Conference programme

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Wednesday, 18 November 2015
11:30 - 13:00 Atmospheric flow over terrain
Resource assessment  
Onshore     


Room: Montparnasse

Complex terrain poses a challenge for onshore wind farms in terms of uncertainty on the energy production and difficult to predict turbulence, shear and gusts. To solve these challenges researcher employ numerical modeling, physical modeling and conduct field experiments.

Session scope:

onshore wind energy only

Learning objectives

The delegates with be able to explain:

  • The importance of field experiments for model validation
  • The strength and limitation of physical flow modeling
  • The possibilities and drawbacks of numerical modelling
  • The possibilities with a completely new kind of wind modeling facility
Lead Session Chair:
Jakob Mann, Professor, DTU Wind Energy / President, EAWE, Denmark
James McCaa Vaisala, Inc., United States
Co-authors:
James McCaa (1) F
(1) Vaisala, Inc., Seattle, United States

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

James McCaa, Ph.D., Director of Advanced Applications for Vaisala, has extensive experience in
developing and applying numerical models of mesoscale, microscale, and turbine-level winds. His
specialties include scalable computing and the parameterization of boundary layer processes. Prior to
joining Vaisala, he held positions at the Applied Physics Laboratory of the University of Washington, and
the National Center for Atmospheric Research. At Vaisala he has focused his efforts at practical
knowledge of how to operate such models to yield the most meaningful and accurate forecasts of wind
energy. His work at Vaisala has also encompassed combined wind and solar engagements for the
purposes of integration and policy planning. Dr. McCaa holds a PhD in Atmospheric Science from the
University of Washington.

Abstract

An Overview of the Wind Forecasting Improvement Project in Complex Terrain (WFIP 2)

Introduction

The Wind Forecasting Improvement Project in Complex Terrain (WFIP 2) is a multi-year, multi-million dollar project sponsored by the U.S. Department of Energy (DOE) and the U.S. National Oceanic and Atmospheric Administration (NOAA), and being conducted in conjunction with four DOE National Laboratories and Vaisala, Inc.

Approach

This talk will inform EWEC attendees of a significant multi-year research effort from the US government attempting to improve the performance of wind energy forecasts in complex terrain.


Main body of abstract

The Wind Forecasting Improvement Project in Complex Terrain (WFIP 2) will focus on improving the physical understanding of atmospheric processes in complex terrain impacting wind industry forecasts and incorporating the new understanding into the foundational weather forecasting models. The integrated WFIP 2 team will conduct a field campaign of unprecedented scale to gather data from all four seasons in the Columbia Gorge of Washington and Oregon. Following the field campaign, data analysis and model improvement work will be conducted to develop new or improved Weather Research and Forecasting (WRF) PBL model schemes or atmospheric modeling theories that better represent physical processes and increase the accuracy of wind energy forecasts in the 0 to 15 hour horizon, as well as positively impacting the day ahead forecast. Decision support tools will be developed which could include probabilistic forecast information, uncertainty quantification and forecast reliability for system operations.

Conclusion

This is an information talk that will provide the audience with an overview of a major US effort to improve the quality of wind energy forecasts through enhancement of foundational numerical weather prediction models. In addition, links to data more information from the project will be provided.


Learning objectives
This is an information talk that will provide the audience with an overview of a major US effort to improve the quality of wind energy forecasts through enhancement of foundational numerical weather prediction models. In addition, links to data more information from the project will be provided.