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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The ASAS-SN catalogue of variable stars – IV. Periodic variables in the APOGEE survey

ABSTRACT We explore the synergy between photometric and spectroscopic surveys by searching for periodic variable stars among the targets observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) using photometry from the All-Sky Automated Survey for Supernovae (ASAS-SN). We identified 1924 periodic variables among more than $258\, 000$ APOGEE targets; 465 are new discoveries. We homogeneously classified 430 eclipsing and ellipsoidal binaries, 139 classical pulsators (Cepheids, RR Lyrae, and δ Scuti), 719 long-period variables (pulsating red giants), and 636 rotational variables. The search was performed using both visual inspection and machine learning techniques. The light curves were also modelled with the damped random walk stochastic process. We find that the median [Fe/H] of variable objects is lower by 0.3 dex than that of the overall APOGEE sample. Eclipsing binaries and ellipsoidal variables are shifted to a lower median [Fe/H] by 0.2 dex. Eclipsing binaries and rotational variables exhibit significantly broader spectral lines than the rest of the sample. We make ASAS-SN light curves for all the APOGEE stars publicly available and provide parameters for the variable objects.

Pawlak, Michał↗
NASA NTRS2007

Convective-Stratiform Precipitation Variability at Seasonal Scale from Eight Years of TRMM Observations: Implications for Multiple Modes of Diurnal Variability

This study investigated the variability of convective and stratiform rainfall from eight years (1998-2005) of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and TRMM Microwave Imager (TMI) measurements--focusing on seasonal diurnal variability. The main scientific goals are: (1) to understand the climatological variability of these two dominant forms of precipitation across the four cardinal seasons and over continents and oceans separately, and (2) to understand how differences in convective and stratiform rainfall variations ultimately determine how diurnal variability of total rainfall is modulated into multiple modes. There are distinct day-night differences for both convective and stratiform rainfall. Oceanic (continental) convective rainfall is up to 25% (50%) greater during nighttime (daytime) than daytime (nighttime). Seasonal variability of convective rainfall's day-night difference is relatively small, while stratiform rainfall exhibits very apparent day-night variations with a seasonal variability of these variations. There are consistent late evening diurnal peaks without obvious seasonal variations over ocean for convective, stratiform, and total rainfall. Over continents, convective and total rainfall exhibit a consistent dominant afternoon peak with little seasonal variability--with a late evening secondary peal exhibiting seasonal variation. Stratiform rainfall over continents shows a consistent strong late evening peak with a weak afternoon peak--with the afternoon mode undergoing seasonal variability. Therefore, the diurnal characteristics of stratiform rainfall control the afternoon secondary maximum of oceanic rainfall and the late evening secondary peak of continental rainfall. Even at seasonal-regional scale spatially or an interannual global scale temporally, the secondary mode can become very pronounced, but on an intermittent basis. Overall, the results demonstrate the importance of partitioning total rainfall into convective and stratiform components and that diurnal modes largely arise from distinct diurnal stratiform variations modulating convective variations.

Yang, Song↗
NASA NTRSDate not supplied

Linking OH Variability to Observable Variables, Meteorology and Transport

The hydroxyl radical (OH) plays a vital role in tropospheric chemistry, as it provides the dominant sink for a multitude of pollutants and climate-relevant gases such as methane. Observational constraints on the global distribution and temporal variability of OH are limited, and models simulate a wide range of OH distributions. While OH itself has a short atmospheric lifetime, OH is photochemically coupled to longer-lived species that undergo atmospheric transport. Here, we investigate how much of the OH variability within and between models can be explained by differences in observable species to develop diagnostics for OH differences. We find that NO 2 and water vapor together explain much of the spatial and temporal variability in simulated OH, and we use satellite observations to identify biases in these variables. The OH response to ENSO also differs between models, and we investigate potential causes of these differences such as differences in convection or lightning NOx. We also explore the potential of idealized tracers to represent the OH distribution. Within a single model, meteorological variables such as humidity and idealized tracers of transport can explain a significant portion of the OH spatial variability. We use a Gradient Boosted Regression Trees, a type of machine learning, to account for non-linear relationships between OH and the input variables.

