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Simple, averaged wind velocity data provide information on energy availability, facilitate generator site selection and enable appropriate operating ranges to be established for windpowered plants. They also provide a basis for the prediction of extreme wind speeds.
Once potential wind turbine sites (either for single installations or clusters) are identified through siting procedures, actual evaluation of the sites must commence. This evaluation is needed to obtain estimates of wind turbine performance and to identify hazards to the machine from the turbulence component of the atmosphere. These estimates allow for more detailed project planning and for preliminary financing arrangements to be secured. The site evaluation process can occur in two stages: (1) utilizing existing nearby data, and (2) establishing and monitoring an onsite measurement program. Since step (2) requires a period of at least 1 yr or more from the time a potential site has been identified, step (1) is often an essential stage in the preliminary evaluation process. Both the methods that have been developed and the unknowns that still exist in assessing the representativeness of available data to a nearby wind turbine site are discussed. How the assessment of the representativeness of available data can be used to develop a more effective onsite meteorological measurement program is also discussed.
Vector winds computed from GMD-1 and FPS-16 radar tracking data indicate accurate wind macro- and mesoscale resolution from GMD data
It is noted that in 1976, 17 candidate sites were identified for detailed evaluation as potential sites for installation of large, horizontal axis Wind Turbines (WT). Attention is given to the Mod-OA, a 200 kW WT located in Clayton, New Mexico. The discussion covers the meteorological data collected, some of the analyses based on these wind data as well as additional areas currently being investigated in relation to these data.
Accuracy of wind data obtained by tracking wind sensors simultaneously with two FPS-16 radars
Several densities of tropical divergent wind data were included in a fourth-order GCM to examine the effects on the accuracy of the model predictions. The experiments covered assimilation of all available tropical wind data, no tropical wind data between 20 deg N and 20 deg S, only westerly tropical wind data and only easterly tropical wind data. The predictions were all made for the 200 mb upper troposphere. Elimination of tropical data produced excessively strong upper tropospheric westerlies which in turn amplified the globally integrated rotational flow kinetic energy by around 10 percent and doubled the global divergent flow kinetic energy. Retaining only easterly wind data, ameliorated most of the error. Inclusion of all the tropical wind data however, did not lead to overall positive effects, as the data were linked to tropical wave energetics and ageostrophic winds which were already assimilated in the model.
The status of the use of wind information is presented in four areas, namely: operational control, design verification, power performance analysis, and lifetime estimation. Attention is given to some of the identified wind information needs and the steps taken to meet these needs.
A method of analyzing Met Rocket wind data is described. Modern tidal theory and specialized analytical techniques were used to resolve specific tidal modes and prevailing components in observed wind data. A representation of the wind which is continuous in both space and time was formulated. Such a representation allows direct comparison with theory, allows the derivation of other quantities such as temperature and pressure which in turn may be compared with observed values, and allows the formation of a wind model which extends over a broader range of space and time. Significant diurnal tidal modes with wavelengths of 10 and 7 km were present in the data and were resolved by the analytical technique.
The use of mass and wind data in numerical weather prediction is examined. The applicability of the mass and wind data on the skill of numerical weather prediction is evaluated by real data assimilation experiments using the the NASA/Goddard Laboratory for Atmospheres analysis/forecast system of Baker (1983) and Kalnay et al. (1983). It is observed that the wind observations are important for small scales and in the tropics and that the wind observations are more accurate than mass observations.
