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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Quantifying Contributions of Land Carbon Fluxes Variability and Atmospheric Transport Variability to Atmospheric CO2 Variability

Much of the uncertainty in predicting variations in greenhouse gases originates in the complex dynamics of the land ecosystem and the atmosphere. To reduce the uncertainties, it is useful to decouple the contributions of land carbon fluxes and atmospheric transport to atmospheric carbon variability. Here we isolate these contributions using a version of the NASA GEOS model that couples carbon, energy and water cycles between the land and the atmosphere. Our current study is a follow-on to a preliminary analysis that suggested that an extreme event (e.g., imposed drought) in a free running AGCM simulation affects atmospheric CO2 more through its impact on atmospheric transport than through a modification of land carbon fluxes. In order to more carefully isolate the effects of the land carbon variability and atmospheric transport variability on atmospheric CO2 variability, we conducted two coupled-AGCM simulations in replay mode, a mode that forces the model's evolution of weather to match that of the MERRA-2 reanalysis. In the control simulation, the land carbon fluxes and the atmospheric CO2 concentrations, as well as the meteorology, are simulated over 2001-2015. We compute the climatological seasonal cycles of net land carbon production from this control simulation and then prescribe, in a second simulation, these climatological carbon fluxes to the atmosphere in the same replay mode. By comparing the atmospheric CO2 variability produced in the two simulations, we fully isolate the part of this variability associated with land surface fluxes. Relative contributions of land flux variability and atmospheric transport variability to CO2 variability are quantified both on a regional basis and as a function of height into the atmosphere to support interpretation of both ground-based and satellite observations.

Lee, Eunjee↗
DOE OSTI2022

How long can luminous blue variables sleep? A long-term photometric variability and spectral study of the Galactic candidate luminous blue variable MN 112

ABSTRACT Luminous blue variables (LBVs) are massive stars that show strong spectral and photometric variability. The questions of what evolutionary stages they represent and what exactly drives their instability are still open, and thus it is important to understand whether LBVs without significant ongoing activity exist, and for how long such dormant LBVs may ‘sleep’. In this paper we investigate the long-term variability properties of the LBV candidate MN 112, by combining its optical and infrared spectral data covering 12 years with photometric data covering nearly a century, as acquired from both modern time-domain sky surveys and historical photographic plates. We analyse the spectra, derive the physical properties of the star by modelling its atmosphere, and use a new distance estimate from Gaia data release 3 (DR3) to determine the position of MN 112 both inside the Galaxy and in the Hertzsprung–Russell diagram. The distance estimation has almost doubled in comparison with Gaia DR2. Because of this, MN 112 moved to upper part of the diagram, and according to our modelling it lies on an evolutionary track for a star with initial mass $M_*=70~\rm M_\odot$ near the Humphreys–Davidson limit. Given the absence of any significant variability, we conclude that the star is a dormant LBV that has now been inactive for at least a century.

Maryeva, O. V. (ORCID:0000000314424755)↗
DOE OSTI2023

Limited Role of Absolute Humidity in Intraurban Heat Variability

Abstract Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks. Significance Statement Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Cycle-to-cycle variability in spark-assisted compression ignition engines near optimal mean combustion phasing

Stoichiometric spark-assisted compression ignition (SACI) combustion with exhaust gas recirculation (EGR) dilution has demonstrated higher part-load thermal efficiencies compared to spark-ignited engines, while maintaining ultra-low tailpipe emissions. However, SACI is often characterized by high cyclic variability in heat release or torque output, which poses a challenge to its implementation in light-duty vehicles. This paper presents an experimental investigation of cyclic variability in stoichiometric SACI combustion under EGR-dilute conditions, while maintaining mean combustion phasing (θ 50 ) near optimal timing for thermal efficiency. The present work focuses on SACI conditions where the flame-based heat release fraction is between approximately 10% and 40% of the overall heat release, and therefore contributes significantly to the combustion process. For the SACI conditions examined, the variability in θ 50 was driven by variability in autoignition timing, which in turn correlated with the start of measurable heat release (θ 02 ). High variability in θ 50 caused unstable work output due to very late combustion with poor or no end-gas autoignition. The use of a high ignition energy dual-coil offset ignition system had negligible impact in reducing θ 50 variability. Analysis of experimental data from close to 1000 operating conditions showed that the magnitude of θ 50 variability correlates with the flame-based heat release fraction ([Formula: see text]), for a large range of intake pressures, spark timings and exhaust gas recirculation levels. For the SACI conditions examined, combustion phasing variability was largely determined by flame-based combustion and particularly the initial flame formation (θ IGN-02 ), and minimally by the end-gas autoignition heat release. The analysis also demonstrated that θ 50 variability is amplified as the contribution of the flame to the overall heat release increases.