Meteorology↗
DOE OSTI2022

Freshwater Flux Variability Lengthens the Period of the Low-Frequency AMOC Variability

Atlantic Meridional Overturning Circulation (AMOC) exhibits interdecadal to multidecadal variability, yet the extent to which the surface freshwater flux (FWF) variability affects the AMOC variability remains unclear. This study isolates the contribution of FWF variability in modulating AMOC through a partially coupled experiment, in which the FWF effect from the atmosphere and sea ice are disabled. It is demonstrated that the FWF effect can remarkably enhance the persistence of positive salinity anomalies in the Labrador Sea, and sustain the dense water formation that was initiated by the cold temperature anomalies. Therefore, the FWF variability serves as a positive feedback on AMOC and lengthens the period of the AMOC oscillation. Further lead-lag regressions of atmospheric and oceanic variables onto the AMOC time series illuminate that the persistent salinity anomalies are generated through two pathways: i) the phase lag between temperature and salinity anomalies in the Labrador Sea; and ii) a downstream propagation of extra high-salinity anomaly along the East Greenland Current, due to the reduced sea ice melting flux associated with an atmosphere forcing over the southern Greenland tip.

58 GEOSCIENCES↗
DOE OSTI2018

Quantifying the agreement between observed and simulated extratropical modes of interannual variability

Using historical simulations of the Coupled Model Intercomparison Project-5 (CMIP5) and multiple observationally-based datasets, we employ skill metrics to analyze the fidelity of the simulated Northern Annular Mode, the North Atlantic Oscillation, the Pacific North America pattern, the Southern Annular Mode, the Pacific Decadal Oscillation, the North Pacific Oscillation, and the North Pacific Gyre Oscillation. We assess the benefits of a unified approach to evaluate these modes of variability, which we call the common basis function (CBF) approach, based on projecting model anomalies onto observed empirical orthogonal functions (EOFs). The CBF approach circumvents issues with conventional EOF analysis, eliminating, for example, corrections of arbitrarily assigned, but inconsistent, signs of the EOF’s/PC’s being compared. It also avoids the problem that sometimes the first observed EOF is more similar to a higher order model EOF, particularly if the simulated EOFs are not well separated. Compared to conventional EOF analysis of models, the CBF approach indicates that models compare significantly better with observations in terms of pattern correlation and root-mean-squared-error (RMSE) than heretofore suggested. In many cases, models are doing a credible job at capturing the observationally-based estimates of patterns; however, errors in simulated amplitudes can be large and more egregious than pattern errors. In the context of the broad distribution of errors in the CMIP5 ensemble, sensitivity tests demonstrate that our results are relatively insensitive to methodological considerations (CBF vs. conventional approach), observational uncertainties in pattern (as determined by using multiple datasets), and internal variability (when multiple realizations from the same model are compared). The skill metrics proposed in this study can provide a useful summary of the ability of models to reproduce the observed EOF patterns and amplitudes. Additionally, the skill metrics can be used as a tool to objectively highlight where potential model improvements might be made. Finally, we advocate more systematic and objective testing of simulated extratropical variability, especially during the non-dominant seasons of each mode, when many models are performing relatively poorly.

