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Lisan Yu, Xiangze Jin, and Robert A. Weller

-generation reanalysis. As for the two ECMWF flux products, one is from the ECMWF operational forecast–analysis model ( ECMWF 1994 ) and the other is from the ECMWF Reanalysis-40 (ERA40; Uppala et al. 1999 ). Unlike in ERA-40, which uses a frozen analysis–forecast system, the ECMWF operational (hereafter referred to ECMWF-OP) system upgrades the model platform to ensure the best analysis–forecast fields and the up-to-date parameterizations of air–sea fluxes ( Beljaars 1997 ; Klinker 1997 ). The change of model

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Joaquim Ballabrera-Poy, Eric Hackert, Raghu Murtugudde, and Antonio J. Busalacchi

selection of the optimal locations is estimated with the help of a full Kalman filter (KF; Kalman and Bucy 1960 ) defined on a coarser grid. To address the issue of redundancy and array simplification, we will identify the most redundant moorings of the array. Redundancy of each mooring will be measured by comparing the results obtained from the assimilation with and without that particular mooring. While some degree of redundancy is necessary in operational observational systems (to provide a better

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Gabriel A. Vecchi and Matthew J. Harrison

-Range Weather Forecasts ( ECMWF 1989 ) 12-hourly, 2.5° × 2.5° resolution operational 10-m wind analysis and another using 2000–02 wind data from the National Aeronautics and Space Administration (NASA) Quick Scatterometer (QuikSCAT), made available by NASA’s Jet Propulsion Laboratory (JPL). Microwave scatterometry gives us the ability to explore basin-scale modes of vector wind variability in an unprecedented manner. We use NASA/JPL’s QuikSCAT level-3 satellite vector wind product, with each vector

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Debasis Sengupta, Retish Senan, B. N. Goswami, and Jérôme Vialard

were obtained from the Center for Space Research, University of Texas at Austin, Austin, Texas (information online at ). The Jason-1 altimeter data were obtained from AVISO, Ramonville St. Agne, France ( ). REFERENCES Anderson , D. L. T. , and D. J. Carrington , 1993 : Modeling interannual variability in the Indian Ocean using momentum fluxes from the operational weather analysis of

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Clémentde Boyer Montégut, Jérôme Vialard, S. S. C. Shenoi, D. Shankar, Fabien Durand, Christian Ethé, and Gurvan Madec

, in good agreement with the 24 W m −2 of Düing and Leetmaa (1980) . However, when comparing our fluxes with the recent Southampton Oceanographic Center (SOC) climatology (see SSS02 , their Fig. 8), one realizes that we have a weaker shortwave gain in all basins and a stronger latent heat loss in the AS during monsoons. This is also the case for older climatologies and for the NCEP or European Centre for Medium-Range Weather Forecasts (ECMWF) heat fluxes ( Weller et al. 1998 ). These fluxes

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