Identification

Title

Assessing the ensemble spread-error relationship

Abstract

The potential ability of an ensemble prediction system (EPS) to represent its own varying forecast error provides strong motivation to produce an EPS over a less expensive deterministic forecast. Traditionally, this ability has been assessed by correlating the realized forecast error with the ensemble's dispersion. This paper revisits the limitations of the skill–spread correlation, but uses aspects of the correlation to introduce two metrics to assess an EPS's capacity to provide a reliable likelihood of its own error. Using a perfect EPS, skill–spread correlation is shown to be limited by its dependence on how “skill” and “spread” are defined and, perhaps most fatally, by its inability to distill the skill–spread reliability from the stability properties of the physical system being modeled. Building from this, it is argued there are two aspects of an ensemble's dispersion that should be assessed. First, is there enough variability in the dispersion to justify the expense of the EPS? The factor that controls the theoretical upper limit of the spread–error correlation can be useful in diagnosing this. Second, does the variable dispersion of an ensemble relate to a variable expectation of the forecast error? Representing the spread–error correlation in relation to its theoretical limit can provide a simple diagnostic of this attribute. A context for these concepts is provided by assessing two operational ensembles: western U.S. temperature forecasts and Brahmaputra River flow before and after postprocessing. It is shown that “skill-spread” reliability can be improved by postprocessing to that of a perfect EPS, but at the cost of the potential information content of the EPS's variable dispersion.

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document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7r212bb

codeSpace

Dataset language

eng

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Classification of spatial data and services

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geoscientificInformation

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title

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reference date

date type

publication

effective date

2016-01-01T00:00:00Z

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date type

publication

effective date

2014-03-01T00:00:00Z

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Use constraints

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 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.

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None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

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