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Tristan S. L’Ecuyer and Greg McGarragh

System (CERES) clouds and radiative swath (CRS) product ( Wielicki et al. 1996 ) offers estimates of Q R that are constrained to match top of the atmosphere (TOA) flux measurements but with reduced temporal sampling, whereas Cloudsat’s level-2B radiative fluxes and heating rates algorithm (2B-FLXHR; L’Ecuyer et al. 2008 ) offers improved cloud boundary information and spatial resolution but at greatly reduced spatial and temporal sampling. All of these algorithms are built on the same basic

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Wei-Kuo Tao, Stephen Lang, Xiping Zeng, Shoichi Shige, and Yukari Takayabu

dominated by phase changes between water vapor and small liquid or frozen cloud-sized particles. It consists of the condensation of cloud droplets, evaporation of cloud droplets and raindrops, freezing of cloud droplets and raindrops, melting of snow and graupel/hail, and the deposition and sublimation of ice particles. In addition, eddy heat flux convergence from cloud motions can also redistribute the heating or cooling vertically and horizontally. LH cannot be measured directly with current

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Shaocheng Xie, Timothy Hume, Christian Jakob, Stephen A. Klein, Renata B. McCoy, and Minghua Zhang

top-of-the-atmosphere (TOA) observations as constraints to adjust atmospheric state variables from soundings by the smallest possible amount to conserve column-integrated mass, moisture, and static energy so that the final analysis dataset is dynamically and thermodynamically consistent. The required observation constraints include the surface and TOA radiative fluxes, surface latent and sensible heat fluxes, and surface precipitation. The variational analysis has been successfully used to process

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Richard H. Johnson, Paul E. Ciesielski, Tristan S. L’Ecuyer, and Andrew J. Newman

-dimensional structure of clouds and precipitation in the atmosphere. Aerosols are prescribed using a static climatology of monthly distributions from the Global Aerosol Climatology Project (GACP). These fields provide input to a broadband radiative transfer model that simulates vertical profiles of upwelling and downwelling longwave and shortwave radiative fluxes and their convergence defines the vertical profile of atmospheric radiative heating Q R . A comprehensive description of the uncertainty characteristics

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Mircea Grecu, William S. Olson, Chung-Lin Shie, Tristan S. L’Ecuyer, and Wei-Kuo Tao

source can also be defined in terms of sources within the averaging area: where the first three terms on the right-hand side are the average latent heating due to clouds and precipitation, the fourth and fifth terms (in the fourth set of parentheses) are the horizontal and vertical convergence of eddy sensible heat flux, the last term is the radiative heating rate ( Q R ), and the primes indicate eddy perturbations with respect to the horizontal average. In the present study, cloud-resolving model

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Xianan Jiang, Duane E. Waliser, William S. Olson, Wei-Kuo Tao, Tristan S. L’Ecuyer, Jui-Lin Li, Baijun Tian, Yuk L. Yung, Adrian M. Tompkins, Stephen E. Lang, and Mircea Grecu

sublimation, respectively. Term I in Eq. (1) represents the latent heat due to phase changes, and term II is the vertical and horizontal eddy sensible heat flux convergence. It is noted that both TRMM/TRAIN Q 1 − Q R and TRMM/CSH Q 1 are not estimated under conditions of zero surface rainfall. As a result, the radiative cooling effect during nonrainy days could be largely underestimated in both TRMM heating estimates. Therefore, in order to facilitate a more direct comparison between these two

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Shoichi Shige, Yukari N. Takayabu, Satoshi Kida, Wei-Kuo Tao, Xiping Zeng, Chie Yokoyama, and Tristan L’Ecuyer

heating and maximum stratiform cooling exist. The heating estimates at the melting level are very sensitive to the estimated fraction of stratiform rainfall from the PR data. Thus sampling errors may account for the discrepancy at the middle level. At the upper level, the radiative cooling must be balanced by deep convection. Since the saturation mixing ratio is low at the upper level, deep convection heats the upper troposphere largely by eddy heat flux convergence ( Sui et al. 1994 ; Mapes 2001

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T. N. Krishnamurti, Arindam Chakraborty, and A. K. Mishra

. The two diagrams show, respectively, the predicted maxima of heating distributions for hours 12–36 and hours 36–60. The units of heating are here expressed in K day −1 . The first 12 hours are not included in the analysis presented here; for reasons of the initial spinup within the three models that utilize different convection schemes, those are different from the Tiedtke (1989) mass flux scheme, which was implicit in the data assimilation that defines our initial states and came from the

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Samson Hagos, Chidong Zhang, Wei-Kuo Tao, Steve Lang, Yukari N. Takayabu, Shoichi Shige, Masaki Katsumata, Bill Olson, and Tristan L’Ecuyer

presence of precipitation. In other words, the diabatic heating associated with precipitation is equal to the total diabatic heating if the precipitation is greater than zero and is zero otherwise. This is because the heating profiles in the TRMM products are available only in regions with precipitation (and hence dominantly latent heating); this conditioning would eliminate the effects of diabatic heating in grid points where precipitation is absent (where sensible heat fluxes and radiative cooling

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