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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Lidar-Based Evaluation of HRRR Performance in California’s Diablo Range

The performance of the NOAA High-Resolution Rapid Refresh (HRRR) model for capturing low-level winds near a wind energy production site during summer 2019 is evaluated. This study catalogs the ability of HRRR to predict boundary layer dynamics relevant to wind energy interests over complex terrain, which has presented challenges for weather and energy forecasting. Performance is evaluated by comparing HRRR output to wind-profiling Doppler lidars at Lawrence Livermore National Laboratory Site 300. HRRR captured the diurnal profile of horizontal winds in the observed 150-m layer, despite strong underpredictions (∼4 m s −1 ) during evening and nighttime hours. These underpredictions may be a result of local speedup flows observed by the lidars, which were unresolved in HRRR due to their small spatial extent. HRRR bias magnitude relative to observations was found to be minimal during days with synoptic-scale troughs and strong 850-hPa geopotential gradients, while bias magnitude was maximal during days with synoptic ridging and weak 850-hPa geopotential gradients. To translate wind speed predictions to energy forecasting, generic turbine models were used to estimate power generation for turbines characteristic of the nearby Altamont Pass Wind Resource Area. Results show that HRRR-based energy estimates predicted daytime power generation adequately relative to lidar-based estimates with an 18-h lead time (bias magnitude < 0.4 MW from 0900 to 1400 LT) but overpredicted power during the rest of the diurnal cycle (bias > 1 MW). These results demonstrate conditions under which HRRR performs well for wind energy applications in complex terrain, while highlighting biases that require further investigation to support usage of a high-resolution model for wind energy forecasts.

Boundary layer↗
DOE OSTI2017

Chapter 16 - Predictive Analytics for Comprehensive Energy Systems State Estimation

Energy sustainability is a subject of concern to many nations in the modern world. It is critical for electric power systems to diversify energy supply to include systems with different physical characteristics, such as wind energy, solar energy, electrochemical energy storage, thermal storage, bio-energy systems, geothermal, and ocean energy. Each system has its own range of control variables and targets. To be able to operate such a complex energy system, big-data analytics become critical to achieve the goal of predicting energy supplies and consumption patterns, assessing system operation conditions, and estimating system states - all providing situational awareness to power system operators. This chapter presents data analytics and machine learning-based approaches to enable predictive situational awareness of the power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2016

Wind Information Uplink to Aircraft Performing Interval Management Operations

Interval Management (IM) is an ADS-B-enabled suite of applications that use ground and flight deck capabilities and procedures designed to support the relative spacing of aircraft (Barmore et al., 2004, Murdoch et al. 2009, Barmore 2009, Swieringa et al. 2011; Weitz et al. 2012). Relative spacing refers to managing the position of one aircraft to a time or distance relative to another aircraft, as opposed to a static reference point such as a point over the ground or clock time. This results in improved inter-aircraft spacing precision and is expected to allow aircraft to be spaced closer to the applicable separation standard than current operations. Consequently, if the reduced spacing is used in scheduling, IM can reduce the time interval between the first and last aircraft in an overall arrival flow, resulting in increased throughput. Because IM relies on speed changes to achieve precise spacing, it can reduce costly, low-altitude, vectoring, which increases both efficiency and throughput in capacity-constrained airspace without negatively impacting controller workload and task complexity. This is expected to increase overall system efficiency. The Flight Deck Interval Management (FIM) equipment provides speeds to the flight crew that will deliver them to the achieve-by point at the controller-specified time, i.e., assigned spacing goal, after the target aircraft crosses the achieve-by point (Figure 1.1). Since the IM and target aircraft may not be on the same arrival procedure, the FIM equipment predicts the estimated times of arrival (ETA) for both the IM and target aircraft to the achieve-by point. This involves generating an approximate four-dimensional trajectory for each aircraft. The accuracy of the wind data used to generate those trajectories is critical to the success of the IM operation. There are two main forms of uncertainty in the wind information used by the FIM equipment. The first is the accuracy of the forecast modeling done by the weather provider. This is generally a global environmental prediction obtained from a weather model such as the Rapid Refresh (RAP) from the National Centers for Environmental Prediction (NCEP). The weather forecast data will have errors relative to the actual, or truth, winds that the aircraft will encounter. The second source of uncertainty is that only a small subset of the forecast data can be uplinked to the aircraft for use by the FIM equipment. This results in loss of additional information. The Federal Aviation Administration (FAA) and RTCA are currently developing standards for the communication of wind and atmospheric data to the aircraft for use in NextGen operations. This study examines the impact of various wind forecast sampling methods on IM performance metrics to inform the standards development.

