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  • 16th International Symposium for the Advancement of Boundary-Layer Remote Sensing (ISARS 2012) x
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A. B. White, M. L. Anderson, M. D. Dettinger, F. M. Ralph, A. Hinojosa, D. R. Cayan, R. K. Hartman, D. W. Reynolds, L. E. Johnson, T. L. Schneider, R. Cifelli, Z. Toth, S. I. Gutman, C. W. King, F. Gehrke, P. E. Johnston, C. Walls, D. Mann, D. J. Gottas, and T. Coleman

from HMT-West is the role that atmospheric rivers (ARs), narrow regions of enhanced water vapor transported in the warm sectors of midlatitude cyclones, play in creating heavy precipitation that can lead to flooding ( Ralph et al. 2004 , 2006 ; Neiman et al. 2008 ; Guan et al. 2010 ; Lavers et al. 2011 ; Moore et al. 2012 ). As defined by Ralph et al. 2004 , ARs are long (>2000 km), relatively narrow (<1000 km), and concentrated (>2 cm of integrated water vapor) moisture plumes. Globally, ARs

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Leslie M. Hartten and Paul E. Johnston

index fluctuations with a scale size of 16.5 cm, one-half of the transmitted wavelength (i.e., from Bragg scattering), and from Rayleigh scattering by falling hydrometeors. The index of refraction is a nonlinear function of pressure, temperature, and moisture ( Gage and Balsley 1978 ); fluctuations on this scale are created by turbulence. The 915-MHz profiler deployed during these cruises was a five-beam, electronically stabilized, phased-array system ( Law et al. 2002 ); the same radar was present

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C. R. Wood, R. D. Kouznetsov, R. Gierens, A. Nordbo, L. Järvi, M. A. Kallistratova, and J. Kukkonen

10 m. The 10-Hz sonic-anemometer time series, of the three components of wind and sonic temperature (i.e., virtual temperature), were used to calculate turbulent fluxes: friction velocity and , in addition to the Obukhov length . All flux data were corrected for a range of standard effects, including moisture ( Nordbo et al. 2012 ). Sonic data were subset into two groups: one with basic quality assurance (only de-spiked) and one with stringent quality assurance according to flux

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Qing Yang, Larry K. Berg, Mikhail Pekour, Jerome D. Fast, Rob K. Newsom, Mark Stoelinga, and Catherine Finley

boundary layer (PBL) parameterizations play an important role in simulating low-level winds and PBL structures ( Berg and Zhong 2005 ; Shin and Hong 2011 ) and are used to parameterize subgrid vertical fluxes of momentum, heat, and moisture. Three PBL parameterization schemes have been selected to test the sensitivity of the WRF model's performance to the choice of PBL schemes: Mellor–Yamada–Janjić (MYJ; Janjić 1994 ), University of Washington (UW; Bretherton and Park 2009 ), and Yonsei University

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Valery M. Melnikov, Richard J. Doviak, Dusan S. Zrnić, and David J. Stensrud

clouds. The potential of the WSR-88D to map cloud structures is shown by Melnikov et al. (2011b) . However, no clouds are reported at lower levels, whereas the radar shows a layer near 2 km AGL. The radar operator [virtual machine monitor (VMM)] visually confirmed the lack of low-level clouds at this time during the data collection period. Furthermore, the corresponding 1700 UTC RUC analysis has relative humidity (RH) values below 50% between 2 and 3 km AGL, suggesting that sufficient moisture is

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S.-E. Gryning, E. Batchvarova, and R. Floors

-resolution analysis improved the skill of the regional model for pressure, temperature, geopotential height, wind, and humidity. Another way to limit this drift relies on periodic reinitialization of the simulations ( Lo et al. 2008 ). Because considerable spinup time is required for each reinitialization, such an approach is computationally expensive. Von Storch et al. (2000) showed that some slowly varying meteorological parameters (e.g., soil moisture) that are essential for long-term calculations might be

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