Visualization of NCAR's wind forecast improvement project 2

Scott Pearse; Pedro Jimenez Munoz; Branko Kosovic

doi:10.1145/3093338.3104179

Visualization of NCAR's wind forecast improvement project 2

Scott Pearse, Pedro Jimenez Munoz, Branko Kosovic

National Center for Atmospheric Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

At the end of 2016, the state of Oregon had an installed wind generating power capacity of over 3 Gigawatts. Wind power, while clean and renewable, requires constant load balancing due to variable weather conditions. These conditions must be met with state of the art forecasting methods in order to maximize load balancing efficiency. New techniques in Numerical Weather Prediction (NWP) have been developed to better optimize load balancing. Visualizations of these new NWP simulations show how winds behave on the leeward side of Mt. Hood. These winds generate a wake that affects Oregon wind farms; causing swings in power that require dynamic load balancing.1

Original language	English
Title of host publication	PEARC 2017 - Practice and Experience in Advanced Research Computing 2017
Subtitle of host publication	Sustainability, Success and Impact
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450352727
DOIs	https://doi.org/10.1145/3093338.3104179
State	Published - Jul 9 2017
Event	2017 Practice and Experience in Advanced Research Computing, PEARC 2017 - New Orleans, United States Duration: Jul 9 2017 → Jul 13 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F128771

Conference

Conference	2017 Practice and Experience in Advanced Research Computing, PEARC 2017
Country/Territory	United States
City	New Orleans
Period	07/9/17 → 07/13/17

Keywords

Load Balancing
Mt. Hood
Numerical Weather Prediction
Planetary Boundary Layer
Weather Research and Forecasting
Wind Power

Access to Document

10.1145/3093338.3104179

Cite this

Pearse, S., Munoz, P. J., & Kosovic, B. (2017). Visualization of NCAR's wind forecast improvement project 2. In PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact Article a77 (ACM International Conference Proceeding Series; Vol. Part F128771). Association for Computing Machinery. https://doi.org/10.1145/3093338.3104179

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title = "Visualization of NCAR's wind forecast improvement project 2",

abstract = "At the end of 2016, the state of Oregon had an installed wind generating power capacity of over 3 Gigawatts. Wind power, while clean and renewable, requires constant load balancing due to variable weather conditions. These conditions must be met with state of the art forecasting methods in order to maximize load balancing efficiency. New techniques in Numerical Weather Prediction (NWP) have been developed to better optimize load balancing. Visualizations of these new NWP simulations show how winds behave on the leeward side of Mt. Hood. These winds generate a wake that affects Oregon wind farms; causing swings in power that require dynamic load balancing.1",

keywords = "Load Balancing, Mt. Hood, Numerical Weather Prediction, Planetary Boundary Layer, Weather Research and Forecasting, Wind Power",

author = "Scott Pearse and Munoz, \{Pedro Jimenez\} and Branko Kosovic",

note = "Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 2017 Practice and Experience in Advanced Research Computing, PEARC 2017 ; Conference date: 09-07-2017 Through 13-07-2017",

year = "2017",

month = jul,

day = "9",

doi = "10.1145/3093338.3104179",

language = "English",

series = "ACM International Conference Proceeding Series",

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Pearse, S , Munoz, PJ & Kosovic, B 2017, Visualization of NCAR's wind forecast improvement project 2. in PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact., a77, ACM International Conference Proceeding Series, vol. Part F128771, Association for Computing Machinery, 2017 Practice and Experience in Advanced Research Computing, PEARC 2017, New Orleans, United States, 07/9/17. https://doi.org/10.1145/3093338.3104179

Visualization of NCAR's wind forecast improvement project 2. / Pearse, Scott ; Munoz, Pedro Jimenez; Kosovic, Branko.
PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact. Association for Computing Machinery, 2017. a77 (ACM International Conference Proceeding Series; Vol. Part F128771).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - At the end of 2016, the state of Oregon had an installed wind generating power capacity of over 3 Gigawatts. Wind power, while clean and renewable, requires constant load balancing due to variable weather conditions. These conditions must be met with state of the art forecasting methods in order to maximize load balancing efficiency. New techniques in Numerical Weather Prediction (NWP) have been developed to better optimize load balancing. Visualizations of these new NWP simulations show how winds behave on the leeward side of Mt. Hood. These winds generate a wake that affects Oregon wind farms; causing swings in power that require dynamic load balancing.1

AB - At the end of 2016, the state of Oregon had an installed wind generating power capacity of over 3 Gigawatts. Wind power, while clean and renewable, requires constant load balancing due to variable weather conditions. These conditions must be met with state of the art forecasting methods in order to maximize load balancing efficiency. New techniques in Numerical Weather Prediction (NWP) have been developed to better optimize load balancing. Visualizations of these new NWP simulations show how winds behave on the leeward side of Mt. Hood. These winds generate a wake that affects Oregon wind farms; causing swings in power that require dynamic load balancing.1

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KW - Mt. Hood

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KW - Planetary Boundary Layer

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M3 - Conference contribution

AN - SCOPUS:85025812557

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ER -

Visualization of NCAR's wind forecast improvement project 2

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