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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Radio and millimeter monitoring of Sgr A{sup ⋆}: Spectrum, variability, and constraints on the G2 encounter

We report new observations with the Very Large Array, Atacama Large Millimeter Array, and Submillimeter Array at frequencies from 1.0 to 355 GHz of the Galactic Center black hole, Sagittarius A*. These observations were conducted between 2012 October and 2014 November. While we see variability over the whole spectrum with an amplitude as large as a factor of 2 at millimeter wavelengths, we find no evidence for a change in the mean flux density or spectrum of Sgr A* that can be attributed to interaction with the G2 source. The absence of a bow shock at low frequencies is consistent with a cross-sectional area for G2 that is less than 2×10{sup 29} cm{sup 2}. This result fits with several model predictions including a magnetically arrested cloud, a pressure-confined stellar wind, and a stellar photosphere of a binary merger. There is no evidence for enhanced accretion onto the black hole driving greater jet and/or accretion flow emission. Finally, we measure the millimeter wavelength spectral index of Sgr A* to be flat; combined with previous measurements, this suggests that there is no spectral break between 230 and 690 GHz. The emission region is thus likely in a transition between optically thick and thin at these frequencies and requires a mix of lepton distributions with varying temperatures consistent with stratification.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Stereoscopic study of the kinematic evolution of a coronal mass ejection and its driven shock from the sun to the earth and the prediction of their arrival times

We present a detailed study of the complete evolution of a coronal mass ejection (CME). We have tracked the evolution of both the ejecta and its shock, and further fit the evolution of the fronts to a simple but physics-based analytical model. This study focuses on the CME initiated on the Sun on 2012 July 12 and arriving at the Earth on 2012 July 14. Shock and ejecta fronts were observed by white light images, as well as in situ by the Advanced Composition Explorer satellite. We find that the propagation of the two fronts is not completely dependent upon one another, but can each be modeled in the heliosphere with a drag model that assumes the dominant force of affecting CME evolution to be the aerodynamic drag force of the ambient solar wind. Results indicate that the CME ejecta front undergoes a more rapid deceleration than the shock front within 50 R {sub ☉} and therefore the propagation of the two fronts is not completely coupled in the heliosphere. Using the graduated cylindrical shell model, as well as data from time-elongation stack plots and in situ signatures, we show that the drag model can accurately describe the behavior of each front, but is more effective with the ejecta. We also show that without the in situ data, based on measurements out to 80 R {sub ☉} combined with the general values for drag model parameters, the arrival of both the shock and ejecta can be predicted within four hours of arrival.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2011

Use of Dual-Polarization Radar Variables to Assess Low-Level Wind Shear in Severe Thunderstorm Near-storm Environments in the Tennessee Valley

The upgrade of the National Weather Service (NWS) network of S ]band dual-polarization radars is currently underway, and the incorporation of polarimetric information into the real ]time forecasting process will enhance the forecaster fs ability to assess thunderstorms and their near ]storm environments. Recent research has suggested that the combination of polarimetric variables differential reflectivity (ZDR) and specific differential phase (KDP) can be useful in the assessment of low level wind shear within a thunderstorm. In an environment with strong low ]level veering of the wind, ZDR values will be largest along the right inflow edge of the thunderstorm near a large gradient in horizontal reflectivity (indicative of large raindrops falling with a relative lack of smaller drops), and take the shape of an arc. Meanwhile, KDP values, which are proportional to liquid water content and indicative of a large number of smaller drops, are maximized deeper into the forward flank precipitation shield than the ZDR arc as the smaller drops are being advected further from the updraft core by the low level winds than the larger raindrops. Using findings from previous work, three severe weather events that occurred in North Alabama were examined in order to assess the utility of these signatures in determining the potential for tornadic activity. The first case is from October 26, 2010, where a large number of storms indicated tornadic potential from a standard reflectivity and velocity analysis but very few storms actually produced tornadoes. The second event is from February 28, 2011, where tornadic storms were present early on in the event, but as the day progressed, the tornado threat transitioned to a high wind threat. The third case is from April 27, 2011, where multiple rounds of tornadic storms ransacked the Tennessee Valley. This event provides a dataset including multiple modes of tornadic development, including QLCS and supercell structures. The overarching goal of examining these three events is to compare dual ]polarization features from this larger dataset to previous work and to determine if these signatures can be a useful indication of the potential for tornadic activity associated with the amount of low ]level wind shear in the near ]storm environment.

