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Axel Andersson, Christian Klepp, Karsten Fennig, Stephan Bakan, Hartmut Grassl, and Jörg Schulz

global water cycle datasets from retrievals of relevant ocean and atmospheric parameters such as sea surface temperature, winds, air humidity, and precipitation. Such datasets are provided with a better spatiotemporal sampling in comparison with in situ observations. The microwave part of the electromagnetic spectrum is ideally suited to retrieve precipitation and parameters useful to estimate latent heat flux and evaporation using a parameterization. At low microwave frequencies the emitted

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M. Tugrul Yilmaz, Paul Houser, Roshan Shrestha, and Valentine G. Anantharaj

modeling errors using the Nelder and Mead (1965) method with autonomous procedures. This study finds that optimally merging precipitation while simultaneously minimizing any surface parameter error [soil moisture (SM), temperature, runoff, or evapotranspiration (ET)] minimized errors in other LSM fluxes. Section 2 outlines the method, sections 3 and 4 present the results and discussion, and section 5 summarizes the conclusions. 2. Method Different precipitation datasets have been optimally

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Alan J. Geer, Peter Bauer, and Christopher W. O’Dell

use a method such as the ICA operationally, a much faster approach is the Monte Carlo ICA (McICA; Pincus et al. 2003 ), which has revolutionized the calculation of broadband fluxes and heating rates in GCMs, including the ECMWF ( Morcrette et al. 2008 ). However, because it introduces large random errors in simulations at any one frequency, it is not appropriate for the simulation of satellite observations. Recently, O’Dell et al. (2007) developed an “optimal” cloud overlap for microwave

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Shelley L. Knuth, Gregory J. Tripoli, Jonathan E. Thom, and George A. Weidner

evaporation (sublimation minus deposition), Q represents snow loss or gain due to the horizontal flux of snow, and R represents the loss of snow from meltwater. The term P corresponds to snowfall from synoptic or mesoscale systems, orographic forcing, or clear sky precipitation (diamond dust) ( Bromwich 1988 ). Net evaporation is defined as snow depth loss from surface sublimation or snow depth gain from surface deposition of hoar frost. Hoar frost, in general, is not a substantial contributor to

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Zhong Liu, Hualan Rui, William Teng, Long Chiu, Gregory Leptoukh, and Steven Kempler

phosphorus co-limit nitrogen fixation in the eastern tropical North Atlantic. Nature , 429 , 292 – 294 . Erratum, 435, 232 . Nijssen , B. , and D. P. Lettenmaier , 2004 : Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Preciptation Measurement satellites. J. Geophys. Res. , 109 , D02103 . doi:10.1029/2003JD003497 . Peck , D. R. , and B. C. Congdon , 2005 : Colony-specific foraging behaviour and coordinated divergence of

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J. J. Shi, W-K. Tao, T. Matsui, R. Cifelli, A. Hou, S. Lang, A. Tokay, N-Y. Wang, C. Peters-Lidard, G. Skofronick-Jackson, S. Rutledge, and W. Petersen

shortwave schemes recently added into WRF, and discussed in section 2 , were adopted to provide longwave and shortwave parameterizations that interact with the atmosphere. The planetary boundary layer parameterization for this study was the Mellor–Yamada–Janjić ( Mellor and Yamada 1982 ; coded and modified by Dr. Janjić for the NCEP Eta Model) level-2 turbulence closure model for the full range of atmospheric turbulent regimes. The surface heat and moisture fluxes (from both ocean and land) were

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Chinnawat Surussavadee and David H. Staelin

commonly observed over western U.S. prairies and similar terrain where warm dry air increases evaporation rates. Alternatively, hydrometeor mass densities H aloft can also be relatively larger if those hydrometeors fall slowly because of strong updrafts or because of their large aerodynamic drag coefficients (e.g., snowflakes), whereas the same melted mass flux (kg m −2 s −1 ) falls more swiftly at lower values of H nearer the surface. However, there is no evidence in the MM5 simulations that snow

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B. J. Sohn, Hyo-Jin Han, and Eun-Kyoung Seo

, Japan, as a visiting professor. This work was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006-2103. REFERENCES Arkin , P. A. , and P. Xie , 1994 : The Global Precipitation Climatology Project: First algorithm intercomparison project. Bull. Amer. Meteor. Soc. , 75 , 401 – 419 . Betts , A. K. , and C. Jakob , 2002 : Evaluation of the diurnal cycle of precipitation, surface thermodynamics, and surface fluxes in the ECMWF model

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Frank S. Marzano, Domenico Cimini, Tommaso Rossi, Daniele Mortari, Sabatino Di Michele, and Peter Bauer

humidity and additional input variables (e.g., tendencies and surface fluxes). Observations, y true , are simulated by processing the true profiles with the RTTOV model as described above. To produce realistic observations ( y o ), noise is added to y true . The noise corresponds to the radiometric noise that has been specified by Schlüssel et al. (2008) . The observation error covariance matrix 𝗖 ε is assumed to be diagonal and derived from the observation statistics in Bauer and Di Michele

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