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Surface Meteorological Station UND 21m, (3) Sonics 3m 10m 21m, (2) T/RH 3m 21m, Physics site-6 Reviewed Data

**Overview** Surface wind, temperature, and turbulence measurements based on three-dimensional (3D) sonic anemometer and temperature/relative humidity (T/RH) data. **Data Details** T/RH events history is available in the attached Excel file: *PS01-TRH-3m *PS01-TRH-10m *PS02...

Otarola-Bustos, S. and Fernando, J. Wind Energy Technologies Office (WETO)

Feb 14, 2016
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wfip2.model/retro.hrrr.01.fcst.01

**Overview** 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 ph...

Macduff, M. Wind Energy Technologies Office (WETO)

Feb 09, 2016
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wfip2.model/retro.hrrr.01.fcst.02

**Overview** 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 ph...

Macduff, M. Wind Energy Technologies Office (WETO)

Feb 09, 2016
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wfip2.model/retro.hrrr.02.fcst.01

**Overview** 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 ph...

Macduff, M. Wind Energy Technologies Office (WETO)

Feb 09, 2016
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wfip2.model/retro.hrrr.02.fcst.02

**Overview** 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 ph...

Macduff, M. Wind Energy Technologies Office (WETO)

Feb 09, 2016
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wfip2.model/retro.rap.01.fcst.01

**Overview** 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 ph...

Macduff, M. Wind Energy Technologies Office (WETO)

Feb 09, 2016
1 Resources
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wfip2.model/retro.rap.02.fcst.01

**Overview** 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 ph...

Macduff, M. Wind Energy Technologies Office (WETO)

Feb 09, 2016
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RivGen Current Flow Measurements, Igiugig, AK

Measurements of flow velocity, distribution and turbulence conducted by University of Washington Applied Physics Laboratory, Igiugig, Alaska, Summer 2014

Thomson, J. et al Ocean Renewable Power Company

Nov 03, 2014
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Surface Meteorological Station PNNL 10m Sonic, Physics site-10 Derived Data

**Overview** This set provides estimation of the turbulent fluxes obtained with EddyPro v6.2.0 software (15-min averaging). **Data Details** Each meteorological station had one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (w...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Mar 31, 2016
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Surface Meteorological Station PNNL 10m Sonic, Physics site-10 Raw Data

**Overview** This dataset provides fast response wind and virtual sonic temperature data. **Data Details** Each meteorological (met) station has one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (within a degree) has been veri...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Mar 31, 2016
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Surface Meteorological Station PNNL 10m Sonic, Physics site-10 Reviewed Data

**Overview** This dataset provides fast response wind and virtual sonic temperature data. **Data Details** Each meteorological (met) station has one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (within a degree) has been veri...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Mar 31, 2016
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Surface Meteorological Station PNNL 10m Sonic, Physics site-4 Derived Data

**Overview** This set provides estimation of the turbulent fluxes obtained with EddyPro v6.2.0 software (15-min averaging). **Data Details** Each meteorological station had one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (w...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Mar 31, 2016
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Surface Meteorological Station PNNL 10m Sonic, Physics site-4 Raw Data

**Overview** This dataset provides fast response wind and virtual sonic temperature data. **Data Details** Each meteorological (met) station has one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (within a degree) has been veri...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Mar 31, 2016
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Surface Meteorological Station PNNL 10m Sonic, Physics site-4 Reviewed Data

**Overview** This dataset provides fast response wind and virtual sonic temperature data. **Data Details** Each meteorological (met) station has one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (within a degree) has been veri...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Mar 31, 2016
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Surface Meteorological Station PNNL 10m Sonic, Physics site-5 Derived Data

**Overview** This set provides estimation of the turbulent fluxes obtained with EddyPro v6.2.0 software (15-min averaging). **Data Details** Each meteorological station had one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (w...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Apr 01, 2016
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Surface Meteorological Station PNNL 10m Sonic, Physics site-5 Raw Data

**Overview** This dataset provides fast response wind and virtual sonic temperature data. **Data Details** Each meteorological (met) station has one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (within a degree) has been veri...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Mar 31, 2016
1 Resources
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Surface Meteorological Station PNNL 10m Sonic, Physics site-5 Reviewed Data

**Overview** This dataset provides fast response wind and virtual sonic temperature data. **Data Details** Each meteorological (met) station has one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (within a degree) has been veri...

Pekour, M. and Berg, L. Wind Energy Technologies Office (WETO)

Mar 31, 2016
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Surface Meteorological Station PNNL 3m Tower, Physics site-7 Raw Data

**Overview** In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon. **Data Details** A PNNL computer is used as the base station to download the meteorological data acquired by th...

Morris, V. Wind Energy Technologies Office (WETO)

Feb 14, 2016
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Surface Meteorological Station PNNL 3m Tower, Physics site-8 Raw Data

**Overview** In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon. **Data Details** A PNNL computer is used as the base station to download the meteorological data acquired by th...

Morris, V. Wind Energy Technologies Office (WETO)

Feb 14, 2016
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Surface Meteorological Station PNNL 3m Tower, Physics site-7 Reviewed Data

**Overview** In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon. **Data Details** A PNNL computer is used as the base station to download the meteorological data acquired by the...

Berg, L. Wind Energy Technologies Office (WETO)

Feb 17, 2016
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Surface Meteorological Station PNNL 3m Tower, Physics site-8 Reviewed Data

**Overview** In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon. **Data Details** A PNNL computer is used as the base station to download the meteorological data acquired by the...

Berg, L. Wind Energy Technologies Office (WETO)

Feb 14, 2016
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Admiralty Inlet Advanced Turbulence Measurements: Final Data and Code Archive

Data and code that is not already in a public location that is used in Kilcher, Thomson, Harding, and Nylund (2017) "Turbulence Measurements from Compliant Moorings Part II: Motion Correction" doi: 10.1175/JTECH-D-16-0213.1. The links point to Python source code used in the publi...

Kilcher, L. et al National Renewable Energy Laboratory

Feb 01, 2011
11 Resources
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Seismic Survey 2016 Metadata at San Emidio, Nevada

1301 Vertical Component seismic instruments were deployed at San Emidio Geothermal field in Nevada in December 2016. The first record starts at 2016-12-05T02:00:00.000000Z (UTC) and the last record ends at 2016-12-11T14:00:59.998000Z (UTC). Data are stored in individual files in o...

Lord, N. et al University of Wisconsin

Dec 05, 2016
10 Resources
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Northwest National Marine Renewable Energy Center, OR Final Technical Report & Appendices

In 2008, the US Department of Energy (DOE) Wind and Water Power Program issued a funding opportunity announcement to establish university-led National Marine Renewable Energy Centers. Oregon State University and the University of Washington combined their capabilities in wave and ...

Hellin, D. Northwest National Marine Renewable Energy Center

Jun 30, 2016
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GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...

Ahmmed, B. Stanford University

Apr 04, 2022
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