A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil
- 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 Sao 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 Sao Francisco it is only oneA 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 Sao 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 Sao Francisco it is only one month, longer-term prediction skill is just above the critical level.…
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| Author details: | Gerd BürgerORCiDGND |
|---|---|
| URN: | urn:nbn:de:kobv:517-opus4-523835 |
| DOI: | https://doi.org/10.25932/publishup-52383 |
| ISSN: | 1866-8372 |
| Title of parent work (German): | Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe |
| Publication series (Volume number): | Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (1214) |
| Publication type: | Postprint |
| Language: | English |
| Date of first publication: | 2019/07/20 |
| Publication year: | 2019 |
| Publishing institution: | Universität Potsdam |
| Release date: | 2021/11/19 |
| Tag: | climate drift; ensemble prediction; seamless prediction; seasonal forecast skill |
| Issue: | 726 |
| Number of pages: | 16 |
| Source: | Q J R Meteorol Soc. 2020; 146: 240– 253. https://doi.org/10.1002/qj.3670 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
| Peer review: | Referiert |
| Publishing method: | Open Access / Green Open-Access |
| License (German): |  CC-BY - Namensnennung 4.0 International |
| External remark: | Bibliographieeintrag der Originalveröffentlichung/Quelle |
