VLF Measurements and Modeling of the D-Region Response to the 2017 Total Solar Eclipse
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Wei Xu↗
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Results for “regional models”
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.
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Managing Increased Electric Vehicle Shares on Decarbonized Bulk Power Systems
Transportation electrification and power sector decarbonization - the convergence of these two trends present a complex planning problem requiring realistic, region-specific modeling and analysis to ensure that both can happen rapidly and cost effectively. This project, funded through the U.S. Department of Energy's Vehicle Technologies Office, models the evolution of the U.S. bulk power system (through 2050) in response to large-scale EV charging across all on-road vehicle segments. We will assess the opportunity and value of demand-side flexibility (i.e., "smart" charging) for reducing energy costs, increasing renewable generation shares, and managing future EV loads on the bulk power system. Overall, this study aims to provide an improved understanding of least-cost solutions for managing EV load growth on the grid and will make high-resolution EV load data sets publicly available for further analysis.
ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding
Decoding visual experiences from fMRI offers a powerful avenue to understand human perception and develop advanced brain-computer interfaces. However, current progress often prioritizes maximizing reconstruction fidelity while overlooking interpretability, an essential aspect for deriving neuroscientific insight. To address this gap, we propose MoRE-Brain, a neuro-inspired framework designed for high-fidelity, adaptable, and interpretable visual reconstruction. MoRE-Brain uniquely employs a hierarchical Mixture-of-Experts architecture where distinct experts process fMRI signals from functionally related voxel groups, mimicking specialized brain networks. The experts are first trained to encode fMRI into the frozen CLIP space. A finetuned diffusion model then synthesizes images, guided by expert outputs through a novel dual-stage routing mechanism that dynamically weighs expert contributions across the diffusion process. MoRE-Brain offers three main advancements: First, it introduces a novel Mixture-of-Experts architecture grounded in brain network principles for neuro-decoding. Second, it achieves efficient cross-subject generalization by sharing core expert networks while adapting only subject-specific routers. Third, it provides enhanced mechanistic insight, as the explicit routing reveals precisely how different modeled brain regions shape the semantic and spatial attributes of the reconstructed image. Extensive experiments validate MoRE-Brain’s high reconstruction fidelity, with bottleneck analyses further demonstrating its effective utilization of fMRI signals, distinguishing genuine neural decoding from over-reliance on generative priors. Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.
Wei, Yuxiang [Georgia Institute of Technology]↗
Modeling and Analysis of Global and Regional Climate Change in Relation to Atmospheric Hydrologic Processes
This research was directed to the development and application of global isentropic modeling and analysis capabilities to describe hydrologic processes and energy exchange in the climate system, and discern regional climate change. An additional objective was to investigate the accuracy and theoretical limits of global climate predictability which are imposed by the inherent limitations of simulating trace constituent transport and the hydrologic processes of condensation, precipitation and cloud life cycles.
Johnson, Donald R.↗
Multi-Decadal Aerosol Variations from 1980 to 2009: A Perspective from Observations and a Global Model
Aerosol variations and trends over different land and ocean regions during 1980-2009 are analyzed with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and observations from multiple satellite sensors and ground-based networks. Excluding time periods with large volcanic influences, the tendency of aerosol optical depth (AOD) and surface concentration over polluted land regions is consistent with the anthropogenic emission changes.The largest reduction occurs over Europe, and regions in North America and Russia also exhibit reductions. On the other hand, East Asia and South Asia show AOD increases, although relatively large amount of natural aerosols in Asia makes the total changes less directly connected to the pollutant emission trends. Over major dust source regions, model analysis indicates that the dust emissions over the Sahara and Sahel respond mainly to the near-surface wind speed, but over Central Asia they are largely influenced by ground wetness. The decreasing dust trend in the tropical North Atlantic is most closely associated with the decrease of Sahel dust emission and increase of precipitation over the tropical North Atlantic, likely driven by the sea surface temperature increase. Despite significant regional trends, the model-calculated global annual average AOD shows little changes over land and ocean in the past three decades, because opposite trends in different regions cancel each other in the global average. This highlights the need for regional-scale aerosol assessment, as the global average value conceals regional changes, and thus is not sufficient for assessing changes in aerosol loading.
atmopheric aerosols↗
A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations
The Arctic sea ice response to a warming climate is assessed in a subset of models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice-edge error, Brier score, and Spatial Probability Score. We find that the CMIP6 multi-model mean captures the mean annual cycle and 1979-2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic sub-regions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models
Abstract In 2022, wind generation accounted for ~10% of total electricity generation in the United States. As wind energy accounts for a greater portion of total energy, understanding geographic and temporal variation in wind generation is key to many planning, operational, and research questions. However, in-situ observations of wind speed are expensive to make and rarely shared publicly. Meteorological models are commonly used to estimate wind speeds, but vary in quality and are often challenging to access and interpret. The Plant-Level US multi-model WIND and generation (PLUSWIND) data repository helps to address these challenges. PLUSWIND provides wind speeds and estimated generation on an hourly basis at almost all wind plants across the contiguous United States from 2018–2021. The repository contains wind speeds and generation based on three different meteorological models: ERA5, MERRA2, and HRRR. Data are publicly accessible in simple csv files. Modeled generation is compared to regional and plant records, which highlights model biases and errors and how they differ by model, across regions, and across time frames.
