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algorithm and the motivation behind the chosen thresholds in section 4a . This algorithm is then applied to the entire GPCP pentad dataset in section 4b and to the ERA-40 forecasts in section 5 . In section 4c some remarkable events identified with the algorithm will be discussed. a. Identification The first step of the identification procedure is to calculate area averages of precipitation over the box indicated in Figs. 1 and 2 spanning 7.5°–15°N, 10°W–10°E. For the GPCP pentad data this
algorithm and the motivation behind the chosen thresholds in section 4a . This algorithm is then applied to the entire GPCP pentad dataset in section 4b and to the ERA-40 forecasts in section 5 . In section 4c some remarkable events identified with the algorithm will be discussed. a. Identification The first step of the identification procedure is to calculate area averages of precipitation over the box indicated in Figs. 1 and 2 spanning 7.5°–15°N, 10°W–10°E. For the GPCP pentad data this
-of-the-atmosphere (TOA) infrared flux is often used as a proxy for convective activity. The difference map ( Fig. 9 ) shows a large region over West Africa where the biases are negative (TOA IR flux is assigned positive downward), signifying too high outgoing longwave radiation. This agrees with the previous precipitation biases, indicating a lack of deep convection in the Sahel in this model cycle, although it should be recalled that TOA infrared flux information is used in the GPCP rainfall retrieval algorithms
-of-the-atmosphere (TOA) infrared flux is often used as a proxy for convective activity. The difference map ( Fig. 9 ) shows a large region over West Africa where the biases are negative (TOA IR flux is assigned positive downward), signifying too high outgoing longwave radiation. This agrees with the previous precipitation biases, indicating a lack of deep convection in the Sahel in this model cycle, although it should be recalled that TOA infrared flux information is used in the GPCP rainfall retrieval algorithms
by the spatial structure of the surface incoming solar radiation ( Fig. 2g ). Figure 3 reproduces the 24-h rainfall fields simulated by some of the global operational forecast models that participated to the 2005 dry-run exercise, together with the rainfall estimate of version 2.0 of the National Oceanic and Atmospheric Administration/Climate Prediction Center (NOAA/CPC) African rainfall estimation algorithm, referred to as CPC-RFE2, which is available from the associated Web site. The spread in
by the spatial structure of the surface incoming solar radiation ( Fig. 2g ). Figure 3 reproduces the 24-h rainfall fields simulated by some of the global operational forecast models that participated to the 2005 dry-run exercise, together with the rainfall estimate of version 2.0 of the National Oceanic and Atmospheric Administration/Climate Prediction Center (NOAA/CPC) African rainfall estimation algorithm, referred to as CPC-RFE2, which is available from the associated Web site. The spread in
. Overview of wave Before evaluating how initial-condition errors affect forecasts of AEW at these two initialization times, a short summary of the AEW and forecast performance is presented. This AEW, one of the most vigorous observed during AMMA, is first identified by the Berry et al. (2007) objective tracking algorithm over southern Sudan at 0000 UTC 5 September 2006 ( Thorncroft et al. 2007 ). Beginning 9 September, the 700-hPa curvature vorticity associated with the AEW markedly increases as it
. Overview of wave Before evaluating how initial-condition errors affect forecasts of AEW at these two initialization times, a short summary of the AEW and forecast performance is presented. This AEW, one of the most vigorous observed during AMMA, is first identified by the Berry et al. (2007) objective tracking algorithm over southern Sudan at 0000 UTC 5 September 2006 ( Thorncroft et al. 2007 ). Beginning 9 September, the 700-hPa curvature vorticity associated with the AEW markedly increases as it
