Wind Farm Prospecting

Projects

Overview

Composite characteristics for strong low-level jet (LLJ) events for the Great Plains region, as represented by NASA's Modern Era Retrospective Reanalysis(MERRA). The mean winds at 300 m AGL are shown by the arrows, and the colors denote the wind speeds. Thick black line marks the location of the cross section shown in the right panel. LLJs are the primary driver of the abundant wind resource over the Great Plains. They are shallow streams of fast moving air, usually located between about 300 to 600 m above the ground, with maximum wind speeds of 10-20 m s-1.

Develop an economical, computationally efficient, and scientifically sound approach to creating dynamical downscaled wind analyses for renewable energy applications that can be used for any region of the world.

Project Motivation

The increasing global demand for the finite (and sometimes volatile) supply of fossil fuels has spurred large investments in renewable energy resources that are clean, reliable, and reduce the Nation's dependence on foreign oil. Wind and solar are the fastest growing sources of renewable electric energy—U. S. wind energy capacity increased by 45% from 2006 to 2009 amid the nearly 30% global increase, a trend that is expected to continue. Yet, renewable energy companies struggle to assess the reliability of wind-generated electricity at prospective sites, owing to wind's intermittent nature.

We aim to greatly improve upon current wind energy prospecting techniques by creating prototype high-resolution dynamically downscaled wind analyses with NCAR's Climate Four Dimensional Data Assimilation System (CFDDA), based upon the state-of-the-art WRF model. CFDDA will use key NASA Earth science datasets to downscale the NASA MERRA global reanalyses to scales appropriate for assessing the wind variability for prospective wind farms (1-3 km). Innovative sampling strategies will be developed to generate the equivalent of a 20-year reanalysis for assessing seasonal to inter-annual variability. The impact of NASA datasets on dynamically downscaled winds will be quantified using ClimoFDDA for two different climate regimes in the U. S.: (1) coastal California and (2) the central Great Plains—both environments having great potential for wind-power production.

By incorporating key NASA Earth science observations and research results into an improved and more efficient wind energy prospecting capability that is ultimately intended for use by the renewable energy industry, we help accelerate the Nation's independence from foreign oil, and the development of clean, reliable and sustainable energy resources.

Funding:

NASA logo

Technology

Climate Four Dimensional Data Assimilation (CFDDA) System

CFDDA is a dynamical downscaling system developed by NCAR to generate high-resolution climatographic analyses for any part of the world. At the heart of CFDDA is NCAR's Real Time Four Dimensional Data Assimilation system (RTFDDA; Liu et al. 2008), a mesoscale-model-based assimilation and forecasting system with versions based upon both the MM5 and its sequel, the Weather Research and Forecasting model (WRF; Skamarock et al. 2005). For the application described herein, we are using the WRF-based version of CFDDA (WRF Version 3.2.1) to create hourly regional analyses on a ~1-km horizontal grid, with 57 vertical levels. NASA MERRA analyses are used for initial and lateral boundary conditions, and serve as a starting point for the dynamical downscaling.

Throughout the integrations, CFDDA continuously assimilates standard surface and upper-air observations using the Newtonian relaxation technique. Observation data are obtained from the Advanced Data Processing datasets, available from the NCAR Research Data Archive. The global dataset contains both hourly and 6-hourly data reports collected by NCEP. The surface dataset includes mostly land reports, but a few ship observations also exist. The upper-air data are primarily standard rawinsonde measurements. The CFDDA solution is also nudged toward the driving NASA Modern Era Retrospective Reanalysis (MERRA) at the upper levels to preserve the large-scale state in CFDDA, and to ensure mass conservation. A time-invariant vertical grid nudging zone is used, from the model top down to about 3.0 km AGL (above the average height of the PBL). The underlying principle is for the CFDDA large-scale solution to closely follow that of the MERRA, while not impeding the ability of the assimilated observations and CFDDA to produce small-scale features, especially within the PBL.

WRF Model

The WRF Model is a next-generation mesocale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of spatial scales ranging from meters to thousands of kilometers.

The WRF Model has been developed through a collaboration between the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (the National Centers for Environmental Prediction (NCEP) and the Earth System Research Laboratory (ESRL) Global Systems Division (GSD), the U.S. Air Force Weather Agency (AFWA), the Naval Research Laboratory, Oklahoma University, and the Federal Aviation Administration (FAA).

Participants

Sponsor

National Aeronautics and Space Administration (NASA)
Funding is provided through NASA Research Opportunities in Space and Environmental Sciences (ROSES) 2008 Grant, under the Applied Sciences Program: Decision Support topic.

Collaborators

Vbar, LLC
Serving the international wind energy industry.

Riso DTU
National Laboratory for Sustainable Energy at the Technical University of Denmark - DTU

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