Editorial Type:
Article
Article Type:
Research Article

Extreme Event Verification for Probabilistic Downscaling

Megan C. Kirchmeier-Young Department of Atmospheric and Oceanic Sciences, and Nelson Institute Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin

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David J. Lorenz Nelson Institute Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin

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Daniel J. Vimont Department of Atmospheric and Oceanic Sciences, and Nelson Institute Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin

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Print Publication:
01 Nov 2016
DOI:
https://doi.org/10.1175/JAMC-D-16-0043.1
Page(s):
2411–2430
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Abstract

Extreme events are important to many studying regional climate impacts but provide a challenge for many “deterministic” downscaling methodologies. The University of Wisconsin Probabilistic Downscaling (UWPD) dataset applies a “probabilistic” approach to downscaling that may be advantageous in a number of situations, including realistic representation of extreme events. The probabilistic approach to downscaling, however, presents some unique challenges for verification, especially when comparing a full probability density function with a single observed value for each day. Furthermore, because of the wide range of specific climatic information needed in climate impacts assessment, any single verification metric will be useful to only a limited set of practitioners. The intent of this study, then, is (i) to identify verification metrics appropriate for probabilistic downscaling of climate data; (ii) to apply, within the UWPD, those metrics to a suite of extreme event statistics that may be of use in climate impacts assessments; and (iii) in applying these metrics, to demonstrate the utility of a probabilistic approach to downscaling climate data, especially for representing extreme events.

Corresponding author address: Megan Kirchmeier-Young, University of Wisconsin–Madison, 1225 W. Dayton St., Madison, WI 53706. E-mail: kirchmeier@wisc.edu

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JAMC-D-16-0043.s1.

Abstract

Extreme events are important to many studying regional climate impacts but provide a challenge for many “deterministic” downscaling methodologies. The University of Wisconsin Probabilistic Downscaling (UWPD) dataset applies a “probabilistic” approach to downscaling that may be advantageous in a number of situations, including realistic representation of extreme events. The probabilistic approach to downscaling, however, presents some unique challenges for verification, especially when comparing a full probability density function with a single observed value for each day. Furthermore, because of the wide range of specific climatic information needed in climate impacts assessment, any single verification metric will be useful to only a limited set of practitioners. The intent of this study, then, is (i) to identify verification metrics appropriate for probabilistic downscaling of climate data; (ii) to apply, within the UWPD, those metrics to a suite of extreme event statistics that may be of use in climate impacts assessments; and (iii) in applying these metrics, to demonstrate the utility of a probabilistic approach to downscaling climate data, especially for representing extreme events.

Corresponding author address: Megan Kirchmeier-Young, University of Wisconsin–Madison, 1225 W. Dayton St., Madison, WI 53706. E-mail: kirchmeier@wisc.edu

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JAMC-D-16-0043.s1.

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