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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

MPAS - Ocean Simulation Quality for Variable-Resolution North American Coastal Meshes

Climate model components utilizing unstructured meshes enable variableresolution, regionally enhanced simulations within global domains. Here we investigate the relationship between mesh quality and simulation statistics using the JIGSAW unstructured meshing library and the Model for Prediction Across ScalesOcean (MPASOcean) with a focus on Gulf Stream dynamics. In the base configuration, the refined region employs 8 km cells that extend 400 km from the coast of North America. This coastal refined region is embedded within a lowresolution global domain, with cell size varying latitudinally between 30 and 60 km. The resolution transition region between the refined region and background mesh is 600 km wide. Three sensitivity tests are conducted: 1) the quality of meshes is intentionally degraded so that horizontal cells are progressively more distorted; 2) the transition region from high to low resolution is steepened; and 3) resolution of the coastal refinement region is varied from 30 km to 8 km. Overall, the ocean simulations are shown to be robust to mesh resolution and quality alterations. Meshes that are substantially degraded still produce realistic currents, with Southern Ocean transports within 0.4% and Gulf Stream transports within 12% of highquality mesh results. The narrowest transition case of 100 km did not produce any spurious effects. Refined regions with high resolution produce eddy kinetic energy and sea surface height variability that are similar to the highresolution reference simulation. These results provide heuristics for the design criteria of variableresolution climate model domains.

Hoch, Kristin E.↗
DOE OSTI2018

Global carbon intensity of crude oil production

Producing, transporting, and refining crude oil into fuels such as gasoline and diesel accounts for ~15 to 40% of the “well-to-wheels” life-cycle greenhouse gas (GHG) emissions of transport fuels. Reducing emissions from petroleum production is of particular importance, as current transport fleets are almost entirely dependent on liquid petroleum products, and many uses of petroleum have limited prospects for near-term substitution (e.g., air travel). Better understanding of crude oil GHG emissions can help to quantify the benefits of alternative fuels and identify the most cost-effective opportunities for oil-sector emissions reductions. Yet, while regulations are beginning to address petroleum sector GHG emissions, and private investors are beginning to consider climate-related risk in oil investments, such efforts have generally struggled with methodological and data challenges. First, no single method exists for measuring the carbon intensity (CI) of oils. Second, there is a lack of comprehensive geographically rich datasets that would allow evaluation and monitoring of life-cycle emissions from oils. We have previously worked to address the first challenge by developing open-source oil-sector CI modeling tools [OPGEE, supplementary materials (SM) 1.1]. Furthermore, we address the second challenge by using these tools to model well-to-refinery CI of all major active oil fields globally—and to identify major drivers of these emissions.

02 PETROLEUM↗
DOE OSTI2023

Validation of LOCA2 and STAR-ESDM Statistically Downscaled Products

The National Climate Assessment (NCA) is the preeminent national report examining current and future risks posed by climate change. Countless agencies, policymakers, stakeholders and other end-users rely upon guidance from the NCA to plan for an uncertain future. These groups all depend on modern curated data, provided alongside the NCA, to quantify the impact of climate change on metrics of relevance for their decision processes. In its fifth iteration (NCA5), two statistically downscaled ensemble products, each providing data at grid spacing of approximately 5km over the contiguous United States, were selected to accompany the report. These include LOCalized Analogs version 2 (LOCA2) and Seasonal Trends and Analysis of Residuals Empirical-Statistical Downscaling Model (STAR-ESDM). Both data products are produced through a process known as statistical downscaling, where relatively coarse Global Climate Model (GCM) data is refined to locally relevant scales through the application of scientifically-supported empirical and algorithmic relationships. In support of the NCA effort, this report provides an independent validation of these two products against historical observations, with a focus on precipitation and near-surface temperature variables. Based on the results of this validation, several recommendations are provided related to the use of these data products. The structure of this report is as follows: In section 2, we review three gridded observational products that are used as part of our intercomparison. In section 3, we describe the two statistical downscaling techniques and their corresponding datasets that are the focus of this study. In section 4, the methodology we employ for validation is described. Section 5 provides results of the validation, which in turn motivate our recommendations on the use of these data products. A brief summary is provided in section 6.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

