Selecting representative days for more efficient dynamical climate downscaling: Application to wind energy

AMS Citation:
Rife, D. L., E. Vanvyve, J. Pinto, A. J. Monaghan, C. A. Davis, and G. S. Poulos, 2013: Selecting representative days for more efficient dynamical climate downscaling: Application to wind energy. Journal of Applied Meteorology and Climatology, 52, 47-63, doi:10.1175/JAMC-D-12-016.1.
Date:2013-01-01
Resource Type:article
Title:Selecting representative days for more efficient dynamical climate downscaling: Application to wind energy
Abstract: This paper describes a new computationally efficient and statistically robust sampling method for generating dynamically downscaled climatologies. It is based on a Monte Carlo method coupled with stratified sampling. A small yet representative set of "case days" is selected with guidance from a large-scale reanalysis. When downscaled, the sample closely approximates the long-term meteorological record at a location, in terms of the probability density function. The method is demonstrated for the creation of wind maps to help determine the suitability of potential sites for wind energy farms. Turbine hub-height measurements at five U.S. and European tall tower sites are used as a proxy for regional climate model (RCM) downscaled winds to validate the technique. The tower-measured winds provide an independent test of the technique, since RCM-based downscaled winds exhibit an inherent dependence upon the large-scale reanalysis fields from which the case days are sampled; these same reanalysis fields would provide the boundary conditions to the RCM. The new sampling method is compared with the current approach widely used within the wind energy industry for creating wind resource maps, which is to randomly select 365 case days for downscaling, with each day in the calendar year being represented. The new method provides a more accurate and repeatable estimate of the long-term record of winds at each tower location. Additionally, the new method can closely approximate the accuracy of the current (365 day) industry approach using only a 180-day sample, which may render climate downscaling more tractable for those with limited computing resources.
Peer Review:Refereed
Copyright Information:Copyright 2013 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be "fair use" under Section 107 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Law (17 USC, as revised by P.L. 94-553) does not require the Society's permission. Republication, systematic reproduction, posting in electronic form on servers, or other uses of this material, except as exempted by the above statements, requires written permission or license from the AMS. Additional details are provided in the AMS Copyright Policies, available from the AMS at 617-227-2425 or amspubs@ametsoc.org. Permission to place a copy of this work on this server has been provided by the AMS. The AMS does not guarantee that the copy provided here is an accurate copy of the published work.
OpenSky citable URL: ark:/85065/d7jd4xm0
Publisher's Version: 10.1175/JAMC-D-12-016.1
Author(s):
  • Daran Rife - NCAR/UCAR
  • Emilie Vanvyve - NCAR/UCAR
  • James Pinto - NCAR/UCAR
  • Andrew Monaghan - NCAR/UCAR
  • Christopher Davis - NCAR/UCAR
  • G. Poulos
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