSS dawn/dusk distortion is the incomplete masking of sunlight reflected off the earth by the optical baffle covering the FPSS sensor heads onboard the SMM during periods of orbit dawn and dusk. For the most part, the modeled behavior of the FPSS under dawn and dusk lighting conditions matches the observed behavior in the SMM flight data.

Kulp, D. R.↗

DOE OSTI2026

Improved Muon Energy Estimation Using a Detailed Model of Multiple Coulomb Scattering in the MicroBooNE LArTPC

We present an improved technique for estimating a muon's energy by measuring the deflections along its path inside the MicroBooNE detector from multiple Coulomb scattering (MCS). This approach implements several innovations that better capture detector non-idealizations compared to previous MCS-based muon energy estimators. As a result, it achieves improved resolution, reduced bias, and better data-model agreement. Using model simulation, for fully contained events the estimated bias is within 1\% and the estimated resolution narrows from 10\% to 4.3\% as muon energy increases from 0.1\,GeV to 2\,GeV. For events with particles exiting the detector volume, at least a meter of reconstructed muon track, and a muon energy below 2\,GeV, the estimated bias is less than 2\% and the estimated resolution varies from 7\% to 17\% over muon energy. These demonstrate significant improvements over the performance of previous work using an MCS-based energy estimator at MicroBooNE~\cite{mcs_2017}, which exhibited approximately twice worse resolution and a bias of 20\% over the same energy region. Data-model goodness-of-fit studies are used to validate the estimator's performance on data, showing good agreement within model uncertainties.

Cooper-Troendle, London [U. Pittsburgh (main); Fer↗

DOE OSTI2024

Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

14 SOLAR ENERGY↗

NASA NTRS1990

Use of satellite data and modeling to assess the influence of stratospheric processes on the troposphere

Current research focused on the examination of the effects of ozone heating and Newtonian cooling on the linear stability of transient planetary waves. A finite difference numerical model was developed in which the basic state wind, temperature, and ozone distributions were constructed using ground-based and satellite data. Preliminary results indicate that ozone dynamics interaction may play a more important role in the stability and maintenance of planetary waves than previously thought, particularly in summer when the mean solar zenith angle is smallest and thus ozone heating largest. Examination of the finite amplitude interactions among radiation, ozone, and dynamics was carried out. Self-consistent, coupled equations governing the weakly nonlinear interactions are currently being analyzed to provide a better understanding of wave-mean flow interactions and ozone transport in a continuously stratified model of the troposphere-stratosphere coupled system.

Nathan, Terrence↗

NASA NTRS1993

Modeling and data assimilation for Mars Observer

The Mars Observer mission will return a substantial set of meteorological data for the atmosphere of Mars, principally in the form of thermal infrared soundings from the Pressure Modulator Infrared Radiometer (PMIRR). The dataset will enable global studies of a range of dynamical and physical phenomena in the martian atmosphere. The solar orbital configuration of Mars Observer, however, means that the coverage of the martian atmosphere and surface by remote sounding instruments will be asynchronous. This leads to difficulties of interpretation when studying synoptic-scale phenomena with timescales of a few days. In an attempt to overcome such problems we propose to use data assimilation techniques, as currently employed for operational weather forecasting on the Earth, in conjunction with a Martian General Circulation Model (MGCM) that is under development at Oxford and Reading Universities. An overview of the model and assimilation scheme will be given, with reference to problems encountered so far in applying such methods to remote-sensing observations of the martian atmosphere.

Lewis, Stephen R.↗

NASA NTRS1989

Use of satellite data and modeling to asses the influence of stratospheric processes on the troposphere

The research is comprised of the following tasks: use of simple analytical and numerical models of a coupled troposphere-stratosphere system to examine the effects of radiation and ozone on planetary wave dynamics and the tropospheric circulation; use of satellite data obtained from the Nimbus 7 Limb Infrared Monitor of the Stratosphere (LIMS) instrument and Solar Backscattered Ultraviolet (SBUV) experiment, in conjunction with National Meteorological Center (NMC) data, to determine the planetary wave vertical structures, dominant wave spectra, ozone spectra, and time variations in diabatic heating rate; and synthesis of the modeling and observational results to provide a better understanding of the effects that stratospheric processes have on tropospheric dynamics.

