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H. W. Barker, A. Marshak, W. Szyrmer, J-P. Blanchet, A. Trishchenko, and Z. Li

1. Introduction Observational estimates of cloud optical depth τ are sought for many reasons, most of which hinge on the important role played by τ in determining earth's radiation budget and climate ( Mitchell et al. 1995 ). Though satellite estimates of τ can be used to help assess global climate models ( Han et al. 1998 ), they are not perfect ( Min and Harrison 1996 ; Barker et al. 1998 ; Li et al. 1999 ) and must be validated independently. Moreover, satellites often provide

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Yasu-Masa Kodama

atmosphere and GCMs simulating the real atmosphere, the STCZs are affected by not only the monsoons but many other factors, including their surrounding topography and land–sea distribution. To exclude these geographical influences, we perform experiments using an aqua-planet GCM without any land, in which a monsoonlike, that is, strong and localized atmospheric heat source, is adopted. As shown later, a heat source away from the equator makes a rainfall zone in the subtropics similar to the STCZs. We

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Juerg Schmidli and Richard Rotunno

5 km to the top of the domain. The Coriolis force is turned off. Fourth-order computational mixing with a mixing coefficient equal to 10 −3 s −1 is used. The thermal forcing is determined by the incoming radiation and the land surface characteristics. To ensure a symmetric forcing of the valley wind system, the model domain is located at the equator and the time of the year is set to the spring equinox (21 March). The solar constant is reduced to 700 W m −2 , so that the resulting surface

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Martin L. M. Wong and Johnny C. L. Chan

used by Wong and Chan (2004) . The vortex is then spun up for a 24-h period after which it attains an intensity of ∼976 hPa. The axisymmetric part of the mass and wind fields then form the initial conditions of the conceptual experiments (see below for details) with the TC center placed at the center of the model domain. In all the experiments, a portion of the surface is specified to be land of roughness length 0.5 m (sea roughness depends on wind strength) so that the TC is located at 50, 100

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Weiqing Qu, A. Henderson-Sellers, A. J. Pitman, T. H. Chen, F. Abramopoulos, A. Boone, S. Chang, F. Chen, Y. Dai, R. E. Dickinson, L. Dümenil, M. Ek, N. Gedney, Y. M. Gusev, J. Kim, R. Koster, E. A. Kowalczyk, J. Lean, D. Lettenmaier, X. Liang, J.-F. Mahfouf, H.-T. Mengelkamp, K. Mitchell, O. N. Nasonova, J. Noilhan, A. Robock, C. Rosenzweig, J. Schaake, C. A. Schlosser, J.-P. Schulz, A. B. Shmakin, D. L. Verseghy, P. Wetzel, E. F. Wood, Z.-L. Yang, and Q. Zeng

1. Introduction In order to improve the understanding of the parameterization of land surface processes, the Project for Intercomparison of Land-Surface Parameterization Schemes (PILPS) was initiated in 1992 as a World Climate Research Programme project. The overall goals of PILPS are to improve the performance of land-surface schemes, as they are used in climate and weather prediction models. The progress to date and planned future activities of PILPS are described in detail by Henderson

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K. Franklin Evans, Alexander Marshak, and Tamás Várnai

, in our statistical retrievals the neural net was trained on a subset of the same dataset. What if we train the neural network with some generic cumulus dataset? Will it be applicable to all cumulus clouds? To begin answering this question we performed retrieval experiments using neural networks trained on land cumulus clouds and used on RICO cumulus, and vice versa. We consider this as a very first step toward applying our retrieval algorithm to real MISR data rather than to simulated data. The

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Samuel S. P. Shen, Nancy Tafolla, Thomas M. Smith, and Phillip A. Arkin

-level agreement shown in Fig. 8 still implies the excellent skill of using the GHCN land precipitation data to predict the oceanic precipitation. The agreement also implies the high quality of both GPCP and GHCN data. This comparison result is hence a validation of our reconstruction model for the historical global precipitation and shows the appropriateness of our approach. Finally, we look at the time series of the quasi-global-average annual precipitation from 1900 to 2011 in Fig. 1 . The positive trend

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Rong Lu and Richard P. Turco

VOL. 51, NO. 15TABLE 1. Parameters used for simulations.Roughness length over land (z0)Roughness length over water (zo)Coriolis parameter (f)Horizontal diffusivity (Kh)Solar constant (So)Latitude (3)Sea surface temperature (T~04.0 cm0.5 cm7.27 x 10-s s-~2.5 x 103 m2 s-~1360 W m-230-N18.3-CAngell et al. 1976). Inert chemical tracers, such as SF6,have been released to determine pollutant source-receptor relationships, thereby identifying pollutant pathways (Drivas and Shair 1974; Lamb et al. 1978

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Jean-Marcel Piriou, Jean-Luc Redelsperger, Jean-François Geleyn, Jean-Philippe Lafore, and Françoise Guichard

as observed during GATE, as well as drier cases (EUROCS sensitivity of moist convection to environmental humidity, EUROCS diurnal cycle of deep convection over land). 1) GATE Phase III We have used grid-scale upper-air datasets analyzed by K. V. Ooyama et al. (1985, personal communication), for the period of GATE Phase III, available at the National Center for Atmospheric Research. A presentation of this data is given in Esbensen et al. (1982) . The single-column model (SCM) is initialized with

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Alan K. Betts

. Data from a field experiment over Venezuela are used to illustrate mean subcloud layer structure and to derive heat and moisture flux profiles and model parameters from a simple budget analysis. The data give (a,B)= (0.11, 0.41) and correspondingly, (a~,~) = (0.08, 0.21) based on virtual static energy fluxes and profiles.During the budget time period (centered on local noon over land) the subcloud layer warms and dries witha corresponding rise of cloud base. The steady-state transition layer model

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