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J. R. Garratt

higher windspeeds (e.g., Toba and Kunishi, 1970).4. Values of drag coefficients over land Direct measurements of surface stress at or nearthe surface have been made by numerous workers[e.g., Sheppard (1947) and Bradley (1968) using dragplates; Mordukovitch and Tsvang (1966)using sonicanemometers~, 'usually over flat, locally homogeneousterrain, during micrometeorological expeditions. Suchobservations, together with simultaneous (surfacelayer) profiles of wind and temperature have

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P. L. Houtekamer and Fuqing Zhang

, it will likely be of value that a moderately large variety of ensemble-based data assimilation systems, using different parallelization and (due to their fundamental impact on computational efficiency) localization strategies, exists at operational centers. Improved boundary conditions, including a reasonable estimate of uncertainty, could be provided by coupling of an atmospheric EnKF with similar systems for the ocean, the ice, the land surface, chemical constituents, etc. In principle, it is

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Zhiyong Meng and Fuqing Zhang

Oklahoma City tornadic supercell storm assimilating radar and surface network data using EnKF . Preprints, 13th Conf. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface (IOAS-AOLS) , Pheonix, AZ, Amer. Meteor. Soc., Paper 6.4. [Available online at http://ams.confex.com/ams/89annual/techprogram/paper_150404.htm with a link to the extended abstract at http://twister.ou.edu/papers/LeiXueYu_AMS2009.pdf .] Li , H. , E. Kalnay , T. Miyoshi , and C. M

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Peter Jan van Leeuwen

) estimated a 684-dimensional state vector in their land surface dynamics model using 1 satellite-derived brightness observation every day for 28 days. They compared the results of the particle filter with probabilistic resampling with those of the EnKF and found that although the particle filter gave better results, it needed 800 particles to converge versus 80 for the EnKF. They mention that when more measurements are used, or when the state space increases, much more particles are needed in the

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Markus Gross, Hui Wan, Philip J. Rasch, Peter M. Caldwell, David L. Williamson, Daniel Klocke, Christiane Jablonowski, Diana R. Thatcher, Nigel Wood, Mike Cullen, Bob Beare, Martin Willett, Florian Lemarié, Eric Blayo, Sylvie Malardel, Piet Termonia, Almut Gassmann, Peter H. Lauritzen, Hans Johansen, Colin M. Zarzycki, Koichi Sakaguchi, and Ruby Leung

highlights the strength of an idealized testing framework to shed light on the physics–dynamics interactions. This approach can also be used to analyze the effects of different physics–dynamics coupling schemes. 5. Intramodel coupling In this section, the focus is on intramodel coupling problems within the modeling system, where the coupling occurs via an exchange of boundary conditions that transmit fluxes through a physical interface (e.g., the ocean–atmosphere, land–atmosphere, ice–atmosphere, or

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Craig S. Schwartz and Ryan A. Sobash

precipitation using the ECMWF ensemble prediction system . Wea. Forecasting , 14 , 168 – 189 , doi: 10.1175/1520-0434(1999)014<0168:PPOPUT>2.0.CO;2 . 10.1175/1520-0434(1999)014<0168:PPOPUT>2.0.CO;2 Chen , F. , and J. Dudhia , 2001 : Coupling an advanced land-surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model description and implementation . Mon. Wea. Rev. , 129 , 569 – 585 , doi: 10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2 . 10

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Tammy M. Weckwerth and David B. Parsons

) process studies in which enhanced moisture measurements were obtained to better understand and predict the location and timing of new convection. This improved knowledge is an essential step in improving QPF skill. (iii) The atmospheric boundary layer component, which examined surface heterogeneities and land-use differences to ascertain their relevance for CI and QPF. (iv) The instrumentation component, which attempted to define the near-future optimal mix of remote and in situ moisture sensors to

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Loïk Berre and Gérald Desroziers

1. Introduction Usual data assimilation systems for numerical weather prediction (NWP), using Kalman filter or variational techniques, are based on a statistical combination of observations and a background, which is usually a short-term forecast. This statistical estimation requires the specification of spatial covariances of errors in these two kinds of information. As presented in Hollingsworth (1987) and Daley (1991 , p. 125), the role of background error covariances is to spatially

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Robert Wood

primarily driven by convective instability caused by cloud-top radiative cooling, a definition that distinguishes stratocumulus from stratus. Stratocumuli swathe enormous regions of Earth’s surface and exhibit a great variety of structure on a wide range of spatial scales ( Fig. 1 ). They cover approximately one-fifth of Earth’s surface in the annual mean (23% of the ocean surface and 12% of the land surface), making them the dominant cloud type by area covered ( Warren et al. 1986 , 1988 ; Hahn and

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David M. Schultz and Philip N. Schumacher

, dynamics, and possible electrification of weather systems that might result from such scenarios. Section 6 investigates the nature of cloud and precipitation banding in relation to MSI, and section 7 addresses the diagnosis of slantwise convection using mesoscale numerical-model output. Finally, section 8 consists of a summary of main points, directions for future research, and a concluding discussion. 2. An ingredients-based methodology for slantwise convection Throughout this

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