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Gabriel G. Katul and Marc B. Parlange

minimize the number of modes, so that thenumber of wavelet coefficients needed for turbulenceanalysis is very large. However, this large number ofwavelet coefficients is essential to obtain reliable spatial statistics of instantaneous realizations of turbulentflows. In this study, we make use of orthonormal wavelettransformations to analyze the structure of turbulencein space and scale close to the land surface. Rapid measurements of temperature and specific humidity are collected at 80 cm over a

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Surabi Menon, Anthony D. Del Genio, Dorothy Koch, and George Tselioudis

multiple regression relationships to predict N for land, N Land , and ocean, N Ocean , are where sulfate, OM, and sea salt are the mass concentrations in μ g m −3 and N is in cm −3 ; N predicted using the above equations is more sensitive to changes in sulfate than to OM due to the higher slope for sulfates, however, the AIE has not been evaluated separately for either sulfates or OM alone. These regressions differ from the commonly used empirical relationships given in Boucher and Lohmann

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Y. Hayashi and D. G. Golder

planetary waves in the tropics, the effects of topography,midlatitude disturbances and condensational heat are eliminated one by one from a GFDL general circulation model during the period June and July. The time development and three-dimensional propagationof waves are examined by a space-time spectral analysis using the maximum entropy method. It is found that the characteristic scale and period of Kelvin and mixed Rossby-gravity waves do notdepend on land-sea contrast or the zonal variation of sea

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E. A. Smith, J. E. Lamm, R. Adler, J. Alishouse, K. Aonashi, E. Barrett, P. Bauer, W. Berg, A. Chang, R. Ferraro, J. Ferriday, S. Goodman, N. Grody, C. Kidd, D. Kniveton, C. Kummerow, G. Liu, F. Marzano, A. Mugnai, W. Olson, G. Petty, A. Shibata, R. Spencer, F. Wentz, T. Wilheit, and E. Zipser

to use the results from the different algorithms to form a “consensus” algorithm. This product was then evaluated by a GPCP merged rain gauge dataset over land and a west Pacific atoll rain gauge dataset over ocean; see Morrissey et al. (1994) for a description of the PIP-1 validation datasets. Of the 17participating algorithms, 16 were designed for application with SSM/I measurements, while the algorithm of Spencer (1993) was designed for measurements from the National Oceanic Atmospheric

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Pragallva Barpanda and Tiffany Shaw

(1999) ]. The SH exhibits a much weaker seasonal evolution of stationary eddy MSE flux ( Fig. 2d ) consistent with its smaller land area. The seasonal evolution of the MSE flux in the NCEP reanalysis is robust when compared to ERA-Interim ( Dee et al. 2011 ) covering the period 1979–2015 (cf. Figs. 2 and 3 with Figs. B1 and B2 ). The NCEP reanalysis data are used to examine storm-track shifts in response to seasonal insolation and the El Niño–Southern Oscillation (ENSO). The ENSO response is

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G. J. Steeneveld, B. J. H. van de Wiel, and A. A. M. Holtslag

-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) mesoscale model (MM5), version 3.5, with U.S. Geologic Survey (USGS) land use classification] for the CASES-99 campaign for 23–26 October 1999. We use both the first-order closure model with prescribed diffusion profile form, medium-range forecast (MRF) ( Hong and Pan 1996 ), and a 1.5-order TKE- l closure model, ETA ( Janjić 1990 ). Horizontal resolution is set to 1 km for the innermost grid nest (in total three nested

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Robert G. Gallimore

predicted atmosphericwater vapor used in the determination of the solar and longwave fluxes; 2) a land albedo and surface energybudget dependency on explicit model calculations of snowfall, snow melt and snow accumulation; 3) a'lapserate dependency for the Iongwave emission, sensible heating and mean energy transport; and 4) separatespecification for mean and eddy energy and water vapor transports. . The sensitivity of the model energy and water vapor budgets is similar to that obtained

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Russell Qualls and Thomas Hopson

) allows the use of (3) to estimate H from land surfaces by means of remotely sensed surface temperatures and ground-based measurements of wind speed, air temperature, and an estimate of atmospheric stability. A simplified form of this method, in which χ = LAI and β = 1, has been applied with surface temperatures obtained from TIMS over FIFE on 11 July1987 by Qualls and Brutsaert (1996a) . Here, the more complete method where χ is given by (8) is applied to as many as 20 surface flux

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Xiping Zeng, Wei-Kuo Tao, Minghua Zhang, Christa Peters-Lidard, Stephen Lang, Joanne Simpson, Sujay Kumar, Shaocheng Xie, Joseph L. Eastman, Chung-Lin Shie, and James V. Geiger

snowpack density), canopy water content, and the energy flux and water flux terms of the surface energy and surface water balances. The LSM land surface parameters were initialized with University of Maryland 1-km datasets for vegetation and land–sea masks ( Hansen et al. 2000 ). Climatological datasets were ingested in order to initialize other vegetation parameters such as albedo and green vegetation fraction. Soils types were set using the State Soil Geographic Database for State ( Soil Survey Staff

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Barry H. Lynn, Alexander P. Khain, Jian Wen Bao, Sara A. Michelson, Tianle Yuan, Guy Kelman, Daniel Rosenfeld, Jacob Shpund, and Nir Benmoshe

with the 1D SST model, only the initial SSTs at 0000 UTC 21 August were used, and the SSTs were calculated thenceforth using the 1D model. The lapse rate below the mixed layer was 0.14 K m −1 . The configuration of the initial aerosol distributions is shown in Fig. 5 . The design of the aerosol distribution over the computational area in Exp. 1 is presented in Fig. 5a . In Exp. 1, high aerosol concentration was assumed over land (continental aerosols) and low aerosol concentration over the ocean

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