SAR - An instrument for planetary geodesy and navigation

Analysis to define an optimal data plan and to define achievable accuracy levels in determination of the physical parameters, global topography of the planet, and refined orbital element estimates of a spacecraft borne synthetic aperture radar (SAR) placed in a near polar circular orbit of 300 km altitude around the planet Venus is presented. It is shown that radar surface images can be used to improve knowledge of physical parameters of Venus, and the improvement in orbit accuracy can reduce navigational uncertainties derivable from conventional Doppler ratio tracking data, yielding a more precise history of the orbits for sensing the low order gravity field of the planet. Data plan strategies in terms of selection of ground points and their spatial distribution are related to achievable accuracy of the solution parameters, and the influence of spacecraft orbit errors on the solution accuracy was discussed.

Mohan, S. N.↗
NASA NTRS1984

Some aspects of understanding changes in the global carbon cycle

The collective character of carbon exchanges between the atmosphere and other pools is partially revealed by comparing the record of CO2 concentration beginning in 1958 with estimates of the releases from fossil fuels during this period. In analyzing the secular increase in CO2 concentration induced by fossil fuel use, the atmosphere is generally treated as a single well-mixed reservoir; however, to study finer structure in the CO2 records, the influence of atmospheric circulation must be more carefully considered. The rate of carbon uptake by the oceans, the primary sink for fossil fuel CO2, is assessed more reliably than influences on the atmosphere due to interactions with other pools. Models of the global carbon cycle are being substantially refined while data that reflect the response of the cycle to fossil fuel use and other perturbations are being extended.

Emanuel, W. R.↗
DOE OSTI2024

Data for Microbial-Explicit Processes and Refined Perennial Plant Traits Improve Modeled Ecosystem Carbon Dynamics

Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation.

Field Data↗
NASA NTRS2003

Studies of Trace Gas Chemical Cycles Using Inverse Methods and Global Chemical Transport Models

We report progress in the first year, and summarize proposed work for the second year of the three-year dynamical-chemical modeling project devoted to: (a) development, testing, and refining of inverse methods for determining regional and global transient source and sink strengths for long lived gases important in ozone depletion and climate forcing, (b) utilization of inverse methods to determine these source/sink strengths using either MATCH (Model for Atmospheric Transport and Chemistry) which is based on analyzed observed wind fields or back-trajectories computed from these wind fields, (c) determination of global (and perhaps regional) average hydroxyl radical concentrations using inverse methods with multiple titrating gases, and (d) computation of the lifetimes and spatially resolved destruction rates of trace gases using 3D models. Important goals include determination of regional source strengths of methane, nitrous oxide, methyl bromide, and other climatically and chemically important biogenic/anthropogenic trace gases and also of halocarbons restricted by the Montreal protocol and its follow-on agreements and hydrohalocarbons now used as alternatives to the restricted halocarbons.

Prinn, Ronald G.↗
NASA NTRS1979

Development and analysis of a twelfth degree and order gravity model for Mars

Satellite geodesy techniques previously applied to artificial earth satellites have been extended to obtain a high-resolution gravity field for Mars. Two-way Doppler data collected by 10 Deep Space Network (DSN) stations during Mariner 9 and Viking 1 and 2 missions have been processed to obtain a twelfth degree and order spherical harmonic model for the martian gravitational potential. The quality of this model was evaluated by examining the rms residuals within the fit and the ability of the model to predict the spacecraft state beyond the fit. Both indicators show that more data and higher degree and order harmonics will be required to further refine our knowledge of the martian gravity field. The model presented shows much promise, since it resolves local gravity features which correlate highly with the martian topography. An isostatic analysis based on this model, as well as an error analysis, shows rather complete compensation on a global (long wavelength) scale. Though further model refinements are necessary to be certain, local (short wavelength) features such as the shield volcanos in Tharsis appear to be uncompensated. These are interpreted to place some bounds on the internal structure of Mars.

