Editorial Type:
Article
Article Type:
Research Article

Estimation of Predictability with a Newly Derived Index to Quantify Similarity among Ensemble Members

Tomohito J. Yamada Institute of Industrial Science, University of Tokyo, Tokyo, Japan

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Randal D. Koster NASA Goddard Space Flight Center, Greenbelt, Maryland

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Shinjiro Kanae Research Institute for Humanity and Nature, Kyoto, and Institute of Industrial Science, University of Tokyo, Tokyo, Japan

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Taikan Oki Institute of Industrial Science, University of Tokyo, Tokyo, Japan

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Print Publication:
01 Jul 2007
DOI:
https://doi.org/10.1175/MWR3418.1
Page(s):
2674–2687
Restricted access

Abstract

This study reveals the mathematical structure of a statistical index, Ω, that quantifies similarity among ensemble members in a weather forecast. Previous approaches for quantifying predictability estimate separately the phase and shape characteristics of a forecast ensemble. The diagnostic Ω, on the other hand, characterizes the similarity (across ensemble members) of both aspects together with a simple expression. The diagnostic Ω is thus more mathematically versatile than previous indices.

Corresponding author address: Tomohito J. Yamada, Be 607, Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan. Email: tomohito@iis.u-tokyo.ac.jp

Abstract

This study reveals the mathematical structure of a statistical index, Ω, that quantifies similarity among ensemble members in a weather forecast. Previous approaches for quantifying predictability estimate separately the phase and shape characteristics of a forecast ensemble. The diagnostic Ω, on the other hand, characterizes the similarity (across ensemble members) of both aspects together with a simple expression. The diagnostic Ω is thus more mathematically versatile than previous indices.

Corresponding author address: Tomohito J. Yamada, Be 607, Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan. Email: tomohito@iis.u-tokyo.ac.jp

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