Verification against perturbed analyses and observations
It has long been known that verification of a forecast against the sequence of analyses used to produce those forecasts can under-estimate the magnitude of forecast errors. Here we show that under certain conditions the verification of a short-range forecast against a perturbed analysis coming from...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-07-01
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| Series: | Nonlinear Processes in Geophysics |
| Online Access: | http://www.nonlin-processes-geophys.net/22/403/2015/npg-22-403-2015.pdf |
| Summary: | It has long been known that verification of a forecast against the sequence
of analyses used to produce those forecasts can under-estimate the magnitude
of forecast errors. Here we show that under certain conditions the
verification of a short-range forecast against a perturbed analysis coming
from an ensemble data assimilation scheme can give the same root-mean-square
error as verification against the truth. This means that a perturbed analysis
can be used as a reliable proxy for the truth. However, the conditions
required for this result to hold are rather restrictive: the analysis must be
optimal, the ensemble spread must be equal to the error in the mean, the
ensemble size must be large and the forecast being verified must be the
background forecast used in the data assimilation. Although these criteria
are unlikely to be met exactly it becomes clear that for most cases
verification against a perturbed analysis gives better results than
verification against an unperturbed analysis.
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We demonstrate the application of these results in a idealised model
framework and a numerical weather prediction context. In deriving this result
we recall that an optimal (Kalman) analysis is one for which the analysis
increments are uncorrelated with the analysis errors. |
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| ISSN: | 1023-5809 1607-7946 |