Search NASA⌕ Search

SEARCH · Search NASA

Results for “wind sampling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

AWAKEN Virtual Tower / Derived Data

Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.

17 WIND ENERGY↗

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

17 WIND ENERGY↗

S0 Uncrewed Aircraft System Measurement Characterization Field Campaign Report

The lower atmosphere contributes directly to the modulation of weather and climate. Understanding the importance of this part of the atmosphere, scientists have worked to improve the representation of the atmospheric boundary layer in numerical prediction tools. Such work depends upon information from observing systems, including remote sensors, weather balloons, and research aircraft. Recent years have seen significant advances in uncrewed aircraft systems (UAS) for atmospheric research. Those efforts have provided new perspectives on atmospheric and surface conditions, particularly from smaller UAS (sUAS) platforms. This field campaign included collection of data using several sUAS, and comparing the sUAS data to those from different observing facilities at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory in Oklahoma. Over the course of one week, the Black Swift Technologies and University of Nebraska teams conducted flights for the sole purpose of system evaluation and intercomparison. In addition, these teams conducted joint flight operations at the Marshall, Oklahoma Mesonet site alongside teams from the University of Colorado, University of Oklahoma, and Oklahoma State University to collect additional data for side-by-side comparison. Platforms operated by Black Swift Technologies and the University of Nebraska under this project included one fixed-wing and two rotary-wing platforms, as well as an instrumented surface vehicle. These include the Black Swift Technologies S0 UAS, the University of Nebraska M600 UAS, a University of Nebraska-operated Meteomatics Meteodrone UAS, and the University of Nebraska CoMeT (Combined Mesonet and Tracker) vehicle. Additional details on all of these platforms can be found in de Boer et al. (in prep). In total, 95 sUAS flights were conducted for a combined total of 18.6 flight hours. The S0 was operated both to the south and north of the SGP 60 m tower, while the M600 and Meteodrone were operated directly to the east of the tower. Weather conditions were generally good, with moderate winds. Flights were conducted both to follow the extra radiosondes that were launched as part of this campaign, as well as to conduct extended statistical sampling at tower instrument heights. The later flights also provided data for statistical platform intercomparison.

54 ENVIRONMENTAL SCIENCES↗

Microbarograph - ESRL Hi-Res Microbarograph, Troutdale - Raw Data

High-precision barometers (Paroscientific 6000-16B-IS) are combined with Nishiyama-Bedard Quad Disk pressure probes, measuring pressure (mb) at the surface, nominally 2 m above ground level. Data are sampled at 20 Hz for potential studies of turbulence. The sensors' high accuracy makes them useful for determining horizontal pressure gradients and their relation to wind ramp events, as well as the temporal variability of pressure associated with mountain wakes and waves. **Note different ASCII file formats for Goldendale (z04) and Walla Walla (z09) sites.**

17 WIND ENERGY↗

Microbarograph - ESRL Hi-Res Microbarograph, Bonneville - Raw Data

High-precision barometers (Paroscientific 6000-16B-IS) are combined with Nishiyama-Bedard Quad Disk pressure probes, measuring pressure (mb) at the surface, nominally 2 m above ground level. Data are sampled at 20 Hz for potential studies of turbulence. The sensors' high accuracy makes them useful for determining horizontal pressure gradients and their relation to wind ramp events, as well as the temporal variability of pressure associated with mountain wakes and waves. **Note different ASCII file formats for Goldendale (z04) and Walla Walla (z09) sites.**

17 WIND ENERGY↗

Microbarograph - ESRL Hi-Res Microbarograph, Goldendale - Raw Data

High-precision barometers (Paroscientific 6000-16B-IS) are combined with Nishiyama-Bedard Quad Disk pressure probes, measuring pressure (mb) at the surface, nominally 2 m above ground level. Data are sampled at 20 Hz for potential studies of turbulence. The sensors' high accuracy makes them useful for determining horizontal pressure gradients and their relation to wind ramp events, as well as the temporal variability of pressure associated with mountain wakes and waves. **Note different ASCII file formats for Goldendale (z04) and Walla Walla (z09) sites.**

17 WIND ENERGY↗

Microbarograph - ESRL Hi-Res Microbarograph, Hood River - Raw Data

High-precision barometers (Paroscientific 6000-16B-IS) are combined with Nishiyama-Bedard Quad Disk pressure probes, measuring pressure (mb) at the surface, nominally 2 m above ground level. Data are sampled at 20 Hz for potential studies of turbulence. The sensors' high accuracy makes them useful for determining horizontal pressure gradients and their relation to wind ramp events, as well as the temporal variability of pressure associated with mountain wakes and waves. **Note different ASCII file formats for Goldendale (z04) and Walla Walla (z09) sites.**

17 WIND ENERGY↗

Microbarograph - ESRL Hi-Res Microbarograph, Wasco Airport - Raw Data

High-precision barometers (Paroscientific 6000-16B-IS) are combined with Nishiyama-Bedard Quad Disk pressure probes, measuring pressure (mb) at the surface, nominally 2 m above ground level. Data are sampled at 20 Hz for potential studies of turbulence. The sensors' high accuracy makes them useful for determining horizontal pressure gradients and their relation to wind ramp events, as well as the temporal variability of pressure associated with mountain wakes and waves. **Note different ASCII file formats for Goldendale (z04) and Walla Walla (z09) sites.**

17 WIND ENERGY↗