Editorial Type:
Article
Article Type:
Research Article

Computationally Efficient Spatial Forecast Verification Using Baddeley’s Delta Image Metric

Eric Gilleland Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Thomas C. M. Lee Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China, and Department of Statistics, Colorado State University, Fort Collins, Colorado

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John Halley Gotway Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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R. G. Bullock Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Barbara G. Brown Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Print Publication:
01 May 2008
DOI:
https://doi.org/10.1175/2007MWR2274.1
Page(s):
1747–1757
Restricted access

Abstract

An important focus of research in the forecast verification community is the development of alternative verification approaches for quantitative precipitation forecasts, as well as for other spatial forecasts. The need for information that is meaningful in an operational context and the importance of capturing the specific sources of forecast error at varying spatial scales are two primary motivating factors. In this paper, features of precipitation as identified by a convolution threshold technique are merged within fields and matched across fields in an automatic and computationally efficient manner using Baddeley’s metric for binary images.

The method is carried out on 100 test cases, and 4 representative cases are shown in detail. Results of merging and matching objects are generally positive in that they are consistent with how a subjective observer might merge and match features. The results further suggest that the Baddeley metric may be useful as a computationally efficient summary metric giving information about location, shape, and size differences of individual features, which could be employed for other spatial forecast verification methods.

Corresponding author address: Eric Gilleland, Research Applications Laboratory, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301. Email: ericg@ucar.edu

Abstract

An important focus of research in the forecast verification community is the development of alternative verification approaches for quantitative precipitation forecasts, as well as for other spatial forecasts. The need for information that is meaningful in an operational context and the importance of capturing the specific sources of forecast error at varying spatial scales are two primary motivating factors. In this paper, features of precipitation as identified by a convolution threshold technique are merged within fields and matched across fields in an automatic and computationally efficient manner using Baddeley’s metric for binary images.

The method is carried out on 100 test cases, and 4 representative cases are shown in detail. Results of merging and matching objects are generally positive in that they are consistent with how a subjective observer might merge and match features. The results further suggest that the Baddeley metric may be useful as a computationally efficient summary metric giving information about location, shape, and size differences of individual features, which could be employed for other spatial forecast verification methods.

Corresponding author address: Eric Gilleland, Research Applications Laboratory, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301. Email: ericg@ucar.edu

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