In this report, we present the latest wind resource data specifically tailored for offshore regions in the United States. The data set, known as the 2023 National Offshore Wind data set (NOW-23), has been developed by the National Renewable Energy Laboratory (NREL) and its partners, and surpasses the previous resource data set, the Wind Integration National Dataset (WIND) Toolkit, which was released approximately ten years ago, in its offshore component. The WIND Toolkit has been widely utilized by stakeholders involved in wind resource assessments across the continental United States. However, with significant advancements in numerical weather prediction modeling over the past decade, the NOW-23 data set incorporates the latest research and development progress to provide stakeholders with an updated and cutting-edge resource for offshore wind analysis. The NOW-23 data set is created using the Weather Research and Forecasting (WRF) model and its output is available, as for its predecessor, at 5-minute time resolution and 2-kilometer horizontal spatial resolution. However, the NOW-23 data set improves upon the WIND Toolkit through: 1. A modeling period of at least 20 years (and as long as 23 years in selected regions), starting in 2000 (compared to the 7-year 2007–2013 modeling period in the WIND Toolkit). 2. For several offshore regions, a region-specific sensitivity analysis, driven by an ensemble of WRF simulations, to assess the most adequate region-specific WRF setup. 3. An updated WRF model, from Version 3.4 used in the WIND Toolkit to Version 4.2.1 used for the NOW-23 data set, which incorporates significant research advancements. 4. The use of the state-of-the-art reanalysis product ERA5 (which supersedes the older ERA-Interim used in the WIND Toolkit) to provide atmospheric forcing at the WRF domain boundaries. Figure 1 shows the NOW-23 mean wind speed at 160 m above sea level (asl), which we use as proxy for hub-height of a commercial offshore wind turbine in this report across all regions.
The National Aeronautics and Space Administration s (NASA) Kennedy Space Center (KSC) has installed a five-instrument 915-Megahertz (MHz) Doppler Radar Wind Profiler (DRWP) system that records atmospheric wind profile properties. The purpose of these profilers is to fill data gaps between the top of the KSC wind tower network and the lowest measurement altitude of the KSC 50-MHz DRWP. The 915-MHz DRWP system has the capability to generate three-dimensional wind data outputs from approximately 150 meters (m) to 6,000 m at roughly 15-minute (min) intervals. NASA s long-term objective is to combine the 915-MHz and 50-MHz DRWP systems to create complete vertical wind profiles up to 18,300 m to be used in trajectory and loads analyses of space vehicles and by forecasters on day-of-launch (DOL). This analysis utilizes automated and manual quality control (QC) processes to remove erroneous and unrealistic wind data returned by the 915-MHz DRWP system. The percentage of data affected by each individual QC check in the period of record (POR) (i.e., January to April 2006) was computed, demonstrating the variability in the amount of data affected by the QC processes. The number of complete wind profiles available at given altitude thresholds for each profiler in the POR was calculated and outputted graphically, followed by an assessment of the number of complete wind profiles available for any profiler in the POR. A case study is also provided to demonstrate the QC process on a day of a known weather event.
Upper-level wind profiles obtained from a 50-MHz Doppler Radar Wind Profiler (DRWP) instrument at Kennedy Space Center are incorporated in space launch vehicle design and day-of-launch operations to assess wind effects on the vehicle during ascent. Automated and manual quality control (QC) techniques are implemented to remove spurious data in the upper-level wind profiles caused from atmospheric and non-atmospheric artifacts over the 2010-2012 period of record (POR). By adding the new quality controlled profiles with older profiles from 1997-2009, a robust database will be constructed of upper-level wind characteristics. Statistical analysis will determine the maximum, minimum, and 95th percentile of the wind components from the DRWP profiles over recent POR and compare against the older database. Additionally, this study identifies specific QC flags triggered during the QC process to understand how much data is retained and removed from the profiles.
Upper-level wind profiles obtained from a 50-MHz Doppler Radar Wind Profiler (DRWP) instrument at Kennedy Space Center are incorporated in space launch vehicle design and day-of-launch operations to assess wind effects on the vehicle during ascent. Automated and manual quality control (QC) techniques are implemented to remove spurious data in the upper-level wind profiles caused from atmospheric and non-atmospheric artifacts over the 2010-2012 period of record (POR). By adding the new quality controlled profiles with older profiles from 1997-2009, a robust database will be constructed of upper-level wind characteristics. Statistical analysis will determine the maximum, minimum, and 95th percentile of the wind components from the DRWP profiles over recent POR and compare against the older database. Additionally, this study identifies specific QC flags triggered during the QC process to understand how much data is retained and removed from the profiles.