Nathan, Terrence R.↗

DOE OSTI2024

Towards in-situ certification of additively manufactured parts: the vital roles of physics-based and data-driven models

Certifying additively manufactured (AM) parts in-situ at the completion of a build is an enticing prospect, as it can help reduce the high costs associated with post-build testing and evaluation. However, achieving this goal presents significant challenges that may keep it aspirational for the foreseeable future. Nonetheless, incremental progress can pave the way forward. A critical aspect of in-situ certification involves continuous quality checking due to the random nature of the AM process and the difficulties in detecting defects or anomalies once layers are built over. While real-time in-situ monitoring strategies assisted by machine learning (ML) play a pivotal role in auditing part quality, they must ideally be supported by real-time (or near real-time) adaptive process control enabled by ML-assisted decision-making. By analyzing in-situ monitoring data in real-time (or near real- time) to dynamically adjust manufacturing parameters, such intervention can ensure AM parts are built to meet stringent certification standards. This can be achieved virtually by using high-fidelity performance models for the physical testing and evaluation tasks. In this short editorial, we discuss the key contributions made by data-driven and physics-based models in providing intelligence to the monitoring and process control tasks underpinning in-situ certification and in the simulation of the build’s performance under test and service conditions. While our focus lies in metal AM, the concepts discussed here are also relevant to other AM processes.

: In-situ monitoring↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

Carter Hulsey↗

NASA NTRS2012

Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the other uses the daily SPoRT/MODIS GVFs. Finally, snapshots of the LIS land surface fields are used to initialize two different simulations of the NU-WRF, one running with climatology LIS and GVFs, and the other running with experimental LIS and NASA/SPoRT GVFs. In this paper/presentation, case study results will be highlighted in regions with significant differences in GVF between the NCEP climatology and SPoRT product during severe weather episodes.

Case, Jonathan L.↗

NASA NTRS2008

Performance of a Local Mesoscale Model with Data Denial

Forecasters at the 45th Weather Squadron (45 WS) use observations from the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) wind tower network and daily rawinsonde observations (RAOB) to issue and verify wind advisories, watches, and warnings for operations. They are also used by the Spaceflight Meteorology Group and Melbourne, Florida National Weather Service to initialize locally run mesoscale models. Due to impending budget cuts, some or all of the mainland wind towers and RAOBs may be eliminated, The loss of these data may significantly impact the forecast capability of the 45 WS and SMG. The Applied Meteorology Unit (AMU) was tasked to conduct an objective independent modeling study to determine how important these observations are to the accuracy of the model output used by the forecasters as input to their forecasts. To accomplish this, the AMU performed a sensitivity study using the Weather Research and Forecasting (WRF) model run with and without KSC/CCAFS wind tower and CCAFS RAOB observations and assessed the accuracy of model forecasts by comparing them to the observations.

Watson, Leela↗

DOE OSTI2024

A New Coupled Biogeochemical Modeling Approach Provides Accurate Predictions of Methane and Carbon Dioxide Fluxes Across Diverse Tidal Wetlands

Abstract Tidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO 2 ) and methane (CH 4 ) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO 2 and CH 4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO 2 and CH 4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (R eco ) modules explained, on average, 73% of the variation in CO 2 exchange with low model error (normalized root mean square error (nRMSE) <1). The CH 4 module also explained the majority of variance in CH 4 emissions in validation sites ( R 2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on R eco , (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH 4 emissions, and (d) CH 4 responses to non‐periodic changes in salinity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Information theory optimization of signals from small-angle scattering measurements

