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

has been done to derive forcing using constrained variational analysis from observations during intensive observation periods (IOPs) at the Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) sites ( Zhang and Lin 1997 ; Zhang et al. 2001 ). More recently, Xie et al. (2003) evaluated the forcing datasets derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) during three IOPs at the ARM Southern Great Plains (SGP) site. They found that although the

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Mark Decker, Michael A. Brunke, Zhuo Wang, Koichi Sakaguchi, Xubin Zeng, and Michael G. Bosilovich

large updates. Satellite radiances are directly assimilated while accounting for variational bias correction through the use of the Joint Center for Satellite Data Assimilation (JCSDA) community radiative transfer model. Unlike other reanalysis products, MERRA also assimilates instantaneous rain rate (precipitation) observations though the use of Special Sensor Microwave Imager (SSM/I) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements. A key difference between

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Brian E. Mapes and Julio T. Bacmeister

Experiment (TOGA COARE) field program. For these two periods, a comprehensive composite of all (more than 100) MERRA dataset variables was made and perused. The similarity of results between these two MJO-active periods gives us confidence that the conclusions reported here are robust. We focus here on the 1990 results, in part to emphasize that the special observations of 1992–93 are not critical for this exercise. Microwave rainfall estimates over ocean, obtained from Remote Sensing Systems ( http

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

minimization process (i.e., reconciling observations with the background guess). This allows the detection of time-dependent biases from the entire suite of observing systems (e.g., Dee and Uppala 2009 ). Like the Japanese 25-year Reanalysis (JRA-25; Onogi et al. 2007 ) and the ECMWF Interim Reanalysis (EC-Interim; Berrisford et al. 2009 ), the MERRA employs this bias correction approach. Despite these advances, additional improvement in bias correction of datasets and models is still required as

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Benjamin A. Schenkel and Robert E. Hart

both reanalysis data and in situ observations to identify candidate warm-core cyclones for consideration for addition to the National Hurricane Center (NHC) Best-Track dataset ( Jarvinen et al. 1984 ; Neumann et al. 1993 ) as TCs. In light of the increased usage of reanalyses for studying TCs, guidance is necessary to determine when the degree of TC representation within reanalyses is sufficiently robust for such studies and to quantify the strength of any nonphysical relationships for TC position

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