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and Hakim 2007 ). This equation is the best-fit line to the linear regression between the analysis state variable and forecast metric, which are the independent and dependent variables, respectively. Multiplying the right-hand side by the ensemble standard deviation, which is an approximation of the analysis error, allows for a qualitative comparison between forecast hours and fields since ∂ J /∂ x i has units of the metric. Ensemble sensitivity is estimated from a relatively small ensemble
and Hakim 2007 ). This equation is the best-fit line to the linear regression between the analysis state variable and forecast metric, which are the independent and dependent variables, respectively. Multiplying the right-hand side by the ensemble standard deviation, which is an approximation of the analysis error, allows for a qualitative comparison between forecast hours and fields since ∂ J /∂ x i has units of the metric. Ensemble sensitivity is estimated from a relatively small ensemble
regressions and empirical models ( Weng et al. 2001 ; Grody 1988 ) has been used in NWP and has facilitated the assimilation of AMSU channels over land. The effectiveness of these models depends on the input parameters about the surface, for which a global analysis does not always exist. To date, observations are more intensively used over sea than over land thanks to effective sea emissivity models ( Deblonde and English 2000 ; Guillou et al. 1998 ; Prigent and Abba 1990 ; Guissard and Sobieski 1987
regressions and empirical models ( Weng et al. 2001 ; Grody 1988 ) has been used in NWP and has facilitated the assimilation of AMSU channels over land. The effectiveness of these models depends on the input parameters about the surface, for which a global analysis does not always exist. To date, observations are more intensively used over sea than over land thanks to effective sea emissivity models ( Deblonde and English 2000 ; Guillou et al. 1998 ; Prigent and Abba 1990 ; Guissard and Sobieski 1987
variability of convection indicate modeling progress must be made in achieving the likely potential of dynamic models ( Waliser et al. 1999 ; von Storch and Baumhefner 1991 ). The aim of this paper is to give a first overview of the predictability of the intraseasonal variability of rainfall over West Africa at a medium lead time. We use a statistical method, singular spectrum analysis (SSA), which has already provided promising results in filtering and predicting intraseasonal oscillations of convection
variability of convection indicate modeling progress must be made in achieving the likely potential of dynamic models ( Waliser et al. 1999 ; von Storch and Baumhefner 1991 ). The aim of this paper is to give a first overview of the predictability of the intraseasonal variability of rainfall over West Africa at a medium lead time. We use a statistical method, singular spectrum analysis (SSA), which has already provided promising results in filtering and predicting intraseasonal oscillations of convection