Christensen, E. J.↗
DOE OSTI2017

Predicting the quantifiable impacts of ISO 50001 on climate change mitigation

The ISO 50001-Energy management standard provides a continual improvement framework for organizations to reduce their energy consumption, which in the industrial and commercial (service) sectors, accounts for nearly 40% of global greenhouse gas emissions. Reducing this energy consumption will be critical for countries to achieve their national greenhouse gas reduction commitments. Several national policies already support ISO 50001; however, there is no transparent, consistent process to estimate the potential impacts of its implementation. This paper presents the ISO 50001 Impacts Methodology, an internationally-developed methodology to calculate these impacts at a national, regional, or global scale suitable for use by policymakers. The recently-formed ISO 50001 Global Impacts Research Network provides a forum for policymakers to refine and encourage use of the methodology. Using this methodology, a scenario with 50% of projected global industrial and service sector energy consumption under ISO 50001 management by 2030 would generate cumulative primary energy savings of approximately 105 EJ, cost savings of nearly US $700 billion (discounted to 2016 net present value), and 6500 million metric tons (Mt) of avoided CO 2 emissions. The avoided annual CO 2 emissions in 2030 alone are equivalent to removing 210 million passenger vehicles from the road.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1988

A global-local finite element method suitable for parallel computations

A global-local finite element analysis procedure is developed based on the fast convergent nature of FEM in displacement. A special scheme is used to utilize the global displacement solution as boundary conditions for local regions of interest. In the local region, a refined mesh is used for further stress analysis. This global-local procedure can be easily programmed in parallel on MIMD multi-processor computers for significant time savings. A Sequent Balance 21000 system is used for demonstrating the parallel programming.

Sun, C. T.↗
NASA NTRS1993

Solution of free-boundary problems using finite-element/Newton methods and locally refined grids - Application to analysis of solidification microstructure

A new method is presented for the solution of free-boundary problems using Lagrangian finite element approximations defined on locally refined grids. The formulation allows for direct transition from coarse to fine grids without introducing non-conforming basis functions. The calculation of elemental stiffness matrices and residual vectors are unaffected by changes in the refinement level, which are accounted for in the loading of elemental data to the global stiffness matrix and residual vector. This technique for local mesh refinement is combined with recently developed mapping methods and Newton's method to form an efficient algorithm for the solution of free-boundary problems, as demonstrated here by sample calculations of cellular interfacial microstructure during directional solidification of a binary alloy.

Tsiveriotis, K.↗
NASA NTRS2019

Diurnal Cycle of IMERG V06 Precipitation

This study demonstrates the maturing ability of the half-hourly precipitation estimates from the Integrated Multi-satellitE Retrievals for GPM (IMERG) for use in global analyses of the diurnal cycle. The refined intercalibration and interpolation between the sensors in V06 leads to greater consistency in the precipitation retrievals over different hours of the day. Evaluation against ground measurements suggests a slight lag in the diurnal phase of only +0.59 h. We demonstrate the diurnal cycle over different regions around the globe, including the Maritime Continent, where accurate representation of precipitation variability in global models remains a challenge. Using examples over Singapore, Bangladesh, and Lake Victoria, we reveal the intricate interplay between diurnal and seasonal variability. This study demonstrates the unprecedented capability of IMERG in capturing the diurnal cycle of precipitation globally, potentially advancing our understanding in regions of sparse ground measurements and supporting improvements in its representation in global models.

Jackson Tan↗
NASA NTRS2012

Multiscale Studies of the Three-Dimensional Dayside X-Line

We review recent experience from the Cluster, Double Star, and THEMIS missions for lessons that apply to the upcoming Magnetospheric Multiscale Mission (MMS) being developed for launch in 2014. On global scales, simulation and statistical studies lead to mean congurations of dayside reconnection, implying specific relative alignments of the inflow magnetic fields and X-line, with implications for MMS operations designed to maximize the number of close encounters with the diffusion region. At intermediate MHD-to-ion scales, reconstruction of features created by one or two X-lines have developed to the point where data from a cluster of spacecraft can determine their temporal trends and the approximate three-dimensional X-line structure. Recent petascale particle-in-cell (PIC) simulations of reconnection encompass three spatial dimensions with excellent resolution, and make striking predictions of electron scale physics that creates complex interacting flux ropes under component reconnection. High time resolution measurements from MMS will determine the detailed electron scale kinetics embedded within the global and MHDion scale contexts. These developments will lead to the refinement of our three-dimensional multiscale picture of reconnection, yielding improved understanding of the global, MHD, and local physics controlling the onset or quenching, variability, and mean rate of reconnection. This in turn will enable improved predictability of the structural features created by transient reconnection, and their space weather consequences.

