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Title: wfip2.model/retro.rap.02.fcst.01 (Model: 10-Day Retrospective)

Abstract

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 physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.

Creator(s)/Author(s):
Publication Date:
DOE Contract Number:  
67025F
Product Type:
Dataset
Research Org.:
Atmosphere to Electrons (A2e) Data Archive and Portal, Pacific Northwest National Laboratory; PNNL
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Subject:
17 Wind Energy
OSTI Identifier:
1405135
DOI:
10.21947/1405135

Citation Formats

Macduff, Matt. wfip2.model/retro.rap.02.fcst.01 (Model: 10-Day Retrospective). United States: N. p., 2019. Web. doi:10.21947/1405135.
Macduff, Matt. wfip2.model/retro.rap.02.fcst.01 (Model: 10-Day Retrospective). United States. doi:10.21947/1405135.
Macduff, Matt. 2019. "wfip2.model/retro.rap.02.fcst.01 (Model: 10-Day Retrospective)". United States. doi:10.21947/1405135. https://www.osti.gov/servlets/purl/1405135. Pub date:Wed Dec 11 00:00:00 EST 2019
@article{osti_1405135,
title = {wfip2.model/retro.rap.02.fcst.01 (Model: 10-Day Retrospective)},
author = {Macduff, Matt},
abstractNote = {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 physics associated with the wind flow in and around the wind plant across a range of temporal and spatial scales, which will be gained through WFIP2’s observational field study and analysis.},
doi = {10.21947/1405135},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {12}
}

Dataset:

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