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

done for da Silva et al. (1994) and the National Oceanography Centre, Southampton (NOC), flux product ( Berry and Kent 2009 ), which used observations from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS; Worley et al. 2005 ). Such products utilize these bulk quantities as input into bulk aerodynamic algorithms to calculate the fluxes by way of the following equations: where ρ a is the density of air, c p is the specific heat of air at constant pressure, and L υ is the

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Yonghong Yi, John S. Kimball, Lucas A. Jones, Rolf H. Reichle, and Kyle C. McDonald

and freeze–thaw status, with improved (<10 km) resolution over current satellite microwave remote sensing products available from the Special Sensor Microwave Imager (SSM/I), Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E), and the Soil Moisture and Ocean Salinity (SMOS) mission. In the Level 4 soil moisture algorithm, SMAP observations will be assimilated within a land surface data assimilation system that is being developed in the Goddard Earth Observing System

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

from parameterizations such as the Coupled Ocean–Atmosphere Response Experiment (COARE; Fairall et al. 2003 ) algorithm. The key variables to be measured are sea surface temperature (SST), near-surface wind speed, specific humidity, and air temperature. A recent review of the abilities to retrieve these quantities is summarized in Bourassa et al. (2010) . Products based on in situ observations such as those of the National Oceanography Centre, Southampton (NOC; Berry and Kent 2009 ) utilize

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Derek J. Posselt, Andrew R. Jongeward, Chuan-Yuan Hsu, and Gerald L. Potter

representation of a host of Earth system variables, reanalyses have been successfully used in a wide range of weather and climate research activities. The general utility of a reanalysis dataset depends on the fidelity of its representation of the state of the atmosphere, which in turn depends on the data assimilation algorithm, data assimilated, and realism of the numerical model’s physical parameterizations. While most reanalysis efforts produce robust estimates of temperature and horizontal wind

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Benjamin A. Schenkel and Robert E. Hart

ability of reanalyses to represent TCs has been limited to the frequency of TC detection using automated tracking algorithms. The parameters chosen for a given algorithm vary among each reanalysis with thresholds subjectively chosen to yield detection rates on the order of 75% or greater for Best-Track TCs. The only attempt at comparing detection rates using a uniform algorithm was performed by Onogi et al. (2007) who examined detection frequencies for the ERA-40 and the Japan Meteorological Agency

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

algorithm of the Tropical Rainfall Measuring Mission (TRMM; Huffman et al. 2007 ) and the Global Precipitation Climatology Project (GPCP; Huffman et al. 2009 ), estimates of surface evaporation fluxes ( E ) from the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF; Chou et al. 2003 ; Shie et al. 2009 ), and the reanalysis q , P , and E products such as those from the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) Modern Era Retrospective

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Franklin R. Robertson and Jason B. Roberts

maps at 2.5° resolution. The objectively analyzed air–sea fluxes for the global ocean (OAFlux) dataset ( Yu and Weller 2007 ; Yu et al. 2008 ) uses a variational objective analysis technique with optimal weighting based on buoy and ship observations to combine satellite and reanalysis data. With these analyses, fluxes are derived using the Coupled Ocean–Atmosphere Response Experiment (COARE) bulk flux algorithm 3.0 ( Fairall et al. 2003 ). From this dataset we took daily values of latent (LHF) and

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Michele M. Rienecker, Max J. Suarez, Ronald Gelaro, Ricardo Todling, Julio Bacmeister, Emily Liu, Michael G. Bosilovich, Siegfried D. Schubert, Lawrence Takacs, Gi-Kong Kim, Stephen Bloom, Junye Chen, Douglas Collins, Austin Conaty, Arlindo da Silva, Wei Gu, Joanna Joiner, Randal D. Koster, Robert Lucchesi, Andrea Molod, Tommy Owens, Steven Pawson, Philip Pegion, Christopher R. Redder, Rolf Reichle, Franklin R. Robertson, Albert G. Ruddick, Meta Sienkiewicz, and Jack Woollen

° longitude with 72 vertical levels, from the surface to 0.01 hPa. Additional details are provided in R2008 . MERRA uses a three-dimensional variational data assimilation (3DVAR) analysis algorithm based on the Gridpoint Statistical Interpolation scheme (GSI; Wu et al. 2002 ; Derber et al. 2003 ; Purser et al. 2003a , b ) with a 6-h update cycle. The GSI, originally developed at NCEP and now jointly developed by NCEP and the GMAO, includes a number of advancements over 3DVAR algorithms used previously

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Franklin R. Robertson, Michael G. Bosilovich, Junye Chen, and Timothy L. Miller

with the AMSU-A window channel effects on the assimilation of moisture, we believe this choice will remain ambiguous until specific satellite algorithm and bias correction issues are studied more thoroughly with numerical experimentation. 6. Results and discussion a. Adjusted time series for heat and water budget terms Figures 11 and 12 show area-averaged anomaly time series of the corrected budget quantities over separate global ocean and land domains. By construction the large trends in the

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Aaron D. Kennedy, Xiquan Dong, Baike Xi, Shaocheng Xie, Yunyan Zhang, and Junye Chen

cloud products ( Minnis et al. 2010 ) using algorithms developed for the NASA Clouds and the Earth’s Radiant Energy System (CERES) project. Cloud properties are retrieved from half-hourly 4-km 0.65, 3.9, 10.8 (IR), and 12.0- μ m radiances taken by GOES-8 . Cloudy pixels are identified using an adaptation of the method described by Minnis et al. (2008) . The areal fraction of clouds [or the amount when present, (AWP)] is the ratio of the number of pixels classified as cloudy to the total number of

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