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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

17 WIND ENERGY↗
NASA NTRS2007

The Impact on EOP Predictions of AAM Forecasts from the ECMWF and NCEP

Predictions of UT1 are improved when dynamical model-based forecasts of the axial component of atmospheric angular momentum (AAM) are used as proxy length-of-day (LOD) forecasts (Freedman et al. 1994; Johnson et al. 2005). For example, the accuracy of JPL's predictions of UT1 are improved by nearly a factor of 2 when AAM forecast data from the National Centers for Environmental Prediction (NCEP) are used. Given the importance of AAM forecasts on the accuracy of UT1 predictions, other sources of AAM forecasts should be sought. Here, the angular momentum of the forecasted wind fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) are computed and used to predict UT1. The results are compared to those obtained using NCEP forecasts.

atmospheric angular momentum (AAM)↗
NASA NTRS2022

Unifying the Validation of Ambient Solar Wind Models

Progress in space weather research and awareness needs community-wide strategies and procedures to evaluate our modeling assets. Here we present the activities of the Ambient Solar Wind Validation Team embedded in the COSPAR ISWAT initiative. We aim to bridge the gap between model developers and end-users to provide the community with an assessment of the state-of-the-art in solar wind forecasting. To this end, we develop an open online platform for validating solar wind models by comparing their solutions with in situ spacecraft measurements. The online platform will allow the space weather community to test the quality of state-of-the-art solar wind models with unified metrics providing an unbiased assessment of progress over time. In this study, we propose a metadata architecture and recommend community-wide forecasting goals and validation metrics. We conclude with a status update of the online platform and outline future perspectives.

Space weather↗
DOE OSTI2025

WFIP3

The Wind Forecasting Improvement Project 3 (WFIP-3) is the first offshore-based wind resource characterization project within the WFIP construct, funded by the U.S. Department of Energy. WFIP-3 will provide a unique field study that will deliver the comprehensive suite of data needed to inform a series of modeling efforts that will develop and evaluate parameterization schemes suited to offshore environments and improved industry-targeted applications. The field study has two goals: (1) detailed sampling of the vertical structure of the Marine Atmospheric Boundary Layer (MABL) at key observational areas, creating a rich dataset that will be used to refine and validate parameterization schemes, and (2) wide-area sampling of the MABL to create a multi-scale array of observations informing and guiding models of resource characterization. We will deploy a multi-platform array of measurements that span the MABL and create a multi-scale observational array stretching south from Marth’s Vineyard across the wind energy areas.

17 WIND ENERGY↗
NASA NTRS1983

The measurement of the synoptic scale wind over the ocean

The effects of mesoscale and microscale fluctuations of the winds on the determination of the synoptic scale wind are studied in order to show the effects of mesoscale variability on the verification of wind forecasts and on the comparison of remotely sensed and anemometer-averaged winds, and to show how to measure the synoptic wind scale more accurately. The frequencies involved correspond to periods longer than one hour and extend to the microscale. Nondimensional spectra that span both the mesoscale and the microscale are derived. Both conventional anemometer averages and remotely sensed winds contain a random component of the mesoscale wind in their values. These components are differences and not errors when winds are compared, and quantitative values for these differences are given. Ways of improving the measurement of the synoptic scale wind by transient ships, data buoys, and scatterometers on future spacecraft are described.

Pierson, W. J., Jr.↗
NASA NTRS2010

An Objective Verification of the North American Mesoscale Model for Kennedy Space Center and Cape Canaveral Air Force Station

The 45th Weather Squadron (45 WS) Launch Weather Officers (LWO's) use the 12-km resolution North American Mesoscale (NAM) model (MesoNAM) text and graphical product forecasts extensively to support launch weather operations. However, the actual performance of the model at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) has not been measured objectively. In order to have tangible evidence of model performance, the 45 WS tasked the Applied Meteorology Unit (AMU; Bauman et ai, 2004) to conduct a detailed statistical analysis of model output compared to observed values. The model products are provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The objective analysis compared the MesoNAM forecast winds, temperature (T) and dew pOint (T d), as well as the changes in these parameters over time, to the observed values from the sensors in the KSC/CCAFS wind tower network shown in Table 1. These objective statistics give the forecasters knowledge of the model's strengths and weaknesses, which will result in improved forecasts for operations.

