Stoelinga, Mark, 3TIER, Inc., Matthew Hendrickson,
3TIER, Inc., and Pascal Storck, 3TIER, Inc.
Meteorological
reanalysis datasets are increasingly being used, in conjunction with mesoscale
models such as WRF, to provide a long-term (several decades) view of the
potential wind energy resource at a site that is under consideration for wind
development. These multi-decadal 3D gridded global datasets of basic
meteorological variables can be downscaled with WRF to a few km spatial
resolution, to provide long-term, high spatial resolution fields of hub-height
wind speed at any prospective wind farm on the globe. This presentation will give an overview of the
considerations and strategies used in downscaling reanalysis data sets for wind
energy resource assessment. In
addition, in recent years, several leading operational weather forecast centers
have developed new reanalysis datasets that offer potential improvement over
the work-horse NCEP/ NCAR Reanalysis Project (NNRP) dataset that was developed
in the mid-1990s, and has been used widely in the renewable energy industry.
Improvements in new reanalysis datasets include better representation of
physical processes and finer grid resolution in the underlying global model, as
well as more sophisticated data assimilation techniques. However, it has yet to be confirmed
whether the improvements in the production methods of the new reanalysis
datasets translate into improved performance for use in wind resource
assessment studies. This study
will directly address this question, by comparing the performance of three
newer reanalysis datasets to that of NNRP: the Climate Forecast System Reanalysis (CFSR) developed at
NCEP; the Modern-Era Retrospective analysis for Research and Applications
(MERRA) developed at the National Aeronautics and Space Administration (NASA);
and the ECMWF Interim Reanalysis (ERA-Interim) developed at the European Centre
for Medium Range Weather Forecasts (ECMWF). These comparisons will examine the
performance of hub-height wind speeds pulled directly from the raw datasets
themselves, as well as the performances of downscaled versions of the datasets
using WRF.