Abstract
Accounting for representative mismatches helps reduce ambiguity when interpreting verification results. For example, there can be a difference in the spatial extent between the physical phenomenon and its data instance. In this work we use lightning to illustrate this. Lightning is near instantaneous in time, highly localised, spatially sparse yet three-dimensional, and often processed into a two-dimensional point location with a time stamp.
Numerical Weather Prediction (NWP) forecasts of lightning flash origin density are typically derived from parameterised sub-grid-scale processes. The minimum areal extent of any forecast quantity is dictated by the model grid length, such that smaller scale phenomena are inflated (and thus misrepresented) in terms of scale. The model representation of reality is often too large, and the observation instance to compare to, is too small.
Gaussian kernel dressing is one way of increasing the footprint of highly localised observations to help mitigate against the representativeness mismatch when using such observations for evaluating model output, even in gridded form. When adjusting the observation “footprint” the observation characteristics must be examined and used as a guide.
The Coverage-Distance-Intensity (CDI) method is used to examine what impact spatial representativeness mismatches have on the interpretation of spatial verification method output. We found a 38% change in the median coverage component between using dressed and undressed observation fields, demonstrating that the spatial method can quantify (and is affected by) representativeness mismatches. Whilst the study is lightning based, the study attempts to synthesise some general recommendations for mitigating against representativeness error.
© 2025 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
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