17 WIND ENERGY↗
On empirical models of the upper atmosphere in the polar regions
Modified expression for exospheric temperature in Jacchia static diffusion models of upper atmosphere in polar regions
Blum, P. W.↗
Energy balance in the solar transition region. III - Helium emission in hydrostatic, constant-abundance models with diffusion
In our previous papers we described the mathematical formalism and the computed results for energy-balance hydrostatic models of the solar transition region. In this paper we discuss in some detail the limitations of the hydrostatic and one-dimensional assumptions used. Then we analyze the determination of helium emission when diffusion is included. We use transport coefficients estimated from kinetic theory to determine the helium departures from local ionization balance. We calculate the helium spectra for each of our models and evaluate the role of helium in the energy transport. Also, we investigate the effects of coronal illumination on the structure of the transition region and upper chromosphere, and show how coronal illumination affects various EUV lines and the He I 10830 A line. Comparing with both absolute intensities and detailed line profiles, we show that our models are consistent not only with the observed hydrogen spectra but also with the available helium spectra.
Fontenla, J. M.↗
Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic
Biomass burning smoke is advected over the southeastern Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly in both magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2017) and September (2016). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.
54 ENVIRONMENTAL SCIENCES↗
Convection from Hemispherical and Conical Model Ice Roughness Elements in Stagnation Region Flows
To improve ice accretion prediction codes, more data regarding ice roughness and its effects on convective heat transfer are required. The Vertical Icing Studies Tunnel (VIST) at NASA Glenn Research was used to model realistic ice roughness in the stagnation region of a NACA 0012 airfoil. In the VIST, a test plate representing the leading 2% chord of the airfoil was subjected to flows of 7.62 m/s (25 ft/s), 12.19 m/s (40 ft/s), and 16.76 m/s (55 ft/s). The test plate was fitted with multiple surfaces or sets of roughness panels, each with a different representation of ice roughness. The sets of roughness panels were constructed using two element distribution patterns that were created based on a laser scan of an iced airfoil acquired in the Icing Research Tunnel at NASA Glenn. For both roughness patterns, surfaces were constructed using plastic hemispherical elements, plastic conical elements, and aluminum conical elements. Infrared surface thermometry data from tests run in the VIST were used to calculate area averaged heat transfer coefficient values. The values from the roughness surfaces were compared to the smooth control surface, showing convective enhancement as high as 400% in some cases. The data gathered during this study will ultimately be used to improve the physical modeling in LEWICE or other ice accretion codes and produce predictions of in-flight ice accretion on aircraft surfaces with greater confidence.
Icing Physics↗
Structure of the chromosphere-corona transition region.
The structure and energy balance of the chromosphere-corona transition region is investigated by means of a static, planar model which is compared with the results of XUV-resonance-line observations. In this model, the transition region is heated by thermal conduction from the corona and cooled by radiative losses. Comparison of the model with observational results implies that this is the dominant process in the energy balance of the transition region, and that the base of the transition region is inherently nonstatic and/or nonplanar. The model explains the observational finding of Noyes et al. (1970) that the number density and the downward heat flux both increase by the same factor from quiet regions to active regions.
Moore, R. L.↗
Molecular hydrogen in H II region transition zones
Model calculations are performed which demonstrate that H II - H I transition zones with densities between 10 and 100 per cu cm may contain molecular-hydrogen column densities of the order of 10 to the 13th power to 10 to the 15th power per sq cm. Observational data for several stars are presented which confirm the theoretical values. It is concluded that all detectable molecular hydrogen is not necessarily located in cold regions of space.
Hill, J. K.↗
A strategy to study regional hydrology and terrestrial ecosystem processes using satellite remote sensing, ground-based data and computer modeling
A strategy is presented for integrating scanning multichannel microwave radiometer data from the Nimbus-7 satellite with meteorological station records and computer simulations of land surface hydrology, terrestrial nutrient cycling, and trace gas emission. Analysis of the observations together with radiative transfer analysis shows that in the tropics the temporal and spatial variations of the polarization difference are determined primarily by the structure and phenology of vegetation and seasonal inundations of major rivers and wetlands. It is concluded that the proposed surface hydrology model, along with climatological records, and, potentially, 37-GHz data for phenology, will provide inputs to a terrestrial ecosystem model that predicts regional net primary production and CO2 gas exchange.
