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

被引:15
|
作者
Landman, Willem A. [1 ,2 ]
Beraki, Asmerom [3 ]
DeWitt, David [4 ]
Loetter, Daleen [1 ]
机构
[1] CSIR, Nat Resources & Environm, Pretoria, South Africa
[2] Univ Pretoria, Dept Geog Geoinformat & Meteorol, ZA-0002 Pretoria, South Africa
[3] South African Weather Serv, Pretoria, South Africa
[4] Int Res Inst Climate & Soc, Palisades, NY USA
基金
新加坡国家研究基金会; 美国海洋和大气管理局;
关键词
AGCM; SST predictions; seasonal forecasting; South Africa; CANONICAL CORRELATION-ANALYSIS; SEASONAL CLIMATE FORECASTS; GENERAL-CIRCULATION MODEL; GCM FORECASTS; PERFORMANCE; SKILL; PREDICTABILITY; RECALIBRATION; ANOMALIES; LONG;
D O I
10.4314/wsa.v40i4.6
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
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.
引用
收藏
页码:615 / 622
页数:8
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