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Heat is one of the most serious hazards in the world as it affects human health and is extremely dangerous to vulnerable populations such as the elderly, people with preexisting cardiovascular or respiratory conditions, and even healthy people with prolonged sunlight exposure during heat waves. As the globe has warmed over the past several decades, extreme heat has become more frequent and intense than ever before, and Africa, especially the Sahel in West Africa, is one of the regions of the world where heat is a major public health concern exacerbated by livelihood activities during the heat season. Yet, there is a major gap in monitoring and forecasting heat waves in Africa. This paper describes NOAA's role in enabling heat–health early warning in Africa, working with meteorological services and health professionals. Emphasis is on real-time heat wave forecasting at week 2, including the postprocessing of the NCEP model outputs, and providing the information to the meteorological services in Africa to serve as guidance in national heat wave forecasts. In addition, the paper describes the end-to-end process of heat hazard outlooks and translating the forecasts into early action and early planning to reduce heat risk to human health. Furthermore, the paper addresses the very important aspect of capacity development tailored at enhancing forecasters' skills to prepare and issue heat wave forecasts and training of a cadre of health professionals to work with meteorologists to coproduce heat–health bulletins and to issue heat–health early warnings. [ABSTRACT FROM AUTHOR]

ABSTRACT Seasonal climate forecasts are operationally produced at various climate prediction centres around the world. However, these forecasts may not necessarily be appropriately integrated into application models in order to help with decision-making processes. This study investigates the use of a combination of physical and empirical models to predict seasonal inflows into Lake Kariba in southern Africa. Two predictions systems are considered. The first uses antecedent seasonal rainfall totals over the upper Zambezi catchment as predictor in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method uses predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model ( CGCM) downscaled to the inflows. Forecast verification results are presented for five run-on 3-month seasons; from September to June over an independent hindcast period of 14 years (1995/1996 to 2008/2009). Verification is conducted using the relative operating characteristic ( ROC) and the reliability diagram. In addition to the presented verification statistics, the hindcasts are also evaluated in terms of their economic value as a usefulness indicator of forecast quality for bureaucrats and to the general public. The models in general perform best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season). Moreover, the prediction system that uses the output of the CGCM is superior to the simple statistical approach. An additional forecast of a recent flooding event (2010/2011), which lies outside of the 14-year verification window, is presented to demonstrate the forecast system's operational capability further during a season of high inflows that caused societal and infrastructure problems over the region. [ABSTRACT FROM AUTHOR]