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Title: Model: Year-Long Reforecast, Forecast, Domain 01

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.

Authors:

  1. Pacific Northwest National Laboratory
Publication Date:
DOE Contract Number:  
67025F
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Atmosphere to Electrons (A2e) Data Archive and Portal
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Subject:
17 Wind Energy
OSTI Identifier:
1405129
DOI:
https://doi.org/10.21947/1405129

Citation Formats

Macduff, Matt. Model: Year-Long Reforecast, Forecast, Domain 01. United States: N. p., 2022. Web. doi:10.21947/1405129.
Macduff, Matt. Model: Year-Long Reforecast, Forecast, Domain 01. United States. doi:https://doi.org/10.21947/1405129
Macduff, Matt. 2022. "Model: Year-Long Reforecast, Forecast, Domain 01". United States. doi:https://doi.org/10.21947/1405129. https://www.osti.gov/servlets/purl/1405129. Pub date:Thu Oct 06 00:00:00 EDT 2022
@article{osti_1405129,
title = {Model: Year-Long Reforecast, Forecast, Domain 01},
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 = {10.21947/1405129},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Oct 06 00:00:00 EDT 2022},
month = {Thu Oct 06 00:00:00 EDT 2022}
}