Ahmad, Nashat N.↗
NASA NTRS1979

The benefits of using short-interval satellite images to derive winds for tropical cyclones

During the 1975, 1976 and 1977 North Atlantic hurricane seasons, NOAA/NESS and NASA/GSFC conducted a cooperative program to determine the best resolution and frequency now available from satellite images for deriving winds to study and forecast tropical cyclones. High spatial and temporal resolution satellite imagery made it feasible to provide a large number of lower and upper tropospheric winds which can be obtained by tracking clouds within 650 km of tropical cyclone centers. Up to 10 (5) times as many low-level winds were derived from images spaced at 3 or 7.5 min intervals as from those at 30 min (15 min) intervals. Rapid-scan full-resolution IR and visible images minimized the 'erroneous' winds derived by tracking cloud elements that propagate by growing on one side and dissipating on the other and by tracking repetitive patterns that provided ambiguous indications of direction of movement.

Rodgers, E.↗
NASA NTRS1983

The influence of the tropics on the prediction of ultralong waves. I - Tropical wind field

The effects of tropical wind data from the FGGE and tropical latent heating on numerical modeling of ultralong waves are considered in a two-part study. The model studied is the global fourth-order GLAS general circulation model, an energy-conserving format with horizontal differences calculated with fourth-order accuracy. Data assimilation experiments were performed with and without the wind data, with account taken of eastward and northward wind components, the geopotential height, and the relative humidity, all over pressure surfaces. The initial conditions were used to generate six pairs of forecasts, and the tropical wind error decreased after two days of prediction when the initial conditions contained the wind data. The deviations from the measured planetary wave data were attributed to differences in the initial rotational wind field, which varied on a three-day basis. The latent heat initial data had a five-day period and extended its influence beyond the tropical zone. The tropical heat sources sustained the tropical westerlies in the GLAS model, and removal of the tropical heat sources reversed the wind to easterlies.

Baker, W. E.↗
NASA NTRS2014

The Use of Red Green Blue Air Mass Imagery to Investigate the Role of Stratospheric Air in a Non-Convective Wind Event

The investigation of non-convective winds associated with passing extratropical cyclones and the formation of the sting jet in North Atlantic cyclones that impact Europe has been gaining interest. Sting jet research has been limited to North Atlantic cyclones that impact Europe because it is known to occur in Shapiro-Keyser cyclones and theory suggests it does not occur in Norwegian type cyclones. The global distribution of sting jet cyclones is unknown and questions remain as to whether cyclones with Shapiro-Keyser characteristics that impact the United States develop features similar to the sting jet. Therefore unique National Aeronautics and Space Administration (NASA) products were used to analyze an event that impacted the Northeast United States on 09 February 2013. Moderate Resolution Imaging Spectroradiometer (MODIS) Red Green Blue (RGB) Air Mass imagery and Atmospheric Infrared Sounder (AIRS) ozone data were used in conjunction with NASA's global Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis and higher-resolution regional 13-km Rapid Refresh (RAP) data to analyze the role of stratospheric air in producing high winds. The RGB Air Mass imagery and a new AIRS ozone anomaly product were used to confirm the presence of stratospheric air. Plan view and cross sectional plots of wind, potential vorticity, relative humidity, omega, and frontogenesis were used to analyze the relationship between stratospheric air and high surface winds during the event. Additionally, the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to plot trajectories to determine the role of the conveyor belts in producing the high winds. Analyses of new satellite products, such as the RGB Air Mass imagery, show the utility of future GOES-R products in forecasting non-convective wind events.