Crowe, Christina C.↗
DOE OSTI2011

Wind Regimes in Complex Terrain of the Great Valley of Eastern Tennessee

This research was designed to provide an understanding of physical wind mechanisms within the complex terrain of the Great Valley of Eastern Tennessee to assess the impacts of regional air flow with regard to synoptic and mesoscale weather changes, wind direction shifts, and air quality. Meteorological data from 2008 2009 were analyzed from 13 meteorological sites along with associated upper level data. Up to 15 ancillary sites were used for reference. Two-step complete linkage and K-means cluster analyses, synoptic weather studies, and ambient meteorological comparisons were performed to generate hourly wind classifications. These wind regimes revealed seasonal variations of underlying physical wind mechanisms (forced channeled, vertically coupled, pressure-driven, and thermally-driven winds). Synoptic and ambient meteorological analysis (mixing depth, pressure gradient, pressure gradient ratio, atmospheric and surface stability) suggested up to 93% accuracy for the clustered results. Probabilistic prediction schemes of wind flow and wind class change were developed through characterization of flow change data and wind class succession. Data analysis revealed that wind flow in the Great Valley was dominated by forced channeled winds (45 67%) and vertically coupled flow (22 38%). Down-valley pressure-driven and thermally-driven winds also played significant roles (0 17% and 2 20%, respectively), usually accompanied by convergent wind patterns (15 20%) and large wind direction shifts, especially in the Central/Upper Great Valley. The behavior of most wind regimes was associated with detectable pressure differences between the Lower and Upper Great Valley. Mixing depth and synoptic pressure gradients were significant contributors to wind pattern behavior. Up to 15 wind classes and 10 sub-classes were identified in the Central Great Valley with 67 joined classes for the Great Valley at-large. Two-thirds of Great Valley at-large flow was defined by 12 classes. Winds flowed on-axis only 40% of the time. The Great Smoky Mountains helped create down-valley pressure-driven winds, downslope mountain breezes, and divergent air flow. The Cumberland Mountains and Plateau were associated with wind speed reductions in the Central Great Valley, Emory Gap Flow, weak thermally-driven winds, and northwesterly down sloping. Ridge-and-valley terrain enhanced wind direction reversals, pressure-driven winds, as well as locally and regionally produced thermally-driven flow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

RESIDUAL ENERGY SPECTRUM OF SOLAR WIND TURBULENCE

It has long been known that the energy in velocity and magnetic field fluctuations in the solar wind is not in equipartition. In this paper, we present an analysis of 5 yr of Wind data at 1 AU to investigate the reason for this. The residual energy (difference between energy in velocity and magnetic field fluctuations) was calculated using both the standard magnetohydrodynamic (MHD) normalization for the magnetic field and a kinetic version, which includes temperature anisotropies and drifts between particle species. It was found that with the kinetic normalization, the fluctuations are closer to equipartition, with a mean normalized residual energy of {sigma}{sub r} = -0.19 and mean Alfven ratio of r{sub A} = 0.71. The spectrum of residual energy, in the kinetic normalization, was found to be steeper than both the velocity and magnetic field spectra, consistent with some recent MHD turbulence predictions and numerical simulations, having a spectral index close to -1.9. The local properties of residual energy and cross helicity were also investigated, showing that globally balanced intervals with small residual energy contain local patches of larger imbalance and larger residual energy at all scales, as expected for nonlinear turbulent interactions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Study on Modeled Wind Speed Errors Using the U.S. Department of Energy Buoys

Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies Office. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of wind energy in the U.S. waters. The first deployment for one buoy was off the coast of Virginia from December 2014 to May 2016, and the first deployment for the other buoy was off the coast of New Jersey from November 2015 until February 2017. This report describes recent analysis of data collected during these first two deployments. Specifically, we compare hub-height wind speed estimates using Monin-Obukhov Similarity Theory (MOST) to the lidar measurements, and examine how those errors are affected by wind direction, atmospheric stability, wind-wave direction differences, and various measures of the wave-state. The comparisons are done using standard similarity functions based on MOST; including the Businger - Dyer, the Beljaars & Holtslag and the Vickers & Mahrt similarity functions. All models produce large errors over the range of atmospheric stabilities that were observed, with the largest errors occurring for stable flows. The Vickers & Mahrt function resulted in the largest overall bias and standard deviation, while Beljaars & Holtslag function gave the smallest bias and standard deviation due to its better performance under stable conditions. The models perform best under unstable conditions, but even in this regime there is a consistent overestimation of the wind speed of between roughly 0 to 1 ms -1 compared to the lidar measurements. We identify specific metocean conditions (i.e. stability and wind and wave directions) at each of the deployment locations that lead to large errors in MOST predictions. Finally, a coupled ocean-atmosphere model framework was investigated to simulate large errors in weather research forecasting (WRF).

