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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AmeriFlux FLUXNET-1F US-xWR NEON Wind River Experimental Forest (WREF)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xWR NEON Wind River Experimental Forest (WREF). This is the FLUXNET version of the carbon flux data for the site US-xWR NEON Wind River Experimental Forest (WREF) produced by applying the standard ONEFlux (1F) software. Site Description - Located in an old growth Pacific Northwest forest west of the Cascade Range, Wind River Experimental Forest has a rich history of research and timber management since the early 1900s. The area is best known for its old-growth forests of Douglas-fir and western hemlock. Data collected at this site provides an interesting comparison to NEON’s ABBY site which is located Yacolt Burn State Forest, a relatively young growth industrial timber production forest.

Network), NEON (National Ecological Observatory [N↗
DOE OSTI2025

WREF tower profiling lidar

The tower profiling lidar #ZX300-1381 was deployed on top of the 74 m tall WREF NEON tower. The zip files contain high frequency data. The excel files are 10-min averages.

17 WIND ENERGY↗
DOE OSTI2025

WREF 3D Sonic Anemometer - Tower A / Raw Data

Tower A is one of four subcanopy towers that were installed in the WREF canopy across a transect distance of 200m. Tower A had two CSAT3-3D sonics mounted at a height of 2.7m a.g.l. One (A1) is facing 290 degrees; the second (A2) is facing 90 degrees. The towers were installed to observe flow just above the forest floor and to identify any advection flows in the canopy. These data were also part of a larger ICOS project looking at forest flow coupling/decoupling with tower sites in Italy and Czech Republic.

17 WIND ENERGY↗
DOE OSTI2025

TTP - WREF 3D Sonic Anemometer Tower D / Raw Data

Tower D is one of four subcanopy towers that were installed in the WREF canopy across a transect distance of 200m. Tower D had one CSAT3-3D sonic mounted at a height of 2.7 m a.g.l. The sonic is facing 290 degrees. The towers were installed to observe flow just above the forest floor and to identify any advection flows in the canopy. These data were also part of a larger ICOS project looking at forest flow coupling/decoupling with tower sites in Italy and Czech Republic.

17 WIND ENERGY↗
DOE OSTI2025

TTP - WREF 3D Sonic Anemometer Tower C / Raw Data

Tower C is one of four subcanopy towers that were installed in the WREF canopy across a transect distance of 200m. Tower C had one CSAT3-3D sonic mounted at a height of 2.7 m a.g.l. The sonic is facing 270 degrees. The towers were installed to observe flow just above the forest floor and to identify any advection flows in the canopy. These data were also part of a larger ICOS project looking at forest flow coupling/decoupling with tower sites in Italy and Czech Republic.

17 WIND ENERGY↗
DOE OSTI2025

TTP - WREF 3D Sonic Anemometer Tower B / Raw Data

Tower B is one of four subcanopy towers that were installed in the WREF canopy across a transect distance of 200m. Tower B had two CSAT3-3D sonics mounted at a height of 2.7 m a.g.l. One (B1) is facing 270 degrees; the second (B2) is facing 90 degrees. The towers were installed to observe flow just above the forest floor and to identify any advection flows in the canopy. These data were also part of a larger ICOS project looking at forest flow coupling/decoupling with tower sites in Italy and Czech Republic.

17 WIND ENERGY↗
DOE OSTI2025

WREF field profiling lidar

The field profiling lidar #ZX300-1945 was deployed on the ground in an open field 880 m south of the NEON tower and tower profiling lidar (ZX300-1381). The zip files contain high frequency data. The excel files are 10-min averages.

17 WIND ENERGY↗
DOE OSTI2025

WREF Halo Streamline scanning lidar / Derived data / RHI

This dataset contains streamwise velocity retrieved from the Range Height Indicator (RHI) scans done by the UC Davis scanning Lidar. The quality control of the radial wind speed is performed following the algorithm of Beck & Kuhn (2017). Instantaneous realizations characterized by wind misalignment greater than 60 degrees (based on the closest VAD scan in time) are discarded.

17 WIND ENERGY↗
DOE OSTI2025

WREF Halo Streamline scanning lidar / Derived data / High-frequency wind profiles

This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by the UC Davis scanning Lidar. 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 Goring & Nikora (2002).

17 WIND ENERGY↗
DOE OSTI2025

WREF Field Profiling Lidar-773 / Raw Data

The field profiling lidar #ZX300-773 was deployed on the ground in an open field 880 m south of the Wind River NEON tower in summer 2025. It was co-located with LLNL's Halo XR+ scanning lidar (z05) and Argonne's Waggle Node to capture mountain-valley flows during the warm summer season. The zip files contain high frequency data. The excel files are 10-min averages.

17 WIND ENERGY↗
DOE OSTI2012

WREF 2012: THE PAST AND FUTURE COST OF WIND ENERGY

The future of wind power will depend on the ability of the industry to continue to achieve cost reductions. To better understand the potential for cost reductions, this report provides a review of historical costs, evaluates near-term market trends, and summarizes the range of projected costs. It also notes potential sources of future cost reductions. Our findings indicate that steady cost reductions were interrupted between 2004 and 2010, but falling turbine prices and improved turbine performance are expected to drive a historically low LCOE for current installations. In addition, the majority of studies indicate continued cost reductions on the order of 20%-30% through 2030. Moreover, useful cost projections are likely to benefit from stronger consideration of the interactions between capital cost and performance as well as trends in the quality of the wind resource where projects are located, transmission, grid integration, and other cost variables.

17 WIND ENERGY↗
DOE OSTI2026

Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics

Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.

Energy - Wind↗
DOE OSTI2025

MLBS tower profiling lidar

The tower profiling lidar #ZX300-1381 was deployed on top of the 30 m tall MLBS NEON tower in an Appalachian forest. Prior to being deployed at MLBS, this lidar was on top of the tower at WREF in Washington. The zip files contain high frequency data. The Excel files are 10-min averages.

17 WIND ENERGY↗