58 GEOSCIENCES↗
DOE OSTI2018

Tundra landscape heterogeneity, not interannual variability, controls the decadal regional carbon balance in the Western Russian Arctic

Across the Arctic, the net ecosystem carbon (C) balance of tundra ecosystems is highly uncertain due to substantial temporal variability of C fluxes and to landscape heterogeneity. We modeled both carbon dioxide (CO2) and methane (CH4) fluxes for the dominant regional land cover types in a sub-Arctic tundra region in northeast European Russia for the period of 2006-2015 using coupled biogeochemical and permafrost models. Modeled net annual CO2 fluxes ranged from -300 g C m-2 y-1 [net uptake] in a willow fen to 3 g C m-2 y-1 [net source] in dry lichen tundra. Modeled annual CH4 emissions ranged from -0.2 to 22.3 g C m-2 y-1 at a peat plateau site and a willow fen site, respectively. Interannual variability over the decade was relatively small (20-30%) in comparison to variability among the land cover types (120-225%). Using high-resolution land cover classification, the region was a net sink of atmospheric CO2 across most land cover types but a net source of CH4 to the atmosphere due to high emissions from permafrost-free fens. Using a lower-resolution for land cover classification resulted in a 20-65% underestimation of regional CH4 flux and smaller (10%) overestimation of regional CO2 uptake, leading to an overall overestimation of the regional GHG sink by 90% due to the underestimation of wetland extent by 60%. Accurately capturing the relative fraction of uplands versus wetlands was key to determining the net regional C balance but required high (<25-30 m) spatial resolution.

Treat, Claire↗
DOE OSTI2021

Seasonal changes in the drivers of water physico-chemistry variability of a small freshwater tidal river

Where rivers meet the sea, tides can exert a physical and chemical influence on the lower reaches of a river. How tidal dynamics in these tidal river reaches interact with upstream hydrological drivers such as storm rainfall, which ultimately determines the quantity and composition of material transferred from watersheds to estuaries, is currently unknown. We monitored a small freshwater tidal river in the Pacific Northwest, USA in high resolution over one year to evaluate the relative importance of tides versus upstream hydrological flows (i.e., base flow and precipitation events) on basic physico-chemical parameters (pH, dissolved oxygen, turbidity, specific conductivity, and temperature), and how these interactions relate to the downstream estuary. Tidal variability and diurnal cycles (i.e. solar radiation) dominated water physico-chemical variability in the summer, but the influence of these drivers was overshadowed by storm-driven sharp pulses in river physico-chemistry during the remainder of the year. Within such events, we found incidences of counterclockwise hysteresis of pH, counterclockwise hysteresis of dissolved oxygen, and clockwise hysteresis of turbidity, although systematic trends were not observed across events. The dominance of storm rainfall in the river’s physico-chemistry dynamics, and similar pulses of decreased pH observed in adjacent estuarine waters, suggest that the linkage between tidal streams and the broader system is variable throughout the year. High-frequency monitoring of tidal river biogeochemistry is therefore crucial to enable the assessment of how the relative strength of these drivers may change with future sea level rise and altered precipitation patterns to modulate biogeochemical dynamics across the land-ocean-atmosphere continuum.

aquatic↗
NASA NTRS2019

Discerning the Impact of Powder Feedstock Variability on Structure, Property, and Performance of Selective Laser Melted Alloy 718: A Principal Component Analysis (PCA) of Feedstock Variability

Extensive mechanical, chemical and microstructural analyses were conducted on additively manufactured Alloy 718 to characterize powders from multiple vendors to determine the effects of variations observed in the powders had on the consolidated material. With over 190 variables examined, it was necessary to reduce the number of variables and identify the variables and classes of variables that had the greatest effect. Principle Component Analysis (PCA) was used to reduce the number of variable to effectively 12 while identifying several classes of variables as most important.

