Computationally efficient spatial forecast verification using Baddeley's delta image metric

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.

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Author Gilleland, Eric
Lee, T.
Halley Gotway, John E.
Bullock, Randy G.
Brown, Barbara G.
Publisher UCAR/NCAR - Library
Publication Date 2008-05-01T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-17T15:58:20.346290
Metadata Record Identifier edu.ucar.opensky::articles:6348
Metadata Language eng; USA
Suggested Citation Gilleland, Eric, Lee, T., Halley Gotway, John E., Bullock, Randy G., Brown, Barbara G.. (2008). Computationally efficient spatial forecast verification using Baddeley's delta image metric. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d75q4w8s. Accessed 01 August 2025.

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