17 WIND ENERGY↗
DOE OSTI2020

Assimilating Scanning Radar Data into High-Resolution Models

This report documents the findings of a project aiming to improve the initial conditions of km-scale simulations of deep convective storms by assimilating cloud-scale weather radar observations. More accurate numerical analyses will increase the effectiveness of research efforts using LES cloud models as tool to better understand land-atmosphere coupling, boundary layer turbulence, and cloud processes, each used for model parameterization development. Work reported on herein includes: i) assessment of radar data sets for use in data assimilation (‘DA’) experiments, ii) quality control of the radar data set and format conversion to one acceptable to DA schemes utilized by the Weather Research and Forecasting (WRF) model, and iii) examination of the sensitivity of the numerical representation of cloud-scale wind and microphysical features to a variety of tunable DA parameters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production by conducting a statistical analysis of historical wind data over a five-year period (2020-2025) from a single 1.5MW turbine manufactured by General Electric (GE) located at NLR’s Flatirons Campus, to generate an experimental test profile that was deployed on a 1.25-MW proton exchange membrane type MC250 electrolyzer system manufactured by Nel Hydrogen . [1] While the electrolyzer balance-of-plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. The historical wind data provided several metrics, however, the analysis particularly focused on the measured power output by the wind turbine. The power output time series of data for each day was categorized by total energy generation and standard deviation, and the day that represented the highest combination of these two metrics was chosen – December 25th, 2022. This process was then repeated for a moving four-hour window within this day to identify the most statistically variable period. Finally, this four-hour period was scaled by 65% to match the 1.25 MW electrolyzer. The electrolysis system controls hydrogen production by varying DC current applied to the stack, from a maximum of 3000 A to a minimum safe operation of 300 A, or 10%. Because the current – voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical wind profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1 Hz frequency. For more details on the statistical analysis process, see the presentation labeled “ Public Reference Data for Megawatt-Scale Hydrogen Electrolysis” provided with each data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}_{scaling factor}-{electrolyzer ramp rate in amperes/second} For instance, “wind-GE1.5MW_0.65-400.zip” represents the hour-long experiment using historical data from the wind-GE1.5MW turbine, scaled to 65%, with the electrolyzer power supply set to a maximum ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and wind power input. A PDF file detailing the historical wind data statistical analysis used to generate the wind profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_historical_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis [1] nelhydrogen.com/product/mc-series-electrolyser .

08 HYDROGEN↗
NASA NTRS1982

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning: Summary report

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This summary report discusses the results of each of the four major tasks of the study. Task 1 compared airline flight plans based on operational forecasts to plans based on the verifying analyses and found that average fuel savings of 1.2 to 2.5 percent are possible with improved forecasts. Task 2 consisted of similar comparisons but used a model developed for the FAA by SRI International that simulated the impact of ATc diversions on the flight plans. While parts of Task 2 confirm the Task I findings, inconsistency with other data and the known impact of ATC suggests that other Task 2 findings are the result of errors in the model. Task 3 compares segment weather data from operational flight plans with the weather actually observed by the aircraft and finds the average error could result in fuel burn penalties (or savings) of up to 3.6 percent for the average 8747 flight. In Task 4 an in-depth analysis of the weather forecast for the 33 days included in the study finds that significant errors exist on 15 days. Wind speeds in the area of maximum winds are underestimated by 20 to 50 kts., a finding confirmed in the other three tasks.

Keitz, J. F.↗
NASA NTRS1993

CAPE for CaPE

In an effort to improve short-term forecasting for the Kennedy Space Center region, Holle et al. (1992) investigated the effects of low level wind regimes on the distribution of cloud-to-ground lightning in central Florida. With a study period of 455 days, Holle et al. (1992) found 'southwest flow contributed 66 percent of the total network flashes while also occurring on the most days (142).' Relationships among mesoscale thermodynamic variables and precipitation and/or lightning have been addressed in previous studies in Canada and the Tennessee valley. Zawadzki et al. (1981) found 'soundings, surface pressure, temperature and humidity obtained from a standard observation network were correlated with rain rates given by raingages and radar.' Buechler et al. (1990) found 'a fair relationship between CAPE (convective available potential energy) and daily cloud-to-ground activity' with a correlation coefficient of r = 0.68. The present research will investigate the relationships among rainfall, cloud-to-ground (CG) lightning, CAPE, and low level wind flow using data collected during the CaPE (Convection and Precipitation/Electrification Experiment) field program. The CaPE field program was conducted in east central Florida from July 8, 1991 to August 18, 1991.

