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Fatima Karbou, Elisabeth Gérard, and Florence Rabier

1. Introduction Microwave observations from the Advanced Microwave Sounding Unit-A and -B [AMSU-A and -B; or Microwave Humidity Sounder (MHS)] instruments have been widely used in numerical weather prediction (NWP) to improve the initial conditions for short-range forecasts. AMSU instruments are on board low-orbiting satellites such as the different generations of the National Oceanic and Atmospheric Administration (NOAA) satellites, the National Aeronautics and Space Administration’s (NASA

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Fatima Karbou, Florence Rabier, Jean-Philippe Lafore, Jean-Luc Redelsperger, and Olivier Bock

observations have been found very useful for measuring the precipitable water vapor (PWV) at different scales over Africa ( Bock et al. 2007 ). The humidity measurement network has been temporally extended as part of AMMA, but more effort is still necessary in order to provide additional estimates of the humidity over all surfaces with good temporal and spatial sampling. Observations from satellite sensors such as the Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B), which have considerable

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C. Faccani, F. Rabier, N. Fourrié, A. Agusti-Panareda, F. Karbou, P. Moll, J.-P. Lafore, M. Nuret, F. Hdidou, and O. Bock

. Rev. , 127 , 26 – 45 . 10.1175/1520-0493(1999)127<0026:SARPPF>2.0.CO;2 Karbou, F. , Gérard E. , and Rabier F. , 2006 : Microwave land emissivity and skin temperature for AMSU-A and -B assimilation over land. Quart. J. Roy. Meteor. Soc. , 132 , 2333 – 2355 . 10.1256/qj.05.216 Karbou, F. , Gérard E. , and Rabier F. , 2009a : Global 4DVAR assimilation and forecast experiments using land surface emissivities from AMSU-A and AMSU-B observations. Part I: Impact on sounding channels

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O. Bock and M. Nuret

to modifications allowing the assimilation of new Advanced Microwave Sounding Unit-B (AMSU-B) channels ( Karbou et al. 2008 , manuscript submitted to Wea. Forecasting ; Faccani et al. 2009 ). The combination of both modifications provides clearly improved analyses and forecasts over the AMMA region (reduced surface humidity bias, improved precipitation scores, higher PWV contents in agreement with GPS observations, and downstream propagation of reduced geopotential height error). In parallel

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Adrian M. Tompkins and Laura Feudale

atmosphere-only l0-day forecast run twice each day and an ensemble forecast at lower resolution, which extends to 1 month using a coupled ocean model ( Vitart et al. 2008 ). The atmospheric model component of these latter systems is usually updated several times per year, each identified with a code XXRY, where Y is the release (R) of each code cycle XX. Some of these atmospheric model upgrades are not relevant for the seasonal forecasting system, monitoring or incorporating new observations into the

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Peter Knippertz and Andreas H. Fink

evaluates ERA-40 forecasts of these events. Section 6 contains a detailed analysis of the dynamics including a differentiation between successful and unsuccessful forecasts. The most important results are summarized and discussed in section 7 together with a compilation of open research issues. 2. Data a. Precipitation observations The main observational dataset used in this study is a merged satellite–gauge product provided by the Global Precipitation Climatology Project (GPCP). This dataset

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