Berndt, E. B.↗
NASA NTRS2007

Users Guide for the Anvil Threat Corridor Forecast Tool V1.7.0 for AWIPS

The Applied Meteorology Unit (AMU) originally developed the Anvil Threat Sector Tool for the Meteorological Interactive Data Display System (MIDDS) and delivered the capability in three phases beginning with a feasibility study in 2000 and delivering the operational final product in December 2003. This tool is currently used operationally by the 45th Weather Squadron (45 WS) Launch Weather Officers (LWO) and Spaceflight Meteorology Group (SMG) forecasters. Phase I of the task established the technical feasibility of developing an objective, observations-based tool for short-range anvil forecasting. The AMU was subsequently tasked to develop short-term anvil forecasting tools to improve predictions of the threat of triggered lightning to space launch and landing vehicles. Under the Phase II effort, the AMU developed a nowcasting anvil threat sector tool, which provided the user with a threat sector based on the most current radiosonde upper wind data from a co-located or upstream station. The Phase II Anvil Threat Sector Tool computes the average wind speed and direction in the layer between 300 and 150 mb from the latest radiosonde for a user-designated station. The following threat sector properties are consistent with the propagation and lifetime characteristics of thunderstorm anvil clouds observed over Florida and its coastal waters (Short et al. 2002): a) 20 n mi standoff circle, b) 30 degree sector width, c) Orientation given by 300 to 150 mb average wind direction, d) 1-, 2-, and 3- hour arcs in upwind direction, and e) Arc distances given by 300 to 150 mb average wind speed. Figure 1 is an example of the MIDDS Anvil Threat Sector tool overlaid on a visible satellite image at 2132 UTC 13 May 2001. Space Launch Complex 39A was selected as the center point and the Anvil Threat Sector was determined from upper-level wind data at 1500 UTC in the preconvective environment. Narrow thunderstorm anvil clouds extend from central Florida to the space launch and landing facilities at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) and beyond. The anvil clouds were generated around 1930 UTC (1430 EDT) by thunderstorm activity over central Florida and transported 90 n mi east-northeastward within 2 hours, as diagnosed by the anvil forecast tool. Phase III, delivered in February 2003, built upon the results of Phase II by enhancing the Anvil Threat Sector Tool with the capability to use national model forecast winds for depiction of potential anvil lengths and orientations over the KSC/CCAFS area with lead times from 3 through 168 hours (7 days). In September 2003, AMU customers requested the capability to use data from the KSC 50 MHz Doppler Radar Wind Profiler (DRWP) in the Anvil Threat Sector Tool and this capability was delivered by the AMU in December 2003. In March 2005, the AMU was tasked to migrate the MIDDS Anvil Threat Sector Tool capabilities onto the Advanced Weather Interactive Processing System (AWIPS) as the Anvil Threat Corridor Forecast Tool.

Bauman, William H., III↗
NASA NTRS1993

A satellite-borne radar wind sensor (RAWS)

