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

widespread examination of the continent. During a field study conducted between January 2005 and October 2006 across the Ross Ice Shelf and on icebergs in the Ross Sea, seven AWS were fitted with automated acoustic depth gauges to provide the first extensive network of ground-based snow depth change measurements in Antarctica. The full suite of AWS measurements was then used to determine the relative influences of two of the factors—horizontal snow transport and precipitation—on changes in snow depth

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Long S. Chiu and Roongroj Chokngamwong

was removed in RSS V6, and the oceanic products are now called UMORA ( Hilburn and Wentz 2008 ). Additional features of the V6 T b data include a land/sea flag and a sea ice flag attached to each pixel. There are three major differences in the METH V4 and V6 algorithm. First, for quality controls of T b data, all pixel T b values of less than 50 K or more than 350 K for all channels are first identified. In the V4 algorithm, the whole scan is rejected if any one pixel from any channel is

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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

Jan to 0000 UTC 23 Jan 2007. Fig . 14. (a) Vertical profiles of the C3VP aircraft-measured ice and liquid water contents between 0600 and 0624 UTC 22 Jan 2007. The vertical axis represents heights above mean sea level. (b) Vertical profiles of area-averaged cloud species (using only grid points in the vicinity of the aircraft flight path) from the model for the period between 0600 and 0700 UTC 22 Jan 2007. Fig . 15. Vertical cross sections of WRF-simulated temperature (°C) and cloud ice plus snow

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

radiation penetrates clouds, resulting in a direct signal of the sea surface state at the satellite receiver with only moderate influence by atmospheric hydrometeors. With the additional use of SST data, estimates of the sea surface evaporation become possible. Moreover, it is possible to detect precipitation by the radiation that is emitted from hydrometeors. At higher microwave frequencies the radiation is strongly influenced by scattering at ice particles, which are an additional indicator for

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

) provides sensitivity to lighter precipitation (missed by conventional terrestrial weather radar systems) and even the smaller ice and/or liquid particles composing the cloud itself. From its satellite vantage point, the Cloud Profiling Radar (CPR) beam is unobstructed by elevated terrain, in contrast to ground-based radars. Although CloudSat provides global coverage, its nadir-only viewing geometry (nonscanning) results in “curtain” observations (2D slices through the atmosphere) as opposed to the 3D

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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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Daniel Vila, Ralph Ferraro, and Hilawe Semunegus

) procedures have been performed to remove unrealistic values in the input data (i.e., antenna temperature). The first purpose of this study was that, although the rain algorithm used at FNMOC and for GPCP are the same, some additional parameterizations and new screening processes (e.g., sea/ice detection) were added for improving monthly products delivered to GPCP ( Ferraro 1997 ). With more than 20 years of SSM/I data now available, enhanced QC procedures can be applied to improve the products

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Chinnawat Surussavadee and David H. Staelin

corrections were generally incorporated in the data presented in the next section. 6. Global observations of precipitation This surface-classification-corrected precipitation-rate retrieval algorithm was evaluated by comparisons with known meteorological phenomena and other global datasets. For example, Fig. 4 presents retrievals over the North Pole for two consecutive days in 2004 as observed from N16 . Light pink indicates sea ice detected by AMSU, and dark pink indicates altitudes that were too high

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

ECMWF. For improved accuracy, inputs to the radiative transfer code came directly from the FG model rather than the simplified 1D operators normally used in rainy 1D + 4D-Var assimilation. SSM/I observations were restricted to latitudes between 60°N and 40°S and the ocean only, avoiding land and sea ice surfaces. Latitudes poleward of 40°S were excluded as a result of a bias problem that will be explained later. All valid observations were used, including those normally sent through the “clear

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Song Yang, Fuzhong Weng, Banghua Yan, Ninghai Sun, and Mitch Goldberg

calibration of DMSP SSM/Is: F-8 to F-14, 1987-1997. IEEE Trans. Geosci. Remote Sens. , 37 , 418 – 439 . Ebert , E. E. , and M. J. Manton , 1998 : Performance of satellite rainfall estimation algorithms during TOGA COARE. J. Atmos. Sci. , 55 , 1537 – 1557 . Ferraro , R. , F. Weng , N. C. Grody , and A. Basist , 1996 : An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc. , 77

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