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Thomas Engel, Andreas H. Fink, Peter Knippertz, Gregor Pante, and Jan Bliefernicht

case showing 1200 UTC 26 Aug 2012. The yellow star shows the location of Dakar. 5. Statistical analysis Using the two cases discussed in section 4 as illustration, here we examine the capability of SRFE to reproduce observed rain rates and their applicability for a flood risk assessment for the African continent. For this, the satellite data will be directly compared to the surface measurements to highlight systematic differences between these two sources of rainfall information. a. Comparison of

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Wassila M. Thiaw and Vadlamani B. Kumar

decades, much attention has focused on advance risk planning in agriculture and water and, more recently, on health. These initiatives require updates of weather and climate outlooks. This paper describes the active role of NOAA’s African Desk in FEWS and in enhancing the capacity of African institutions to improve forecasts. The African Desk was established in 1994 to provide services to U.S. agencies and African institutions. Emphasis is on the operational products across all time scales from

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Robert A. Clark III, Zachary L. Flamig, Humberto Vergara, Yang Hong, Jonathan J. Gourley, Daniel J. Mandl, Stuart Frye, Matthew Handy, and Maria Patterson

applying the standardized precipitation index (SPI), a drought index, to the precipitation forecasts could be used to improve the prediction of droughts and their associated impacts. In the Zambezi River of southern Africa, Meier et al. (2011) demonstrate that hydrological models based only upon satellite estimates of precipitation and soil moisture can produce viable real-time forecasts of streamflow at a point. The Flood and Drought Risk Management Workshop was held in Namibia’s capital, Windhoek

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Zewdu T. Segele, Michael B. Richman, Lance M. Leslie, and Peter J. Lamb

totals at Addis Ababa and Combolcha ( Fig. 1 ) and standardized JJAS rainfall anomalies for northeastern Ethiopia based on atmospheric and SST predictors observed in March. Tables 5 – 7 list the 20-predictor sets used for local and regional predictions. Following previous studies (e.g., Gissila et al. 2004 ; Block and Rajagopalan 2007 ; Korecha and Barnston 2007 ; Nicholson 2014 ), these predictors were selected based on a careful assessment of maps of predictand–predictor correlations. All

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