Modeling global atmospheric circulations and forecasting the weather would improve if worldwide information on winds aloft were available. Accurate prediction of weather is important to agriculture, shipping, air traffic, and many other fields. Global system models of climate are of great importance. Current global atmospheric models use pressure measurements and thermodynamic properties to calculate the effects of wind for use in Numerical Weather Prediction (NWP) models. Inputs to the NWP models are temperature, pressure and wind velocities at different heights. Clearly direct wind measurements could significantly improve the NWP model performance. The RAdar Wind Sounder (RAWS) program at the University of Kansas is a study of the feasibility and the trade-offs in the design of a space-based radar system to measure wind vectors. This can be done by measuring the Doppler shift of cloud and rain returns from three or more points and calculating the components of the wind vector. The RAWS study to date uses the candidate system selected after preliminary study of frequencies and sensitivities. Two frequencies chosen, 10 and 35 GHz, allow higher sensitivity for clouds and more penetration for rain. The past year was devoted to modeling the signal-to-noise ratio (SNR) achievable for the two frequencies. The determination of SNR versus cloud penetration depth used a cloud backscattering and attenuation model in the appropriate radar equation. Calculations assumed reasonable losses in reception and transmission, in addition to the atmospheric attenuation. We discovered that ice clouds provide a higher SNR than previously calculated, but some water clouds give lower SNRs than we calculated before. One of the primary issues in the SNR calculation was the choice of the drop size distribution. Although Xin used several distributions (e.g., log normal, Khrigian and Mazin), this year we used the Deirmendjian cloud model. SNR versus cloud penetration plots were generated to validate the candidate system. Rain, which appears in the cloud models at the lower altitudes, provides ample SNR, as do the higher clouds composed of ice particles. However, in some cloud situations we found the sensitivity for the clouds was marginal or inadequate. At 35 GHz, two of the cloud models characterized by 1 to 2 g/cu m of water content at altitudes extending from 150 to 1500 meters, produced a sufficient SNR. Other models, however, with water contents ranging from 0.5 to 4 g/cu m and altitudes up to 4000 meters, exhibit SNR of -3 to -23 dB, largely because of attenuation in the upper cloud layers. These results coupled with the lower SNR at 10 GHz, led to an investigation of alternate frequencies. The rain present beneath these clouds provides adequate SNR at 10 GHz, and in most cases, at GHz.

Moore, Richard K.↗
DOE OSTI2022

Sensitivity Analysis of Wind and Turbulence Predictions With Mesoscale‐Coupled Large Eddy Simulations Using Ensemble Machine Learning

Abstract Coupling between mesoscale models and large‐eddy simulation (LES) models is increasingly used to more realistically represent the wide range of scales of atmospheric motions affecting boundary layer winds and turbulence that need to be simulated accurately for applications such as wind energy. However, such mesoscale‐to‐microscale coupled modeling frameworks are potentially affected by a large number of uncertain closure parameters. Here, we investigate the sensitivity associated with six closure parameters related to a 1.5‐order subgrid‐scale turbulence closure for an ensemble of mesoscale‐coupled LES. The simulations are performed using the Weather Research and Forecasting model nested from horizontal resolutions of greater than a kilometer down to tens of meters. Closure parameters are varied to generate perturbed parameter ensembles for two case studies of highly sheared, convective boundary layers observed in the Columbia Basin of Oregon and Washington during the Second Wind Forecast Improvement Project. Machine learning algorithms are used to explore the sensitivity of LES predictions, considering the effects of the perturbed physical parameters alongside categorical factors such as the case study identity, measurement location, and LES resolution. For the conditions we examine, a single parameter, the eddy viscosity coefficient, is the dominant source of parametric sensitivity and its importance is comparable to the categorical factors for several of the simulation response variables we examine.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Automatic detection of low altitude wind shear due to gust fronts in the terminal Doppler weather radar operational demonstration

A gust front is the leading edge of the cold air outflow from a thunderstorm. Wind shears and turbulence along the gust front may produce potentially hazardous conditions for an aircraft on takeoff or landing such that runway operations are significantly impacted. The Federal Aviation Administration (FAA) has therefore determined that the detection of gust fronts in the terminal environment be an integral part of the Terminal Doppler Weather Radar (TDWR) system. Detection of these shears by the Gust Front Algorithm permits the generation of warnings that can be issued to pilots on approach and departure. In addition to the detection capability, the algorithm provides an estimate of the wind speed and direction following the gust front (termed wind shift) and the forecasted location of the gust front up to 20 minutes before it impacts terminal operations. This has shown utility as a runway management tool, alerting runway supervisors to approaching wind shifts and the possible need to change runway configurations. The formation and characteristics of gust fronts and their signatures in Doppler radar data are discussed. A brief description of the algorithm and its products for use by Air Traffic Control (ATC), along with an assessment of the algorithm's performance during the 1988 Operational Test and Evaluation, is presented.

