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Thunderstorms, due to their high frequency of occurrence over southern Africa and their dominant contribution to summer rainfall, are the primary focus of very short range forecasting and nowcasting efforts in South Africa. Most southern African countries are heavily reliant on satellite technology due to the limited number of surface and upper-air observations and the limited availability of numerical model output. In developing tools for the first 12 forecast hours, the South African Weather Service has to address both national and regional needs. The blending of techniques in an optimal manner is essential in achieving this. In this paper a description is given of how the Global Instability Index product derived from the European Meteosat Second Generation Satellite was adapted to fit to South African circumstances using a different numerical model as background information, creating the Regional Instability Indices (RII). The focus of the study is the development of a new convection indicator, called the Combined Instability Index (CII), which calculates the probability of convection based on satellite derived instability indices and moisture as well as orographic lift early in the morning when the sky is as cloud free as possible. The CII is an objective way for operational forecasters to calculate the probability for convection later in the day based on satellite and model data. Early morning CII values were evaluated statistically against the occurrence of lightning as well as against satellite derived precipitation later in the same day. It is shown that the CII not only performs well, but also outperforms the individual RII. The CII will be beneficial to operational forecasters to focus their attention on the area which is most favourable for the development of convection later in the day. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT An assessment of probabilistic prediction skill of seasonal temperature extremes over Southern Africa is presented. Verification results are presented for six run-on seasons; September to November, October to December, November to January, December to February, January to March, and February to April over a 15-year retroactive period. Comparisons are drawn between downscaled seasonal 850 hPa geopotential height field forecasts of a two-tiered system versus downscaled height forecasts from a coupled ocean-atmosphere system. The ECHAM4.5 atmospheric general circulation model (GCM) is used for both systems; in the one-tiered system the ECHAM4.5 is directly coupled to the ocean model Modular Ocean Model version three (MOM3), and in the two-tiered system the ECHAM4.5 is coupled with Van den Dool sea surface temperature (SST) hindcasts. Model output statistical equations are developed using canonical correlation analysis (CCA) to reduce system deficiencies. Probabilistic verification is conducted using the relative operating characteristic (ROC) and reliability diagram. The coupled model performs best in capturing seasonal maximum temperature extremes. Seasons demonstrating the highest ROC scores coincide with the period of highest seasonal temperatures found over Southern Africa. The above-normal category of the one-tiered system indicates the highest skill in predicting maximum temperature extremes, implying the coupled model predicts skilfully when there is a high likelihood of experiencing extremely high seasonal maximum temperatures during mid to late summer. The downscaled coupled maximum temperature hindcasts are evaluated additionally in terms of their monetary value and quality to the general public. The seasonal forecast system presented in this study should be able to reduce risks in decision making by the health industry in Southern Africa. [ABSTRACT FROM AUTHOR]