Reconnection↗
NASA NTRSDate not supplied

Improving Simulation of Irrigation in a Global Land Surface Model

Studies have shown that irrigated agriculture has substantial effects on local to regional hydrologic and atmospheric processes. These effects include increased evapotranspiration and near surface humidity, decreased air and ground temperature, augmented downwind precipitation, enhanced recharge, depletion of source water stocks, and overall changes to the regional water budget. However, developers of global offline and coupled modeling systems have been slow to incorporate irrigation. This presentation will describe progress towards simulating irrigation worldwide in the Land Information System (LIS) and Global Land Data Assimilation System (GLDAS). We have developed and refined the ability to simulate sprinkler irrigation, furrow irrigation, paddy irrigation, and drip irrigation. We have reconciled multiple global and national irrigation, land cover, and crop type databases in an effort to (1) distinguish irrigated and rainfed agriculture, (2) simulate the proper distribution of crops (as sub-grid tiles) in each model grid pixel, and (3) apply the most likely irrigation method in each tile based on the crop type and country or state level information on the relative usage of sprinkler, flood, and drip irrigation. Using agricultural water withdrawal data, as available, for calibration and evaluation, we show that the implemented approach is promising but that much fine tuning remains to be done. In particular, irrigation model parameters that produce good results in one location often produce less satisfying results in others.

Matthew Rodell↗
DOE OSTI2020

hp -VPINNs: Variational physics-informed neural networks with domain decomposition

We formulate a general framework for hp-variational physics-informed neural networks (hp-VPINNs) based on the nonlinear approximation of shallow and deep neural networks and hp-refinement via domain decomposition and projection onto the space of high-order polynomials. The trial space is the space of neural network, which is defined globally over the entire computational domain, while the test space contains piecewise polynomials. Specifically in this study, the hp-refinement corresponds to a global approximation with a local learning algorithm that can efficiently localize the network parameter optimization. Here, we demonstrate the advantages of hp-VPINNs in both accuracy and training cost for several numerical examples of function approximation and in solving differential equations.

42 ENGINEERING↗
NASA NTRS1992

Cartesian based grid generation/adaptive mesh refinement

Grid adaptation has recently received attention in the computational fluid dynamics (CFD) community as a means to capture the salient features of a flowfield by either moving grid points of a structured or by adding cells in an unstructured manner. An approach based on a background cartesian mesh is investigated from which the geometry is 'cut' out of the mesh. Once the mesh is obtained, a solution on this coarse grid is found, that indicates which cells need to be refined. This process of refining/solving continues until the flow is grid refined in terms of a user specified global parameter (such as drag coefficient etc.). The advantages of this approach are twofold: the generation of the base grid is independent of the topology of the bodies or surfaces around/through which the flow is to be computed, and the resulting grid (in uncut regions) is highly isotropic, so that the truncation error is low. The flow solver (which, along with the grid generation is still under development) uses a completely unstructured data base, and is a finite volume, upwinding scheme. Current and future work will address generating Navier-Stokes suitable grids by using locally aligned and normal face/cell refining. The attached plot shows a simple grid about two turbine blades.

Coirier, William J.↗
NASA NTRS1997

Global Analysis of Empirical Relationships Between Annual Climate and Seasonality of NDVI

This study describes the use of satellite data to calibrate a new climate-vegetation greenness function for global change studies. We examined statistical relationships between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our empirical understanding of intraannual patterns and global abiotic controls on natural vegetation dynamics. Multiple linear regression results using global l(sup o) gridded data sets suggest that three climate indexes: growing degree days, annual precipitation total, and an annual moisture index together can account to 70-80 percent of the variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same climate index values from the previous year explained no significant additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes was closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from l(sup o) grid cells mapped as greater than 25 percent inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes were not accurately predicted are mainly high latitude ecosystems and other remote locations where climate station data are sparse.

Potter, C. S.↗
DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