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–29 August time interval is presented using satellite-based estimates of rainfall and surface incoming solar flux, together with the latent heat flux (LE) derived from these products with land surface modeling ( Boone et al. 2009 ); see section 4 (and Table 3 ) for further details on these products. According to these estimates, the 24-h rainfall and evapotranspiration ( E ) display high and related variability from one day to the next. Links between the rainfall and subsequent LE patterns are obvious
–29 August time interval is presented using satellite-based estimates of rainfall and surface incoming solar flux, together with the latent heat flux (LE) derived from these products with land surface modeling ( Boone et al. 2009 ); see section 4 (and Table 3 ) for further details on these products. According to these estimates, the 24-h rainfall and evapotranspiration ( E ) display high and related variability from one day to the next. Links between the rainfall and subsequent LE patterns are obvious
increasing convective activity at certain times of the year. The suppression of the convection would be temporary, lasting until the enhanced monsoon humidity flux allows deep convection onset in the Sahel ( Sultan et al. 2007 ). The reproduction of this effect in models would rely on them representing the vertical humidity structure and its impacts on convection well, which, as Derbyshire et al. (2004) demonstrated, is a task that many current state-of-the-art models fail to accomplish. Figure 3
increasing convective activity at certain times of the year. The suppression of the convection would be temporary, lasting until the enhanced monsoon humidity flux allows deep convection onset in the Sahel ( Sultan et al. 2007 ). The reproduction of this effect in models would rely on them representing the vertical humidity structure and its impacts on convection well, which, as Derbyshire et al. (2004) demonstrated, is a task that many current state-of-the-art models fail to accomplish. Figure 3
reverse flow at 750 hPa above the monsoon flux between the ITD and the ITCZ ( Zhang et al. 2006 ). All these changes for all variables due to the assimilation of low-level microwave observations are coherent and correspond at this location (central Africa) to a better-organized active monsoon. The moistening does not result in a northward shift of the monsoon due to stabilization effects. We now analyze the impacts of the assimilation of low-level microwave observations on the precipitation forecast
reverse flow at 750 hPa above the monsoon flux between the ITD and the ITCZ ( Zhang et al. 2006 ). All these changes for all variables due to the assimilation of low-level microwave observations are coherent and correspond at this location (central Africa) to a better-organized active monsoon. The moistening does not result in a northward shift of the monsoon due to stabilization effects. We now analyze the impacts of the assimilation of low-level microwave observations on the precipitation forecast
that the ECMWF model has problems with the cloud and precipitation over West Africa, with the ITCZ being shifted to the south and an overall lack of precipitation over the Sahel ( Agustí-Panareda and Beljaars 2008 ; Agustí-Panareda et al. 2009c ). Agustí-Panareda et al. (2009b) and Meynadier et al. (2010, manuscript submitted to J. Geophys. Res. ) have shown that the cloud and precipitation bias is linked to an overestimation of the moisture flux divergence associated with an
that the ECMWF model has problems with the cloud and precipitation over West Africa, with the ITCZ being shifted to the south and an overall lack of precipitation over the Sahel ( Agustí-Panareda and Beljaars 2008 ; Agustí-Panareda et al. 2009c ). Agustí-Panareda et al. (2009b) and Meynadier et al. (2010, manuscript submitted to J. Geophys. Res. ) have shown that the cloud and precipitation bias is linked to an overestimation of the moisture flux divergence associated with an
-B channels sensitive to humidity in the lower troposphere) should lead to improved forecast skill for precipitation over West Africa. It should also allow the computation of more accurate water budgets, especially at regional scales, for which NWP models are especially useful at estimating moisture flux divergence and tendency terms ( Higgins et al. 1996 ; Trenberth and Guillemot 1998 ; Roads et al. 2002 ; Bock et al. 2008b ). Understanding the water cycle of the WAM is a fundamental objective of AMMA
-B channels sensitive to humidity in the lower troposphere) should lead to improved forecast skill for precipitation over West Africa. It should also allow the computation of more accurate water budgets, especially at regional scales, for which NWP models are especially useful at estimating moisture flux divergence and tendency terms ( Higgins et al. 1996 ; Trenberth and Guillemot 1998 ; Roads et al. 2002 ; Bock et al. 2008b ). Understanding the water cycle of the WAM is a fundamental objective of AMMA
average of the quantity () and [()] represents a meridional average of the zonal average (area mean). A prime indicates a deviation from a zonal average. When C E is positive, this represents a gain of eddy kinetic energy at the expense of eddy available potential energy that occurs when warm air rises and cold air sinks. A positive value of C K represents an increase in eddy kinetic energy at the expense of the zonal kinetic energy that occurs when the eddy momentum flux is down the mean momentum
average of the quantity () and [()] represents a meridional average of the zonal average (area mean). A prime indicates a deviation from a zonal average. When C E is positive, this represents a gain of eddy kinetic energy at the expense of eddy available potential energy that occurs when warm air rises and cold air sinks. A positive value of C K represents an increase in eddy kinetic energy at the expense of the zonal kinetic energy that occurs when the eddy momentum flux is down the mean momentum
al. 1996 ). This dataset consists of a reanalysis of the global observational network of meteorological variables (wind, temperature, geopotential height, humidity on pressure levels, surface variables, and flux variables such as precipitation rate) with a “frozen” state-of-the-art analysis and forecast system at a triangular spectral truncation of T62 to perform data assimilation throughout the period from 1948 to the present. This circumvents problems with previous operational analyses due to
al. 1996 ). This dataset consists of a reanalysis of the global observational network of meteorological variables (wind, temperature, geopotential height, humidity on pressure levels, surface variables, and flux variables such as precipitation rate) with a “frozen” state-of-the-art analysis and forecast system at a triangular spectral truncation of T62 to perform data assimilation throughout the period from 1948 to the present. This circumvents problems with previous operational analyses due to
