Regional assessment of sampling techniques for more efficient dynamical climate downscaling

AMS Citation:
Pinto, J. O., A. J. Monaghan, L. Delle Monache, E. Vanvyve, and D. L. Rife, 2014: Regional assessment of sampling techniques for more efficient dynamical climate downscaling. Journal of Climate, 27, 1524-1538, doi:10.1175/JCLI-D-13-00291.1.
Resource Type:article
Title:Regional assessment of sampling techniques for more efficient dynamical climate downscaling
Abstract: Dynamical downscaling is a computationally expensive method whereby finescale details of the atmosphere may be portrayed by running a limited area numerical weather prediction model (often called a regional climate model) nested within a coarse-resolution global reanalysis or global climate model output. The goal of this study is to assess using sampling techniques to dynamically downscale a small subset of days to approximate the statistical properties of the entire period of interest. Two sampling techniques are explored: one where days are randomly selected and another where representative days are chosen (or targeted) based on a set of selection criteria. The relative merit of using random sampling versus targeted random sampling is demonstrated using daily mean 2-m air temperature (T2M). The first two moments of dynamically downscaled T2M can be approximated within 0.3 K using just 5% of the population of available days during a 20-yr period. Targeted random sampling can reduce the mean absolute error of these estimates by as much as 30% locally. Estimation of the more extreme values of T2M is more uncertain and requires a larger sample size. The potential reduction in computational cost afforded by these sampling techniques could greatly benefit applications requiring high-resolution dynamically downscaled depictions of regional climate, including situations in which an ensemble of regional climate simulations is required to properly characterize uncertainty in the model physics assumptions, scenarios, and so on.
Subject(s):Climatology, Temperature, Statistical techniques, Numerical analysis/modeling
Peer Review:Refereed
Copyright Information:Copyright 2014 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 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/d7nk3fzz
Publisher's Version: 10.1175/JCLI-D-13-00291.1
  • James Pinto - NCAR/UCAR
  • Andrew Monaghan - NCAR/UCAR
  • Luca Delle Monache - NCAR/UCAR
  • Emilie Vanvyve - NCAR/UCAR
  • Daran Rife
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