Bauman, William H., III↗
DOE OSTI2019

Spatial Variability of Winds and HRRR–NCEP Model Error Statistics at Three Doppler-Lidar Sites in the Wind-Energy Generation Region of the Columbia River Basin

Annually and seasonally averaged wind profiles from three Doppler lidars were gathered from sites in the Columbia River basin of east-central Oregon and Washington, a major region of wind-energy production, for the Second Wind Forecast Improvement Project (WFIP2) experiment. The profile data are used to quantify the spatial variability of wind flows in this area of complex terrain, to assess the HRRR-NCEP model's ability to capture spatial and temporal variability of wind profiles, and to evaluate model errors. Annually averaged measured wind speed differences over the 70-km extent of the lidar measurements reached 1 m s -1 within the wind-turbine rotor layer, and 2 m s -1 for 200-500 m AGL. Stronger wind speeds in the lowest 500 m occurred at sites higher in elevation, farther from the river, and farther west - closer to the Cascade Mountain barrier. Validating against the lidar data, the HRRR model underestimated strong wind speeds (>12 m s -1 ) and, consequently, their frequency of occurrence, especially at the two lowest-elevation sites, producing annual low biases in rotor-layer wind speed of 0.5 m s -1 . The RMSE between measured and modeled winds at all sites was about 3 m s -1 and did not degrade significantly with forecast lead time. The nature of the model errors was different for different seasons. Furthermore, although the three sites were located in the same basin terrain, the nature of the model errors was different at each site. Thus, if only one of the sites had been instrumented, different conclusions would have been drawn as to the major sources of model error, depending on where the measurements were made.

17 WIND ENERGY↗
DOE OSTI2026

Hurricane wind field representation shapes storm surge and building-scale flood hazard estimates

Coastal flood hazard estimates rely on precise hurricane wind forecasts to assess damage and risk. Here, we demonstrate that errors in hurricane wind field representation can lead to significant biases in storm surge and property-level damage estimates. Using Hurricane Ian (2022) as a case study, we compare widely used parametric, reanalysis, and hybrid wind datasets. Improved wind field accuracy reduces storm surge and damage estimate bias by up to 70\%. Our results underscore the importance of accurately predicting hurricane wind structure in hazard assessments.

Coastal Flooding↗
DOE OSTI2018

Evaluating the performance of the immersed boundary method within the grey zone for improved weather forecasts in the HRRR model

Improved performance of the High Resolution Rapid Refresh (HRRR) weather forecast in regions of complex terrain is a primary goal of the second Wind Forecasting Improvement Project (WFIP2). The HRRR forecast uses the Weather Research and Forecasting (WRF) model at a horizontal resolution of 3 km and includes an experimental nest over the WFIP2 study area with 750 m horizontal resolution. At these resolutions the complex terrain of the WFIP2 study area, the Columbia River Gorge, is well resolved and can include steep terrain slopes. The terrain-following coordinate system used by the WRF model is susceptible to numerical instabilities as model resolution and resolved terrain slopes increase. The immersed boundary method (IBM) has been shown to reduce these instabilities at very high resolutions (roughly 10 m or less), and thus use of IBM is desirable for high resolution simulations over the WFIP2 study area. However, accuracy of the IBM appears to degrade with coarser grid resolution and the sensitivities of IBMs to grid resolution are not well understood. In this report, we work to understand the accuracy and performance of the IBM at coarser resolutions with the goal of developing an IBM for use in the grey zone, or the intermediate scales between mesoscale and large-eddy simulation. Here, several existing immersed boundary algorithms from the literature are implemented into WRF, along with a new implementation developed during this project. The IBM algorithms for the atmospheric boundary layer are evaluated within the WRF model for a range of grid resolutions. While the strong performance of IBMs at high-resolution is confirmed, important model sensitivities are also revealed. Particular focus is placed on IBM performance at intermediate grid resolutions, which to this point has not been studied in the atmospheric modeling literature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Model: Year-Long Reforecast, Forecast, Domain 02

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

17 WIND ENERGY↗
DOE OSTI2022

Model: Year-Long Reforecast, Forecast, Domain 01

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

17 WIND ENERGY↗
DOE OSTI2022

Model: 10-Day Retrospective, HRRR, Config 01, Forecast Domain 01

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

17 WIND ENERGY↗
DOE OSTI2022

Model: 10-Day Retrospective, HRRR, Config 01, Forecast Domain 02

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

17 WIND ENERGY↗
DOE OSTI2022

Model: 10-Day Retrospective, RAP, Config 02, Forecast Domain 01

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

17 WIND ENERGY↗
DOE OSTI2022

Model: 10-Day Retrospective, HRRR, Config 02, Forecast Domain 01

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

17 WIND ENERGY↗
DOE OSTI2022

Model: 10-Day Retrospective, HRRR, Config 02, Forecast Domain 02

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

17 WIND ENERGY↗
DOE OSTI2022

Model: 10-Day Retrospective, RAP, Config 01, Forecast Domain 01

The primary purpose of WFIP2 Model Development Team is to improve existing numerical weather prediction models in a manner that leads to improved wind forecasts in regions of complex terrain. Improvements in the models will come through better understanding of the physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

17 WIND ENERGY↗