Ellis, David↗
DOE OSTI2018

Large-eddy Simulations of Spray Variability Effects on Flow Variability in a Direct-injection Spark-ignition Engine Under Non-combusting Operating Conditions

Large-eddy Simulations (LES) have been carried out to investigate spray variability and its effect on cycle-to-cycle flow variability in a direct-injection spark-ignition (DISI) engine under non-reacting conditions. Initial simulations were performed of an injector in a constant volume spray chamber to validate the simulation spray set-up. Comparisons showed good agreement in global spray measures such as the penetration. Local mixing data and shot-to-shot variability were also compared using Rayleigh-scattering images and probability contours. The simulations were found to reasonably match the local mixing data and shot-to-shot variability using a random-seed perturbation methodology. After validation, the same spray set-up with only minor changes was used to simulate the same injector in an optically accessible DISI engine. Particle Image Velocimetry (PIV) measurements were used to quantify the flow velocity in a horizontal plane intersecting the spark plug gap. The engine was operated in a skip-fired operating mode and comparisons focused on cycles that included fuel injection, but no spark event and therefore no combustion. 105 total LES engine cycles were simulated using a parallel cycle simulation approach and 3 different perturbation methods in an attempt to isolate the effects of shot-to-shot spray variability and the initial turbulent flow field as well as their interaction effects on overall engine CCVs. The experimental mean and standard deviations were reasonably well matched by the simulations, though quantitative comparisons near the injection event during the intake stroke were difficult due to the high uncertainty in the PIV measurements at these crank angles. The 3 simulation perturbation methods resulted in very similar results, though further analysis found the current parallel cycle approach may be limiting the ability of the simulations to isolate the spray and flow effects.

Cycle-to-cycle Variability↗
NASA NTRSDate not supplied

Interannual to Decadal Variability in Subarctic Atlantic Ocean Primary Productivity Historical Variability, Dynamics, and Response to Warming

Phytoplankton primary productivity in the Subarctic Atlantic Ocean sustains valuable fisheries and exports anthropogenic atmospheric carbon dioxide, but multiple Earth system models show that this productivity declines substantially in 21st century global warming scenarios. Prior studies have investigated the spatial and temporal dynamics of the seasonal cycle of the phytoplankton, which exhibit a prominent but spatially variable spring bloom that is caused by the closely related seasonal cycles in solar radiation and vertical mixing that modulate both phytoplankton growth and loss rates. However, recent work indicates that multi-decade declines in this spring bloom with global warming in multiple Earth system models are driven by reductions in circulation of nutrients from the subtropical and tropical thermocline, rather than local changes in vertical mixing or shortwave radiation in the Subarctic. It is unclear if current models of how phytoplankton productivity varies seasonally or over a century in global warming can be interpolated to explain the interannual to decadal variability. In this poster, I will quantify the interannual to decadal variability of phytoplankton chlorophyll using historical satellite ocean color observations and model simulations with the Community Earth System Model. I will then use the observations and model simulations to elucidate the mechanisms of interannual to decadal variability and how that variability is restructured in 21st century global warming scenarios.

nterannual↗
DOE OSTI2025

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

The Variable and Non-Variable X-Ray Absorbers in Compton-Thin Type II Active Galactic Nuclei

We have conducted an extensive X-ray spectral variability study of a sample of 20 Compton-thin type II galaxies using broadband spectra from XMM-Newton, Chandra, and Suzaku. The aim is to study the variability of the neutral intrinsic X-ray obscuration along the line of sight and investigate the properties and location of the dominant component of the X-ray-obscuring gas. The observations are sensitive to absorption columns of N(sub H) ∼ 10(exp 20.5–24) cm(exp -2) of fully and partially covering neutral and/or lowly ionized gas on timescales spanning days to well over a decade. We detected variability in the column density of the full-covering absorber in 7/20 sources, on timescales of months to years, indicating a component of compact-scale X-ray-obscuring gas lying along the line of sight of each of these objects. Our results imply that torus models incorporating clouds or over-dense regions should account for line-of-sight column densities as low as ∼a few ×10(exp 21) cm(exp -2). However, 13/20 sources yielded no detection of significant variability in the full-covering obscurer, with upper limits of ΔN(sub H) spanning 10(exp 21–23) cm(exp -2). The dominant absorbing media in these systems could be distant, such as kiloparsec-scale dusty structures associated with the host galaxy, or a homogeneous medium along the line of sight. Thus, we find that overall, strong variability in full-covering obscurers is not highly prevalent in Compton-thin type IIs, at least for our sample, in contrast to previous results in the literature. Finally, 11/20 sources required a partial-covering, obscuring component in all or some of their observations, consistent with clumpy near-Compton-thick compact-scale gas.

