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

precipitation rates might occur if global temperature will increase, as is commonly accepted, thus causing a positive feedback loop with a possible increase of weather extremes and durations of flood and drought episodes ( Levitus et al. 2001 ; Ziegler et al. 2003 ). This scenario clearly raises the need for accurate, stable, and continuous space–time sampling of atmospheric precipitation over land and ocean toward a precise estimate of accumulated water (and related phenomena such as moisture transport

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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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Hilawe Semunegus, Wesley Berg, John J. Bates, Kenneth R. Knapp, and Christian Kummerow

products at daily to monthly time scales. SSM/I data are also used by the cryospheric scientific community to monitor Arctic sea ice cover and detect contemporary changes in sea ice and ice sheets, which are critical for understanding the role of the Arctic in the global climate system ( Serreze et al. 1990 ; Belchansky et al. 2005 ). Other applications that have been developed from SSM/I include the estimation of land surface temperature, soil moisture content, and oceanic surface wind speed ( Weng

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

satellite observations (e.g., Kummerow 1998 ). Here, even when two fields of view contain the same mass of rain or cloud, variations in fractional cloudiness can cause large differences in measured radiances. Rain- and cloud-affected microwave radiances are assimilated at the European Centre for Medium-Range Weather Forecasts (ECMWF; Bauer et al. 2006a , b ), improving forecasts of tropical moisture and wind ( Kelly et al. 2008 ). However, large biases between simulated and observed brightness

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

predominantly southerly wind direction from flow off the dry southern Ross Ice Shelf, which results in a lack of available moisture in the region to produce precipitation. Additionally, a flat topography in this region yields little opportunity for forcing mechanisms to generate precipitation. The Nascent site is also in a location where mesoscale and synoptic systems are not as frequent ( Carrasco et al. 2003 ; Simmonds et al. 2003 ). Those systems that do affect this location are also likely to

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Cristian Mitrescu, Tristan L’Ecuyer, John Haynes, Steven Miller, and Joseph Turk

; Mitrescu et al. 2007 ; Haynes et al. 2009 ). For land surfaces, however, the spread is much higher because of a wider seasonal variation in canopy cover and texture, but also because of terrain roughness and composition including moisture content. A model describing surface returns for these areas is much more complicated and is not essential for the scope of the present work. e. Melting layer model As ice crystals fall through the freezing layer, they start to melt. Because of a lack of a priori

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

ERA-Int exceeds HOAPS partly by more than 2 mm day −1 (up to 50%). The issue of excessive tropical precipitation is already known from the former ERA-40 reanalysis. However, the tropical moisture budget in ERA-Int appears to be improved over ERA-40, for which this positive bias was even stronger ( Simmons et al. 2007 ). Except for the large tropical biases, the deviations between HOAPS and ERA-Int are small and remain mostly below 1 mm day −1 (<20%). HOAPS precipitation values are noticeably

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