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Michael A. Brunke, Zhuo Wang, Xubin Zeng, Michael Bosilovich, and Chung-Lin Shie

Bourras et al. (2002) . Finally, sea surface turbulent fluxes can be derived from global model results that have been constrained by surface and rawinsonde observations and satellite measurements. Such products are called reanalyses and are produced by some of the major modeling centers, such as the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency

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Sun Wong, Eric J. Fetzer, Brian H. Kahn, Baijun Tian, Bjorn H. Lambrigtsen, and Hengchun Ye

overestimation of AIRS–MERRA in these regions may be related to the coarse resolution used to compute the water vapor advection. In general, the similarity between the AIRS–MERRA and the MERRA–MERRA Σs suggests that sampling effects caused by the missing data around cloudy areas in the AIRS L3 dataset ( Fig. 4 ) are minimal. However, cloud-induced systematic biases may still exist and cannot be assessed by merely comparing the two datasets. Microwave remote sensing techniques for water vapor in cloudy areas

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Man-Li C. Wu, Oreste Reale, and Siegfried D. Schubert

evidence of two time scales within the easterly wave regime. One of the two subregimes, appearing on the 6–9-day time scale with a wavelength of about 6000 km, was attributed to oscillations within subtropical high belts. In the more extensive follow-up study by the same team ( Diedhiou et al. 1999 ), the spectral analysis and wave tracks were computed from both NCEP and European Centre for Medium-Range Weather Forecasts (ECMWF) daily reanalyses, confirming those results and providing more robust

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J. Brent Roberts, Franklin R. Robertson, Carol A. Clayson, and Michael G. Bosilovich

with respect to its accuracy, climatology, and variability. In a reanalysis effort, comprehensive global models are combined with sophisticated data assimilation techniques to utilize observations in constraining the model state variables (surface pressure, air temperature, water vapor, and wind) while producing regular gridded state and physics fields (even where observations do not exist). Examples of these products include the National Centers for Environmental Prediction (NCEP; Kanamitsu et al

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Nili Harnik, Judith Perlwitz, and Tiffany A. Shaw

-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) dataset in their study. They noted that ERA-40 and MERRA were consistent in their climatologies of downward wave coupling in the Southern Hemisphere. Downward wave coupling is analyzed using the diagnostics of Shaw et al. (2010) . A cross-spectral correlation technique ( Randel 1987 ) is used to isolate upward and downward propagating planetary wave signals. The diagnostic considers two geopotential height Fourier coefficients of wavenumber k at two

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Tiffany A. Shaw, Judith Perlwitz, Nili Harnik, Paul A. Newman, and Steven Pawson

1. Introduction In a recent study Shaw et al. (2010) investigated the nature of downward wave coupling between the stratosphere and troposphere using the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) dataset. Downward wave coupling occurs when planetary waves reflected in the stratosphere impact the troposphere and is distinct from zonal-mean coupling, which results from wave dissipation and its subsequent impact on the zonal-mean flow ( Perlwitz and

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Franklin R. Robertson and Jason B. Roberts

(IAU) procedure ( Bloom et al. 1996 ), the analysis correction is applied to the forecast model gradually through an additional tendency term in the model equations during the corrector segment of the analysis cycle. This methodology minimizes spin-up–down issues with fluxes and enables closed budgets of model variables. With MERRA in particular, the representation of the MJO in the assimilating model in free-running mode is extremely weak ( Kim et al. 2009 ). Mapes and Bacmeister (2012) point

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