Klingle-Wilson, Diana↗
NASA NTRS2018

Global Assimilation of Loon Stratospheric Balloon Observations

Project Loon has an overall goal of providing worldwide internet coverage using a network of long-durationsuper-pressure balloons. Since 2013, Loon has launched over 1600 balloons from multiple tropical and middlelatitude locations. These GPS tracked balloon trajectories provide lower stratospheric wind information overthe oceans and remote land areas where traditional radiosonde soundings are sparse, thus providing uniquecoverage of lower stratospheric winds. To fully investigate these Loon winds we: 1) compare the Loon windsto winds produced by a global data assimilation system (DAS: NASA GEOS) and 2) assimilate the Loon windsinto the same comprehensive DAS. Results show that in middle latitudes the Loon winds and DAS winds agreewell, and the Loon wind assimilation has only a minor impact on the forecasts. However, in the Tropics, thereis often a substantial difference between the assimilated winds and the observed Loon winds, of 8 m/s or morein magnitude. In these cases, assimilating the Loon winds significantly improves the meteorological analysesand subsequently the forecasts of the Loon winds. By highlighting cases where the Loon and DAS winds differ,these results can lead to improved understanding of stratospheric winds, especially in the tropics, as well asaiding analyses of the representation of dynamical forcing mechanisms in the GEOS model.

Coy, Lawrence↗
DOE OSTI2021

A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system

Hydro-wind-photovoltaic (PV) hybrid system has the potential to increase the integration of renewable energy sources into an existing grid. For the long-term operation of the system, due to the non-storable nature of wind and PV power, it is essentially to decide the optimal long-term carryover storage of cascade reservoirs. However, it remains a challenge due to high uncertainties of long-term forecasts and complicated hydraulic/electrical relationships between cascade reservoirs. Here in this study, a forecast-driven decision-making model is proposed for the hybrid system, which converts the multi-stage long-term operation process into a two-stage operation problem (including current stage and carryover stage) to avoid using longer-horizon forecast information with lower accuracy. First, the carryover stage energy surfaces (CESs) considering the forecast uncertainties of wind, solar and hydro resources are proposed to characterize carryover stage benefit quantitatively. Then a CESs-based forward decision-making optimization model is developed to guide the long-term operation of a hydro-wind-photovoltaic hybrid system. The applications in a hydro-wind-PV hybrid system of Yalong River basin results show that: compared with conventional operation, 1) power generation increases 9.03%; 2) in terms of the carryover storages control, the reservoir impounding and drawdown timing are delayed, and the drawdown depth is increased, which can be used to formulate better reservoir operation rules.

13 HYDRO ENERGY↗
NASA NTRS1999

Follow-on Missions to SPARCLE

Global tropospheric wind observations are considered the number one unaccommodated atmospheric observation for the new series of NPOESS (National Polar Orbiting Environmental Satellite Series) platforms. The operational meteorology communities have long recognized the potential for improved forecasting skill if accurate wind observations were available around the globe. Recent analyses suggest that wind observations such as those that could be made by a space-based Doppler wind lidars; would even result in greater improvements than better temperature soundings. In this paper we present a roadmap that begins with the NASA approved SPAce Readiness Coherent Lidar Experiment (SPARCLE) and ends with an operational DWL (Doppler Wind Lidar) on an NPOESS platform.

Emmitt, G. D.↗
NASA NTRS1992

Radar sensitivity and antenna scan pattern study for a satellite-based Radar Wind Sounder (RAWS)

