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| Titel |
Wavelet-based spatial comparison technique for analysing and evaluating two-dimensional geophysical model fields |
| VerfasserIn |
S. Saux Picart, M. Butenschön, J. D. Shutler |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 5, no. 1 ; Nr. 5, no. 1 (2012-02-13), S.223-230 |
| Datensatznummer |
250002301
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| Publikation (Nr.) |
 copernicus.org/gmd-5-223-2012.pdf |
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| Zusammenfassung |
| Complex numerical models of the Earth's environment, based around 3-D or 4-D
time and space domains are routinely used for applications including climate
predictions, weather forecasts, fishery management and environmental impact
assessments. Quantitatively assessing the ability of these models to
accurately reproduce geographical patterns at a range of spatial and temporal
scales has always been a difficult problem to address. However, this is
crucial if we are to rely on these models for decision making. Satellite data
are potentially the only observational dataset able to cover the large
spatial domains analysed by many types of geophysical models. Consequently
optical wavelength satellite data is beginning to be used to evaluate model
hindcast fields of terrestrial and marine environments. However, these
satellite data invariably contain regions of occluded or missing data due to
clouds, further complicating or impacting on any comparisons with the model.
This work builds on a published methodology, that evaluates precipitation
forecast using radar observations based on predefined absolute thresholds. It
allows model skill to be evaluated at a range of spatial scales and rain
intensities. Here we extend the original method to allow its generic
application to a range of continuous and discontinuous geophysical data
fields, and therefore allowing its use with optical satellite data. This is
achieved through two major improvements to the original method: (i) all
thresholds are determined based on the statistical distribution of the input
data, so no a priori knowledge about the model fields being analysed is
required and (ii) occluded data can be analysed without impacting on the
metric results. The method can be used to assess a model's ability to
simulate geographical patterns over a range of spatial scales. We illustrate
how the method provides a compact and concise way of visualising the degree
of agreement between spatial features in two datasets. The application of the
new method, its handling of bias and occlusion and the advantages of the
novel method are demonstrated through the analysis of model fields from a
marine ecosystem model. |
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