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Wanqiu Wang and Pingping Xie

observations to estimate SST under cloudy sky. The recent availability of several new satellite-based SST observations makes it possible to construct fine-resolution SST analyses with improved quality. In particular, passive microwave observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; Kummerow et al. 1998 ; Wentz et al. 2000 ) and the Advanced Microwave Scanning Radiometer (AMSR; Wentz and Meissner 1999 ) have been used to derive SST estimates over both clear and

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Myong-In Lee, Siegfried D. Schubert, Max J. Suarez, Isaac M. Held, Arun Kumar, Thomas L. Bell, Jae-Kyung E. Schemm, Ngar-Cheung Lau, Jeffrey J. Ploshay, Hyun-Kyung Kim, and Soo-Hyun Yoo

precipitation in observations and model forecasts during NAME with emphasis on the diurnal cycle. J. Climate , 20 , 1680 – 1692 . Joyce , R. J. , J. E. Janowiak , P. A. Arkin , and P. Xie , 2004 : CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor. , 5 , 487 – 503 . Koster , R. D. , and Coauthors , 2004 : Regions of strong coupling between soil moisture and precipitation

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Paquita Zuidema, Chris Fairall, Leslie M. Hartten, Jeffrey E. Hare, and Daniel Wolfe

1. Introduction Monsoons are fundamentally driven by land–sea heating asymmetries. While the North American Monsoon Experiment (NAME) was well instrumented on land, for logistical reasons observations of the coincident conditions at sea were far fewer. One of the participating research vessels was the Mexican Navy’s R/V Altair , which positioned itself close to the mouth of the gulf intermediate between Mazatlan and La Paz from 7 July until 12 August. 1 The shipboard measurements contributed

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X. Gao, J. Li, and S. Sorooshian

scientific objectives by using “a symbiotic mix of diagnostic, modeling, and prediction studies together with enhanced observations” ( NAME Project Science Team 2004 ). The current study analyzes the results of numerical modeling according to NAM system characteristics that were exposed by diagnostic studies based on historic observations and data-assimilation reanalysis. We used a currently available, physically based numerical model to check the agreement and disagreement between the model results, the

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Kingtse C. Mo, Eric Rogers, Wesley Ebisuzaki, R. Wayne Higgins, J. Woollen, and M. L. Carrera

day, and 0000 UTC usually had more data counts than 1200 UTC. The upper-air data included rawinsonde, pibal, dropwinsonde, and reconnaissance, and had good coverage at 0000 UTC ( Fig. 2a ). At 1800 UTC, most data came from the NAME special soundings ( Fig. 2b ). The surface observations from landmasses had very large data counts for both 0000 and 1800 UTC. In the nearby oceans, data included the surface marine ships, buoys, Coastal-Marine Automated Network (C-MAN) platforms, and splash

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J. Craig Collier and Guang J. Zhang

were used for validating the model. For evaluating the sensitivity of the CAM3 rainfall simulation to increasing horizontal resolution, hourly rainfall rates over four monsoon seasons from NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite ( Simpson et al. 1988 ; Kummerow et al. 1998 , 2000 ) were used. Instantaneous rainfall rates were derived from the combination of measurements from the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). Available in the 3G68 (v.6

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Enrique R. Vivoni, Hugo A. Gutiérrez-Jurado, Carlos A. Aragón, Luis A. Méndez-Barroso, Alex J. Rinehart, Robert L. Wyckoff, Julio C. Rodríguez, Christopher J. Watts, John D. Bolten, Venkataraman Lakshmi, and Thomas J. Jackson

implications. Remote Sens. Environ. , 82 , 417 – 430 . Schmugge , T. , T. J. Jackson , W. P. Kustas , R. Roberts , R. Parry , D. C. Goodrich , S. A. Amer , and M. A. Weltz , 1994 : Push broom microwave radiometer observations of surface soil moisture in Monsoon ’90. Water Resour. Res. , 30 , 1321 – 1328 . Seyfried , M. S. , and M. D. Murdock , 2004 : Measurement of soil water content with a 50-MHz soil dielectric sensor. Soil Sci. Soc. Amer. J. , 68 , 394 – 403

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Timothy J. Lang, David A. Ahijevych, Stephen W. Nesbitt, Richard E. Carbone, Steven A. Rutledge, and Robert Cifelli

derived from passive microwave and geosynchronous infrared data. J. Climate , 6 , 2144 – 2161 . Nesbitt , S. W. , E. J. Zipser , and D. J. Cecil , 2000 : A census of precipitation features in the Tropics using TRMM: Radar, ice scattering, and lightning observations. J. Climate , 13 , 4087 – 4106 . Nesbitt , S. W. , R. Cifelli , and S. A. Rutledge , 2006 : Storm morphology and rainfall characteristics of TRMM precipitation features. Mon. Wea. Rev. , 134 , 2702 – 2721

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Mekonnen Gebremichael, Enrique R. Vivoni, Christopher J. Watts, and Julio C. Rodríguez

observations and the complexity of the terrain. Information on the submesoscale variability is of great interest to water resource managers in this water-scarce region. It is essential to address basic research questions on the link between topography and monsoon rainfall variability. Improved large-scale numerical simulations also depend on the proper characterization of the submesoscale variability ( Gutzler et al. 2005 ). In this paper, we present an in-depth examination of the submesoscale monsoon

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John E. Janowiak, Valery J. Dagostaro, Vernon E. Kousky, and Robert J. Joyce

Joyce et al. (2004) . Briefly, precipitation estimates from all available passive microwave (PMW) sensors aboard low earth orbit space craft are merged for each 30-min period. Even though eight PMW instruments are used, considerable gaps in coverage remain. Infrared (IR) data from the present-day constellation of five geostationary meteorological satellites are used to infer the movement of precipitation features that have been identified by the PMW information. Movement is determined from the IR

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