Modeling global atmospheric circulations and forecasting the weather would improve greatly if worldwide information on winds aloft were available. Recognition of this led to the inclusion of the LAser Wind Sounder (LAWS) system to measure Doppler shifts from aerosols in the planned for Earth Observation System (EOS). However, gaps will exist in LAWS coverage where heavy clouds are present. The RAdar Wind Sensor (RAWS) is an instrument that could fill these gaps by measuring Doppler shifts from clouds and rain. Previous studies conducted at the University of Kansas show RAWS as a feasible instrument. This thesis pertains to the signal-to-noise ratio (SNR) sensitivity, transmit waveform, and limitations to the antenna scan pattern of the RAWS system. A dop-size distribution model is selected and applied to the radar range equation for the sensitivity analysis. Six frequencies are used in computing the SNR for several cloud types to determine the optimal transmit frequency. the results show the use of two frequencies, one higher (94 GHz) to obtain sensitivity for thinner cloud, and a lower frequency (24 GHz) to obtain sensitivity for thinner cloud, and a lower frequency (24 GHz) for better penetration in rain, provide ample SNR. The waveform design supports covariance estimation processing. This estimator eliminates the Doppler ambiguities compounded by the selection of such high transmit frequencies, while providing an estimate of the mean frequency. the unambiguous range and velocity computation shows them to be within acceptable limits. The design goal for the RAWS system is to limit the wind-speed error to less than 1 ms(exp -1). Due to linear dependence between vectors for a three-vector scan pattern, a reasonable wind-speed error is unattainable. Only the two-vector scan pattern falls within the wind-error limits for azimuth angles between 16 deg to 70 deg. However, this scan only allows two components of the wind to be determined. As a result, a technique is then shown, based on the Z-R-V relationships, that permit the vertical component (i.e., rain) to be computed. Thus the horizontal wind components may be obtained form the covariance estimator and the vertical component from the reflectivity factor. Finally, a new candidate system is introduced which summarizes the parameters taken from previous RAWS studies, or those modified in this thesis.

Stuart, Michael A.↗
NASA NTRSDate not supplied

Lidar and UAS Measurements of Winds for the NASA Advanced Air Mobility Mission

The envisioned future of Advanced Air Mobility involves low-altitude operation of a new class of air vehicles. The low altitudes of the flight paths would have these vehicles spending most of their operation in conditions where unpredictable wind and turbulence effects may occur. Wind measurements will hence be critical to ensure safe and efficient operations for AAM. Such wind measurement could be used as a monitoring system for warning of hazardous wind events, as data input to forecasting models, or as a research tool to understand wind effects in complex environments. To meet this need NASA is evaluating wind sensing technologies with a capability to probe the atmospheric boundary layer. Doppler lidar is a leading candidate ground-based sensor, as shown by the decades-long history of being an effective tool for many wind studies. However, there is a need to re-assess the Doppler wind lidars for the AAM application which involves evaluating wind effects as vehicles operate in and out of vertiports, notably regarding spatial resolution. Meeting the needs for spatial resolution may involve the use of dual-Doppler techniques, rather than just single Doppler lidar. The study was furthermore motivated by looking toward the future of AAM, in which the air vehicles involved can also provide wind measurements. Airborne wind measurements, obtained directly from vehicle-mounted anemometers or indirectly from vehicle navigation data, offer a means to compare remotely sensed wind lidar with in-situ measurements. The following sections report on results of wind measurements obtained using lidar and the small uninhabited aircraft systems (sUAS’s) operating in the same volume of air.

Adam Medina↗
NASA NTRS2008

Applied Meteorology Unit (AMU)

This report summarizes the Applied Meteorology Unit (AMU) activities for the second quarter of Fiscal Year 2008 (January - March 2008). Projects described are: (1) Peak Wind Tool for User Launch Commit Criteria (LCC), (2) Peak Wind Tool for General Forecasting, (3) Situational Lightning Climatologies for Central Florida. Phase III, (4) Volume Averaged Height Integrated Radar Reflectivity (VAHIRR), (5) Impact of Local Sensors, (6) Radar Scan Strategies for the PAFB WSR-74C Replacement and (7) WRF Wind Sensitivity Study at Edwards Air Force Base.

Bauman, William H., Jr.↗
NASA NTRS1986

Analysis and forecast experiments incorporating satellite soundings and cloud and water vapor drift wind information

A system for assimilating conventional meteorological data and satellite-derived data in order to produce four-dimensional gridded data sets of the primary atmospheric variables used for updating limited area forecast models is described. The basic principles of a data assimilation scheme as proposed by Lorenc (1984) are discussed. The design of the system and its incremental assimilation cycles are schematically presented. The assimilation system was tested using radiosonde, buoy, VAS temperature, dew point, gradient wind data, cloud drift, and water vapor motion data. The rms vector errors for the data are analyzed.

Goodman, Brian M.↗