SST prediction methodologies and verification considerations for dynamical mid-summer rainfall forecasts for South Africa

dc.contributor.authorLandman, Willem Adolf
dc.contributor.authorBeraki, Asmerom Fissehatsion
dc.contributor.authorDeWitt, David
dc.contributor.authorLotter, Daleen
dc.date.accessioned2015-06-03T07:17:19Z
dc.date.available2015-06-03T07:17:19Z
dc.date.issued2014-10
dc.description.abstractSeasonal-to-interannual hindcasts (re-forecasts) for December-January-February (DJF) produced at a 1-month lead-time by the ECHAM4.5 atmospheric general circulation model (AGCM) are verified after calibrating model output to DJF rainfall at 94 districts across South Africa. The AGCM is forced with SST forecasts produced by (i) statistically predicted SSTs, and (ii) predicted SSTs from a dynamically coupled ocean-atmosphere model. The latter SST forecasts in turn consist of an ensemble mean of SST forecasts, and also by considering the individual ensemble members of the SST forecasts. Probabilistic hindcasts produced for two separate category thresholds are verified over a 24-year test period from 1978/79 to 2001/02 by investigating the various AGCM configurations’ attributes of discrimination (whether the forecasts are discernibly different given different outcomes) and reliability (whether the confidence communicated in the forecasts is appropriate). Deterministic hindcast skill is additionally calculated through a range of correlation estimates between hindcast and observed DJF rainfall. For both probabilistic and deterministic verification the hindcasts produced by forcing the AGCM with dynamically predicted SSTs attain higher skill levels than the AGCM forced with statistical SSTs. Moreover, ensemble mean SST forecasts lead to improved skill over forecasts that considered an ensemble distribution of SST forecasts.en_ZA
dc.description.librarianam2015en_ZA
dc.description.sponsorshipPartly supported financially by the Water Research Commission (K5/2050) and by the National Research Foundation (NRF) of South Africa. The computing to produce the retrospective forecasts at IRI was provided by a US multi-agency computing grant through the Climate Simulation Laboratory (CSL) program (DeWitt, PI). Dave DeWitt’s time working on this project was paid for by a grant/cooperative agreement from the National Oceanic and Atmospheric Administration, NA100AR4310210.en_ZA
dc.description.urihttp://www.wrc.org.zaen_ZA
dc.identifier.citationLandman, WA, Beraki, A, DeWitt, D & Lotter, D 2014, 'SST prediction methodologies and verification considerations for dynamical mid-summer rainfall forecasts for South Africa', Water SA, vol. 40, no. 4, pp. 615-622.en_ZA
dc.identifier.issn0378-4738 (print)
dc.identifier.issn1816-7950 (online)
dc.identifier.other10.4314/wsa.v40i4.6
dc.identifier.urihttp://hdl.handle.net/2263/45376
dc.language.isoenen_ZA
dc.publisherWater Research Commissionen_ZA
dc.rightsWater Research Commissionen_ZA
dc.subjectSST predictionsen_ZA
dc.subjectSeasonal forecastingen_ZA
dc.subjectAtmospheric general circulation model (AGCM)en_ZA
dc.subjectSouth Africa (SA)en_ZA
dc.subjectSea-surface temperature (SST)en_ZA
dc.titleSST prediction methodologies and verification considerations for dynamical mid-summer rainfall forecasts for South Africaen_ZA
dc.typeArticleen_ZA

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