Engineering↗
DOE OSTI2024

Subseasonal Variability of U.S. Coastal Sea Level from MJO and ENSO Teleconnection Interference

Abstract Climate variability affects sea levels as certain climate modes can accelerate or decelerate the rising sea level trend, but subseasonal variability of coastal sea levels is underexplored. This study is the first to investigate how remote tropical forcing from the MJO and ENSO impact subseasonal U.S. coastal sea level variability. Here, composite analyses using tide gauge data from six coastal regions along the U.S. East and West Coasts reveal influences on sea level anomalies from both the MJO and ENSO. Tropical MJO deep convection forces a signal that results in U.S. coastal sea level anomalies that vary based on MJO phase. Further, ENSO is shown to modulate both the MJO sea level response and background state of the teleconnections. The sea level anomalies can be significantly enhanced or weakened by the MJO-associated anomaly along the East Coast due to constructive or destructive interference with the ENSO-associated anomaly, respectively. The West Coast anomaly is found to be dominated by ENSO. We examine physical mechanisms by which MJO and ENSO teleconnections impact coastal sea levels and find consistent relationships between low-level winds and sea level pressure that are spatially varying drivers of the variability. Two case studies reveal how MJO and ENSO teleconnection interference played a role in notable coastal flooding events. Much of the focus on sea level rise concerns the long-term trend associated with anthropogenic warming, but on shorter time scales, we find subseasonal climate variability has the potential to exacerbate the regional coastal flooding impacts. Significance Statement Coastal flooding due to sea level rise is increasingly threatening communities, but natural fluctuations of coastal sea levels can exacerbate the human-caused sea level rise trend. This study assesses the role of tropical influences on coastal subseasonal (2 weeks–3 months) sea level heights. Further, we explore the mechanisms responsible, particularly for constructive interference of signals contributing to coastal flooding events. Subseasonal signals amplify or suppress the lower-frequency signals, resulting in higher or lower sea level heights than those expected from known climate modes (e.g., ENSO). Low-level onshore winds and reduced sea level pressure connected to the tropical phenomena are shown to be indicators of increased U.S. coastal sea levels, and vice versa. Two case studies reveal how MJO and ENSO teleconnection interference played a role in notable coastal flooding events. Much of the focus on sea level rise concerns the long-term trend associated with anthropogenic warming, but on shorter time scales, we find subseasonal climate variability has the potential to exacerbate the regional coastal flooding impacts.

Meteorology & Atmospheric Sciences↗
NASA NTRS2001

Constraints on Variability of Brightness and Surface Magnetism on Time Scales of Decades to Centuries in the Sun and Sun-Like Stars: A Source of Potential Terrestrial Climate Variability

