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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.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Calibrated Radar Wind Profiler (RWP) Moments

The SGP Central Facility (C1) radar wind profiler (RWP) was calibrated using nearby surface disdrometer observations. Between 2011 and 2019, the SGP C1 RWP operated in two modes. The vertically pointing mode (named the precipitation mode) transmitted a short and long pulse length to have two different range resolutions and the beam-swinging mode (named the wind mode) transmitted one pulse length into three different beam directions. The precipitation-mode observations were available and calibrated from April 2011 through mid-August 2019. The wind-mode observations were available and calibrated between April 2014 and March 2019. The RWP spectra were processed to account for Nyquist velocity aliasing and coherent integration filtering effects before calculating the spectrum moments. During intense precipitation events, the calculated signal-to-noise ratio (SNR) is biased low due to signal power being distributed across the velocity spectrum such that some signal power is erroneously included in the noise level estimate, causing the noise level to be biased high. To correct for the low SNR bias, a new noise level is estimated using observations without precipitation and the SNR is increased accordingly. The adjusted SNR was converted to radar reflectivity factor and then calibrated against a nearby surface disdrometer. The calibration methodology is fully described in: Williams, CR, J Barrio, PE Johnston, P Muradyan, and S Giangrande. 2023. “Calibrating radar wind profiler reflectivity factor using surface disdrometer observations.” Atmospheric Measurement Techniques, https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1405/

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating wind profiles in a numerical weather prediction model with Doppler lidar

We use Doppler lidar wind profiles from six locations around the globe to evaluate the wind profile forecasts in the boundary layer generated by the operational global Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts (ECMWF). The six locations selected cover a variety of surfaces with different characteristics (rural, marine, mountainous urban, and coastal urban). We first validated the Doppler lidar observations at four locations by comparison with co-located radiosonde profiles to ensure that the Doppler lidar observations were of sufficient quality. The two observation types agree well, with the mean absolute error (MAE) in wind speed almost always less than 1 m s –1 . Large deviations in the wind direction were usually only seen for low wind speeds and are due to the wind direction uncertainty increasing rapidly as the wind speed tends to zero. Time–height composites of the wind evaluation with 1 h resolution were generated, and evaluation of the model winds showed that the IFS model performs best over marine and coastal locations, where the mean absolute wind vector error was usually less than 3 m s –1 at all heights within the boundary layer. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. For example, in Granada, which is near a high mountain range, the IFS model failed to capture a commonly occurring mountain breeze, which is highly dependent on the sub-grid-size terrain features that are not resolved by the model. The uncertainty in the wind forecasts increased with forecast lead time, but no increase in the bias was seen. At one location, we conditionally performed the wind evaluation based on the presence or absence of a low-level jet diagnosed from the Doppler lidar observations. The model was able to reproduce the presence of the low-level jet, but the wind speed maximum was about 2 m s –1 lower than observed. This is attributed to the effective vertical resolution of the model being too coarse to create the strong gradients in wind speed observed. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

NANT Site - NOAA Radar Wind Profiler / Raw Data

This dataset contains data from a NOAA Radar Wind Profiler located on Nantucket island. Data can be found at https://psl.noaa.gov/data/obs/datadisplay/ with the following filters: Active Sites: nte Instrument: 915 MHz Wind Profiler Product: Radar 915MHz Sub-Hourly Weber Wuertz Wind

17 WIND ENERGY↗
DOE OSTI2025

Radar Wind Profiler / Raw Data

This dataset contains data from a NOAA Radar Wind Profiler located on Block island. Data can be found at https://psl.noaa.gov/data/obs/datadisplay/ with the following filters: Active Sites: bid Instrument: 915 MHz Wind Profiler Product: Radar 915MHz Sub-Hourly Weber Wuertz Wind

17 WIND ENERGY↗
DOE OSTI2020

Radar Wind Profiler (RWP) and Radio Acoustic Sounding System (RASS) Instrument Handbook

The radar wind profiler (RWP) is an active remote-sensing instrument that can routinely, and virtually unattended, observe wind and turbulence in the troposphere through scattering from clear-air irregularities of the atmospheric refractive index (Gage and Balsley 1978). The principle of Doppler radars in general is based on sending electromagnetic pulses in the vertical and several tilted directions (vertical and oblique beams) and measuring the signal that is scattered back by atmospheric turbulence at all heights and received at the antenna. The RWPs provides measurements of backscattered signal strength and wind profiles nominally between 0.1 km and 6 km. The RWP operation assumes that the wind field is homogeneous over the spatial separation of the antenna beams, which is a safe assumption under stable atmospheric conditions. However, the wind may be different between the beams due to significant changes in the wind field, resulting in erroneous wind calculations. Therefore, the radial measurements are averaged over a sufficient time period to validate the assumption of homogeneity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

MLBS Site - Halo Scanning Lidar High-frequency Wind Profile / Derived data

This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by a Streamline XR Doppler Lidar operated by the University of Virginia and deployed at the MLBS site. The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Foken et al. (2004).

17 WIND ENERGY↗
DOE OSTI2026

Tree Tops Site - Halo Streamline Scanning Lidar High-Frequency Wind Profile / Derived data

This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by a Streamline XR Doppler Lidar operated by Lawrence Livermore National Laboratory and deployed at the Tree Tops site (1.5 km South-West of MLBS site). The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Foken et al. (2004).

17 WIND ENERGY↗
DOE OSTI2025

BLOC Site - Radar Wind Profiler / Derived Data Reformatted

A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and combined to derive the horizontal wind field over the radar. These data are typically sampled and averaged hourly or sub-hourly (15-min) and usually have 60-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz system. Both a high-resolution, lower height coverage mode and a low-resolution, higher height coverage mode are used to collect the data.

17 WIND ENERGY↗
DOE OSTI2025

NANT Site - Radar Wind Profiler / Derived Data Reformatted

A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and combined to derive the horizontal wind field over the radar. These data are typically sampled and averaged hourly or sub-hourly (15-min) and usually have 60-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz system. Both a high-resolution, lower height coverage mode and a low-resolution, higher height coverage mode are used to collect the data.

17 WIND ENERGY↗