Vorosmarty, C.↗
GCM simulations of intraseasonal variability in the Pacific/North American region
General circulation model (GCM) simulations of low-frequency variability with time scales of 20 to 70 days are analyzed for the Pacific sector during boreal winter. The GCM's leading mode in the upper-tropospheric zonal wind is associated with fluctuations of the East Asian jet; this mode resembles, in both structure and amplitude, the Pacific/North American (PNA) pattern found in the observations on these time scales. In both the model and observations, the PNA anomaly is characterized by: (1) a linear balance in the upper-tropospheric vorticity budget with no significant Rossby wave source in the tropics, (2) a barotropic conversion of kinetic energy from the time mean Pacific jet, and (3) a north/south displacement of the Pacific storm track. In the GCM, the latter is associated with synoptic eddy heat flux and latent heat anomalies that appear to contribute to a strong lower-tropospheric source of wave activity over the North Pacific. This is in contrast to the observations, which show only a weak source of wave activity in this region.
Schubert, Siegfried↗
The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina
Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.
Nazmus Shams Sazib↗
Models of Sector Flows Under Local, Regional and Airport Weather Constraints
Recently, the ATM community has made important progress in collaborative trajectory management through the introduction of a new FAA traffic management initiative called a Collaborative Trajectory Options Program (CTOP). FAA can use CTOPs to manage air traffic under multiple constraints (manifested as flow constrained areas or FCAs) in the system, and it allows flight operators to indicate their preferences for routing and delay options. CTOPs also permits better management of the overall trajectory of flights by considering both routing and departure delay options simultaneously. However, adoption of CTOPs in airspace has been hampered by many factors that include challenges in how to identify constrained areas and how to set rates for the FCAs. Decision support tools providing assistance would be particularly helpful in effective use of CTOPs. Such DSTs tools would need models of demand and capacity in the presence of multiple constraints. This study examines different approaches to using historical data to create and validate models of maximum flows in sectors and other airspace regions in the presence of multiple constraints. A challenge in creating an empirical model of flows under multiple constraints is a lack of sufficient historical data that captures diverse situations involving combinations of multiple constraints especially those with severe weather. The approach taken here to deal with this is two-fold. First, we create a generalized sector model encompassing multiple sectors rather than individual sectors in order to increase the amount of data used for creating the model by an order of magnitude. Secondly, we decompose the problem so that the amount of data needed is reduced. This involves creating a baseline demand model plus a separate weather constrained flow reduction model and then composing these into a single integrated model. A nominal demand model is a flow model (gdem) in the presence of clear local weather. This defines the flow as a function of weather constraints in neighboring regions, airport constraints and weather in locations that can cause re-routes to the location of interest. A weather constrained flow reduction model (fwx-red) is a model of reduction in baseline counts as a function of local weather. Because the number of independent variables associated with each of the two decomposed models is smaller than that with a single model, need for amount of data is reduced. Finally, a composite model that combines these two can be represented as fwx-red (gdem(e), l) where e represents non-local constraints and l represents local weather. The approaches studied to developing these models are divided into three categories: (1) Point estimation models (2) Empirical models (3) Theoretical models. Errors in predictions of these different types of models have been estimated. In situations when there is abundant data, point estimation models tend to be very accurate. In contrast, empirical models do better than theoretical models when there is some data available. The biggest benefit of theoretical models is their general applicability in wider range situations once the degree of accuracy of these has been established.
Kulkarni, Deepak↗
Development of a defect stream function, law of the wall/wake method for compressible turbulent boundary layers
The method presented is designed to improve the accuracy and computational efficiency of existing numerical methods for the solution of flows with compressible turbulent boundary layers. A compressible defect stream function formulation of the governing equations assuming an arbitrary turbulence model is derived. This formulation is advantageous because it has a constrained zero-order approximation with respect to the wall shear stress and the tangential momentum equation has a first integral. Previous problems with this type of formulation near the wall are eliminated by using empirically based analytic expressions to define the flow near the wall. The van Driest law of the wall for velocity and the modified Crocco temperature-velocity relationship are used. The associated compressible law of the wake is determined and it extends the valid range of the analytical expressions beyond the logarithmic region of the boundary layer. The need for an inner-region eddy viscosity model is completely avoided. The near-wall analytic expressions are patched to numerically computed outer region solutions at a point determined during the computation. A new boundary condition on the normal derivative of the tangential velocity at the surface is presented; this condition replaces the no-slip condition and enables numerical integration to the surface with a relatively coarse grid using only an outer region turbulence model. The method was evaluated for incompressible and compressible equilibrium flows and was implemented into an existing Navier-Stokes code using the assumption of local equilibrium flow with respect to the patching. The method has proven to be accurate and efficient.
Wahls, Richard A.↗