Small-angle X-ray scattering (SAXS) of particles in solution informs on the conformational states and assemblies of biological macromolecules (bioSAXS) outside of cryo- and solid-state conditions. In bioSAXS, the SAXS measurement under dilute conditions is resolution limited, and through an inverse Fourier transform, the measured SAXS intensities directly relate to the physical space occupied by the particles via the P (r)-distribution. Yet, this inverse transform of SAXS data has been historically cast as an ill-posed, ill-conditioned problem requiring an indirect approach. Here, we show that through the applications of matrix and information theories, the inverse transform of SAXS intensity data is a well-conditioned problem. The so-called ill-conditioning of the inverse problem is directly related to the Shannon number. By exploiting the oversampling enabled by modern detectors, a direct inverse Fourier transform of the SAXS data is possible, provided the recovered information does not exceed the Shannon number. The Shannon limit corresponds to the maximum number of significant singular values that can be recovered in a SAXS experiment, suggesting this relationship is a fundamental property of band-limited inverse integral transform problems. This correspondence reduces the complexity of the inverse problem to the Shannon limit and maximum dimension. We propose a hybrid scoring function using an information theory framework that assesses both the quality of the model-data fit as well as the quality of the recovered P (r)-distribution. The hybrid score utilizes the Akaike information criteria and Durbin-Watson statistic that considers parameter-model complexity, i.e., degrees of freedom, and the randomness of the model-data residuals. The described tests and findings extend the boundaries for bioSAXS by completing the information theory formalism initiated by Peter B. Moore to enable a quantitative measure of resolution in SAXS, robustly determine maximum dimension, and more precisely define the best parameter model appropriately representing the observed scattering data.

Rambo, Robert P. [Science and Technology Facilitie↗

DOE OSTI2025

Distribution Grid Model Publication Investigation

Interest in the external exchange of distribution grid model data is growing around the world, driven largely by the challenges and opportunities presented by the increasing amount of generation, storage, and flexible load being embedded within the distribution grid. This report provides an overview of the current state of distribution grid model data sharing, with a focus on the industry-leading activities currently underway in Great Britain (GB). A second report will explore opportunities for external distribution grid model sharing in the United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2019

Assimilated Hydrological Data at NASA GES DISC with Examples of Extreme Events

Extreme weather and climate events, such as heavy rainfall, heatwave, floods and droughts, and strong wind, can have devastating impacts on society. NASA and NOAA, based on independent analyses, recently announced that global surface temperatures in 2018 are the fourth warmest since 1880, behind only those of 2016, 2017, and 2015 (nasa.gov). Also in 2018, the United States experienced 14 billion-dollar disasters, ranking as the fourth highest total number of such events, behind only the years 2017, 2011, and 2016 (climate.gov). Many research studies have focused on acquiring observational and modeling data, to reveal linkages between increasing extreme events, global water and energy cycle, and global climate change. However, draw conclusions is still a challenge. NASA Goddard Earth Sciences Data and Information Services Center is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remotely-sensed and model data and provides reliable and robust data access and services to users worldwide. This presentation provides a few examples of extreme event study that use Land Surface Model (LSM) assimilated, quality-controlled, and spatially and temporally consistent, hydrological data from the GES DISC. Also provided is a summary table for the hydrological data holdings, along with discussions of recent updates to data and data services.

Rui, Hualan↗

NASA NTRS2013

Combining Satellite Data and Models to Assess the Impacts of Urbanization on the Continental US Surface Climate

Urbanization is one of the most important and long lasting forms of land transformation. Urbanization affects the surface climate in different ways: (1) by reduction of the vegetation fraction causing subsequent reduction in photosynthesis and plant s water transpiration, (2) by alternation of surface runoff and infiltration and their impacts on soil moisture and the water table, (3) by change in the surface albedo and surface energy partitioning, and (4) by transformation of the surface roughness length and modification of surface fluxes. Land cover and land use change maps including urban areas have been developed and will be used in a suite of land surface models of different complexity to assess the impacts of urbanization on the continental US surface climate. These maps and datasets based on a full range of available satellite data and ground observations will be used to characterize distant-past (pre-urban), recent-past (2001), present (2010), and near future (2020) land cover and land use changes. The main objective of the project is to assess the impacts of these land transformation on past, current and near-future climate and the potential feedbacks from these changes on the atmospheric, hydrologic, biological, and socio-economic properties beyond the immediate metropolitan regions of cities and their near suburbs. The WRF modeling system will be used to explore the nature and the magnitude of the two-way interactions between urban lands and the atmosphere and assess the overall regional dynamic effect of urban expansion on the northeastern US weather and climate

Bounoua, L.↗