Brooks, Joni↗
NASA NTRS2011

On the Relationship Between High Speed Solar Wind Streams and Radiation Belt Electron Fluxes

Both past and recent research results indicate that solar wind speed has a close connection to radiation belt electron fluxes [e.g., Paulikas and Blake, 1979; Reeves et aI., 2011]: a higher solar wind speed is often associated with a higher level of radiation electron fluxes. But the relationship can be very complex [Reeves et aI., 2011]. The study presented here provides further corroboration of this viewpoint by emphasizing the importance of a global perspective and time history. We find that all the events during years 2010 and 2011 where the >0.8 MeV integral electron flux exceeds 10(exp 5) particles/sq cm/sr/s (pfu) at GEO orbit are associated with the high speed streams (HSS) following the onset of the Stream Interaction Region (SIR), with most of them belonging to the long-lasting Corotating Interaction Region (CIR). Our preliminary results indicate that during HSS events, a maximum speed of 700 km/s and above is a sufficient but not necessary condition for the > 0.8 MeV electron flux to reach 10(exp 5) pfu. But in the exception cases of HSS events where the electron flux level exceeds the 10(exp 5) pfu value but the maximum solar wind speed is less than 700 km/s, a prior impact can be noted either from a CME or a transient SIR within 3-4 days before the arrival of the HSS - stressing the importance of time history. Through superposed epoch analysis and studies providing comparisons with the CME events and the HSS events where the flux level fails to reach the 10(exp 5) pfu, we will present the quantitative assessment of behaviors and relationships of various quantities, such as the time it takes to reach the flux threshold value from the stream interface and its dependence on different physical parameters (e.g., duration of the HSS event, its maximum or average of the solar wind speed, IMF Bz, Kp). The ultimate goal is to apply what is derived to space weather forecasting.

Zheng, Yihua↗
DOE OSTI2024

OSW Consortium 2 - Validated National Offshore Wind Resource Dataset with Uncertainty Quantification (CRADA Report)

This research has led to the development of the 2023 National Offshore Wind data set (NOW-23), which offers the latest wind resource information for offshore regions in the United States. NOW-23 supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published a decade ago and is currently a primary resource for wind resource assessments and grid integration studies in the contiguous United States. By incorporating advancements in the Weather Research and Forecasting (WRF) model, NOW-23 delivers an updated and cutting-edge product to stakeholders. As part of this project, we also developed a summary of the uncertainty quantification in NOW-23, along with NOW-WAKES, a 1-year post-construction data set that quantifies expected offshore wake effects in the US Mid-Atlantic lease areas. Stakeholders can access the NOW-23 data set at https://doi.org/10.25984/1821404.

17 WIND ENERGY↗
NASA NTRS2014

Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

This study assesses the skill of boreal winter Arctic Oscillation (AO) predictions with state-of-the-art dynamical ensemble prediction systems (EPSs): GloSea4, CFSv2, GEOS-5, CanCM3, CanCM4, and CM2.1. Long-term reforecasts with the EPSs are used to evaluate how well they represent the AO and to assess the skill of both deterministic and probabilistic forecasts of the AO. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper level wind, and precipitation associated with the different phases of the AO. The results demonstrate that most EPSs improve upon persistence skill scores for lead times up to 2 months in boreal winter, suggesting some potential for skillful prediction of the AO and its associated climate anomalies at seasonal time scales. It is also found that the skill of AO forecasts during the recent period (1997-2010) is higher than that of the earlier period (1983-1996).

Arctic Oscillation↗
NASA NTRS2014

Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger

Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea.Objectives: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels.Data and methods: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January-May. Incidence was used as a proxy for immunological state.

Soil dust↗
NASA NTRS1982

Weather radar research at the USA's storm laboratory

Radar research that is directed toward improving storm forecasts and hazard warnings and studying lightning is discussed. The two moderately sensitive Doppler weather radars in central Oklahoma, with their wide dynamic range, have demonstrated the feasibility of mapping wind fields in all weather conditions from the clear skies of quiescent air and disturbed prestorm air near the earth's surface to the optically opaque interior of severe and sometimes tornadic thunderstorms. Observations and analyses of Doppler weather radar data demonstrate that improved warning of severe storm phenomena and improved short-term forecast of storms may be available when Doppler techniques are well integrated into the national network of weather radars. When used in combination with other sensors, it provides an opportunity to learn more about the complex interrelations between the wind, water, and electricity in storms.

Doviak, R. J.↗
DOE OSTI2021

Review of Onsite Temperature and Solar Forecasting Models to Enable Better Building Design and Operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. Additionally, this paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