What is it about?
New spatial dissimilarity measures are introduced that succinctly summarize a high-resolution weather forecast's performance.
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Why is it important?
Comparing spatial fields in the realm of weather forecast verification is challenging. Most recently proposed methods are rather complicated to implement and/or interpret. Spatial dissimilarity measures are simple and easy to use and readily interpretable, but existing measures suffer from serious drawbacks in the operational verification setting. These new measures provide useful information without the typical drawbacks.
Perspectives
Having attempted to evaluate high-resolution weather forecast performance from many different spatial verification methods, I've found spatial dissimilarity measures (e.g., Hausdorff distance, Baddeley's delta metric, etc.) to be the easiest to deal with and interpret, particularly the mean-error distance (MED). However, each one has certain important drawbacks for weather forecast verification. I, therefore, developed these measures (especially Gbeta) to overcome these drawbacks.
Eric Gilleland
National Center for Atmospheric Research
Read the Original
This page is a summary of: Novel measures for summarizing high-resolution forecast performance, Advances in Statistical Climatology Meteorology and Oceanography, February 2021, Copernicus GmbH,
DOI: 10.5194/ascmo-7-13-2021.
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