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A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil

Bürger, GerdORCIDiD
DOI: https://doi.org/10.1002/qj.3670
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9470
Bürger, Gerd, 2019: A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil. In: Quarterly Journal of the Royal Meteorological Society, Band 146, 726: 240 - 253, DOI: 10.1002/qj.3670.
 
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  • Abstract
A digital filter is introduced which treats the problem of predictability versus time averaging in a continuous, seamless manner. This seamless filter (SF) is characterized by a unique smoothing rule that determines the strength of smoothing in dependence on lead time. The rule needs to be specified beforehand, either by expert knowledge or by user demand. As a result, skill curves are obtained that allow a predictability assessment across a whole range of time-scales, from daily to seasonal, in a uniform manner. The SF is applied to downscaled SEAS5 ensemble forecasts for two focus regions in or near the tropical belt, the river basins of the Karun in Iran and the São Francisco in Brazil. Both are characterized by strong seasonality and semi-aridity, so that predictability across various time-scales is in high demand. Among other things, it is found that from the start of the water year (autumn), areal precipitation is predictable with good skill for the Karun basin two and a half months ahead; for the São Francisco it is only one month, longer-term prediction skill is just above the critical level.
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  • Geophysik, Extraterrestische Forschung [826]
Subjects:
climate drift
ensemble prediction
seamless prediction
seasonal forecast skill
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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