Upper-level wind profiles obtained from a 50-MHz Doppler Radar Wind Profiler (DRWP) instrument at Kennedy Space Center are incorporated in space launch vehicle design and day-of-launch operations to assess wind effects on the vehicle during ascent. Automated and manual quality control (QC) techniques are implemented to remove spurious data in the upper-level wind profiles caused from atmospheric and non-atmospheric artifacts over the 2010-2012 period of record (POR). By adding the new quality controlled profiles with older profiles from 1997-2009, a robust database will be constructed of upper-level wind characteristics. Statistical analysis will determine the maximum, minimum, and 95th percentile of the wind components from the DRWP profiles over recent POR and compare against the older database. Additionally, this study identifies specific QC flags triggered during the QC process to understand how much data is retained and removed from the profiles.
Enhancement and editing of high-density cloud motion wind assessments and research satellite soundings have been necessary to improve the quality of data used in The Global Weather Experiment. Editing operations are conducted by a man-computer interactive data access system. Editing will focus on such inputs as non-US satellite data, NOAA operational sounding and wind data sets, wind data from the Indian Ocean satellite, dropwindsonde data, and tropical mesoscale wind data. Improved techniques for deriving cloud heights and higher resolution sounding in meteorologically active areas are principal parts of the data enhancement program.
In this report, we present the latest wind resource data specifically tailored for offshore regions in the United States. The data set, known as the 2023 National Offshore Wind data set (NOW-23), has been developed by the National Renewable Energy Laboratory (NREL) and its partners, and surpasses the previous resource data set, the Wind Integration National Dataset (WIND) Toolkit, which was released approximately ten years ago, in its offshore component. The WIND Toolkit has been widely utilized by stakeholders involved in wind resource assessments across the continental United States. However, with significant advancements in numerical weather prediction modeling over the past decade, the NOW-23 data set incorporates the latest research and development progress to provide stakeholders with an updated and cutting-edge resource for offshore wind analysis.
A series of data assimilation experiments is performed to assess the impact of Seasat A satellite scatterometer (SASS) wind data on Goddard Laboratory for Atmospheric Sciences (GLAS) model forecasts. The SASS data are dealiased as part of an objective analysis system utilizing a three-pass procedure. The impact of the SASS data is evaluated with and without temperature soundings from the NOAA 4 Vertical Temperature Profile Radiometer (VTPR) instrument in order to study possible redundancy between surface wind data and upper air temperature data. In the northern hemisphere the SASS data are generally found to have a negligible effect on the forecasts. In the southern hemisphere the forecast impact from SASS data is somewhat larger and primarily beneficial in the absence of VTPR data. However, the inclusion of VTPR data effectively eliminates the positive impact over Australia and South America. This indicates that SASS data can be beneficial for numerical weather prediction in regions with large data gaps, but in the presence of satellite soundings the usefulness of SASS data is significantly reduced.
The reported investigation has the objective to assess the potential impact on numerical weather prediction (NWP) of remotely sensed surface wind data. Other investigations conducted with similar objectives have not been satisfactory in connection with a use of procedures providing an unrealistic distribution of initial errors. In the current study, care has been taken to duplicate the actual distribution of information in the conventional observing system, thus shifting the emphasis from accuracy of the data to the data coverage. It is pointed out that this is an important consideration in assessing satellite observing systems since experience with sounder data has shown that improvements in forecasts due to satellite-derived information is due less to a general error reduction than to the ability to fill data-sparse regions. The reported study concentrates on the evaluation of the observing system simulation experimental design and on the assessment of the potential of remotely sensed marine surface wind data.