These four points summarize our work to date. (1) Conciliation of solar and stellar photometric variability. Previous research by us and colleagues suggested that the Sun might at present be showing unusually low photometric variability compared to other sun-like stars. Those early results would question the suitability of the technique of using sun-like stars as proxies for solar irradiance change on time scales of decades to centuries. However, our results indicate the contrary: the Sun's observed short-term (seasonal) and longterm (year-to-year) brightness variations closely agree with observed brightness variations in stars of similar mass and age. (2) We have demonstrated an inverse correlation between the global temperature of the terrestrial lower troposphere, inferred from the NASA Microwave Sounding Unit (MSU) radiometers, and the total area of the Sun covered by coronal holes from January 1979 to present (up to May 2000). Variable fluxes of either solar charged particles or cosmic rays, or both, may influence the terrestrial tropospheric temperature. The geographical pattern of the correlation is consistent with our interpretation of an extra-terrestrial charged particle forcing. (3) Possible climate mechanism amplifying the impact of solar ultraviolet irradiance variations. The key points of our proposed climate hypersensitivity mechanism are: (a) The Sun is more variable in the UV (ultraviolet) than in the visible. However, the increased UV irradiance is mainly absorbed in the lower stratosphere/upper troposphere rather than at the surface. (b) Absorption in the stratosphere raises the temperature moderately around the vicinity of the tropopause, and tends to stabilize the atmosphere against vertical convective/diffusive transport, thus decreasing the flux of heat and moisture carried upward from surface. (c) The decrease in the upward convection of heat and moisture tends to raise the surface temperature because a drier upper atmosphere becomes less cloudy, which in turn allows more solar radiation to reach the Earth's surface. (4) Natural variability in an ocean-atmosphere climate model. We use a 14-region, 6-layer, global thermo-hydrodynamic ocean-atmosphere model to study natural climate variability. All the numerical experiments were performed with no change in the prescribed external boundary conditions (except for the seasonal cycle of the Sun's tilt angle). Therefore, the observed inter-annual variability is of an internal kind. The model results are helpful toward the understanding of the role of nonlinearity in climate change. We have demonstrated a range of possible climate behaviors using our newly developed ocean-atmosphere model. These include climate configurations with no interannual variability, with multi-year periodicities, with continuous chaos, or with chaotically occuring transitions between two discrete substrates. These possible modes of climate behavior are all possible for the real climate, as well as the model. We have shown that small temporary climate influences can trigger shifts both in the mean climate, and among these different types of behavior. Such shifts are not only theoretically plausible, as shown here and elsewhere; they are omnipresent in the climate record on time scales from several years to the age of the Earth. This has two apparently opposite implications for the possibility of anthropogenic global warming. First, any warming which might occur as a result of human influence would be only a fraction of the small-to-large unpredictable natural changes and changes which result from other external causes. On the other hand, small temporary influences such as human influence do have the potential of causing large permanent shifts in mean climate and interannual variability.

Baliunas, Sallie L.↗
DOE OSTI2021

Novel Transformer with Variable Leakage and Magnetizing Inductances

This paper introduces the concept of a novel variable inductance transformer (VIT) whose leakage and magnetizing inductances can be varied dynamically and independently to aid the advancement of research in isolated power electronic converters. In this transformer, the leakage inductance can be varied by varying the length of overlap between the primary and secondary windings, while the magnetizing inductance can be varied by varying the air gap between the core legs. Individual controls for the two inductances ensure that one can be varied independent of the other, as needed to satisfy different power conversion objectives. In this paper, the analytical models of the variable leakage and magnetizing inductances are presented. Analytical results are compared with those obtained from Finite Element Method (FEM) and an experimental prototype of a VIT. Analytical solutions of the variable leakage inductance present an error of less than 2.35 % when compared to FEM solutions, while the analytical solutions of the variable magnetizing inductance present a maximum error of 1.82 % when compared to experimental measurements.

image method, leakage inductance, magnetizing indu↗
DOE OSTI2021

Identifying Periodic Variable Stars and Eclipsing Binary Systems with Long-term Las Cumbres Observatory Photometric Monitoring of ZTF J0139+5245

We present the results of our search for variable stars using the long-term Las Cumbres Observatory (LCO) monitoring of white dwarf ZTF J0139+5245 with the two 1.0 m telescope nodes located at McDonald Observatory using the Sinistro imaging instrument. In this search, we find 38 variable sources, of which 27 are newly discovered or newly classified (71%) based on comparisons with previously published catalogs, thereby increasing the number of detections in the field of view under consideration by a factor of ≈2.5. We find that the improved photometric precision per exposure due to longer exposure time for LCO images combined with the greater time sampling of LCO photometry enables us to increase the total number of detections in this field of view. Each LCO image covers a field of view of 26' × 26' and observes a region close to the Galactic plane (b = -9.°4) abundant in stars with an average stellar density of ≈8 arcmin{sup -2}. We perform aperture photometry and Fourier analysis on over 2000 stars across 1560 LCO images spanning 537 days to find 28 candidate BY Draconis variables, three candidate eclipsing binaries of type EA, and seven candidate eclipsing binaries of type EW. In assigning preliminary classifications to our detections, we demonstrate the applicability of the Gaia color–magnitude diagram as a powerful classification tool for variable-star studies.

47 OTHER INSTRUMENTATION↗
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 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↗
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 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 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↗