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

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dc.contributor.author Landman, W.A. (Willem Adolf), 1964-
dc.contributor.author Beraki, Asmerom Fissehatsion
dc.contributor.author DeWitt, David
dc.contributor.author Lotter, Daleen
dc.date.accessioned 2015-06-03T07:17:19Z
dc.date.available 2015-06-03T07:17:19Z
dc.date.issued 2014-10
dc.description.abstract Seasonal-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.librarian am2015 en_ZA
dc.description.sponsorship Partly 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.uri http://www.wrc.org.za en_ZA
dc.identifier.citation Landman, 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.issn 0378-4738 (print)
dc.identifier.issn 1816-7950 (online)
dc.identifier.other 10.4314/wsa.v40i4.6
dc.identifier.uri http://hdl.handle.net/2263/45376
dc.language.iso en en_ZA
dc.publisher Water Research Commission en_ZA
dc.rights Water Research Commission en_ZA
dc.subject SST predictions en_ZA
dc.subject Seasonal forecasting en_ZA
dc.subject Atmospheric general circulation model (AGCM) en_ZA
dc.subject South Africa (SA) en_ZA
dc.subject Sea-surface temperature (SST) en_ZA
dc.title SST prediction methodologies and verification considerations for dynamical mid-summer rainfall forecasts for South Africa en_ZA
dc.type Article en_ZA


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