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| Titel |
A rainfall calibration methodology for impact modelling based on spatial mapping |
| VerfasserIn |
F. Di Giuseppe, F. Molteni, A. M. Tompkins |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250059214
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| Zusammenfassung |
| A spatially-based precipitation bias correction is introduced that generalises existing
approaches. The method consists of projecting observed precipitation anomalies onto the
models modes of variability for a large set of model hindcasts to produce artificial mapped
empirical orthogonal functions, which can then be used to bias correct forecasts. Similar to
previous spatially based methods, the scheme can shift displaced anomalies, associated with
the West African monsoon progression for example, to their correct location, and by
construction produces a corrected field with a zero-mean bias with respect to the
observations.
The new method has the advantage that it only applies corrections to modes of
variability for which the model has proven skill, and does not rely on a one-to-one
direct correspondence between the observational and model modes, a restriction of
previous methods. By processing the precipitation fields in sequences of seven
pentad averages, it is also possible to including variability on shorter than monthly
timescales, important if the end product is to be used for end-user impacts focused
research.
The method is tested for various EOF-defined climate macro-regions within Africa and is
shown to reduce biases while also improving threat skill scores over a range of thresholds and
forecast lead-times. Results for the regions that contain Senegal, Ghana and Malawi will be
shown with special emphasis, as it is in these three countries that the corrected forecasts will
be used to drive a prototype malaria prediction system in the project Quantifying
the impact of Weather and climate on health in developing countries (QWeCI). |
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