Active galaxies↗
DOE OSTI2018

Wind energy variability and links to regional and synoptic scale weather

The accurate characterization of seasonal and inter-annual site-level wind energy variability is essential during wind project development. Understanding the features and probability of low-wind years is of particular interest to developers and financers. However, a dearth of long-term, hub-height wind observations makes these characterizations challenging, and thus techniques to improve these characterizations are of great value. To improve resource characterization, we explicitly link wind resource variability (at hub-height, and at specific sites) to regional and synoptic scale wind regimes. Our approach involves statistical clustering of high-resolution modeled wind data, and is applied to California for a period covering 1980–2015. With this approach, we investigate the unique meteorological patterns driving low and high wind years at five separate wind project sites. We also find wind regime changes over the 36-year period consistent with global warming: wind regimes associated with anomalously hot summer days increased at half a day per year and stagnant conditions increased at one-third days per year. Despite these changes, the average annual resource potential remained constant at all project sites. Additionally, we identify correlations between climate modes and wind regime frequency, a linkage valuable for resource characterization and forecasting. Our general approach can be applied in any location and may benefit many aspects of wind energy resource evaluation and forecasting.

17 WIND ENERGY↗
DOE OSTI2022

An independent analysis of bias sources and variability in wind plant pre-construction energy yield estimation methods

The wind resource assessment community has long had the goal of reducing the bias between wind plant pre-construction energy yield assessment (EYA) and the observed annual energy production (AEP). This comparison is typically made between the 50% probability of exceedance (P50) value of the EYA and the long-term corrected operational AEP (hereafter OA P50), and is known as the P50 bias. The industry has critically lacked an independent analysis of bias reduction investigated across multiple consultants to identify the greatest sources of uncertainty and variance in the EYA process and the best opportunities for uncertainty reduction. The present study addresses this gap by benchmarking consultant methodologies against each other and against operational data at a scale not seen before in industry collaborations. We consider data from 10 wind plants and evaluate discrepancies between eight consultancies in the steps taken from estimates of gross to net energy. Consultants tend to overestimate the gross energy produced at the turbines and then compensate by further overestimating downstream losses, leading to a mean P50 bias near zero, still with significant variability among the individual wind plants. Within our data sample, we find that consultant estimates of all loss categories, except environmental losses, tend to reduce the project-to-project variability of the P50 bias. The disagreement between consultants, however, remains flat throughout the addition of losses. Finally, we find that differences in consultants’ estimates of project performance can lead to differences up to $10/MWh in the levelized cost of energy for a wind plant.

Todd, Austin C.↗
DOE OSTI2017

Stochastic modeling of phosphorus transport in the Three Gorges Reservoir by incorporating variability associated with the phosphorus partition coefficient

Phosphorus (P) fate and transport plays a crucial role in the ecology of rivers and reservoirs in which eutrophication is limited by P. A key uncertainty in models used to help manage P in such systems is the partitioning of P to suspended and bed sediments. By analyzing data from field and laboratory experiments, we stochastically characterize the variability of the partition coefficient (Kd) and derive spatio-temporal solutions for P transport in the Three Gorges Reservoir (TGR). We formulate a set of stochastic partial different equations (SPDEs) to simulate P transport by randomly sampling Kd from the measured distributions, to obtain statistical descriptions of the P concentration and retention in the TGR. The correspondence between predicted and observed P concentrations and P retention in the TGR combined with the ability to effectively characterize uncertainty suggests that a model that incorporates the observed variability can better describe P dynamics and more effectively serve as a tool for P management in the system. This study highlights the importance of considering parametric uncertainty in estimating uncertainty/variability associated with simulated P transport.

Partition coefficient↗
DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