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

). 2. Superensemble methodology The superensemble methodology combines a set of multimodel forecasts to construct a single consensus forecast ( Krishnamurti et al. 1999 , 2000 ). In this methodology, the models are combined with different weights, which is unlike a simple ensemble construction. The weights of the models are determined based on their past performance. The details of this methodology are as follows. The time line of the model dataset is divided into two parts: a training phase and a

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

described. The results are shown and discussed in section 3 . In section 4 , the performance of the new CSH algorithm is presented. Finally, the major results and future work are summarized in section 5 . 2. Numerical modeling and data a. Goddard cumulus ensemble model The GCE is a CRM and is used to simulate clouds/cloud systems and their associated heating budget. The model is nonhydrostatic. It accounts for both absorption and scattering for solar radiation and both emission and absorption for

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

three-dimensional (3D) CRM simulations for multiday periods with large horizontal domains becoming increasing prevalent. Although real clouds and cloud systems are 3D, a 3D CRM does not automatically give more realistic simulation than a 2D CRM does. This is because the results of the simulation depend very strongly on incomplete and uncertain parameterizations of ice microphysical processes. Considerable effort has been devoted in recent years to evaluating the performance of CRMs using the TRMM

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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

differences in MJO variability exhibited by the models is quite large ( Slingo et al. 1996 ; Zhang et al. 2006 ; CLIVAR Madden–Julian oscillation Working Group 2009 ). Similar to the mean fields, there is a fair amount of discrepancy with regards to the depth of the MJO heating variations, and this could have important implications on both the theories and the model performance in terms of propagation characteristics and teleconnection properties. While we have considerable information of the large

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Tristan S. L’Ecuyer and Greg McGarragh

regional estimates of Q 1 have been made from high time and space resolution observations of temperature and humidity profiles from enhanced sounding networks (e.g., Johnson and Ciesielski 2002 ; Zhang et al. 2001 ), global diabatic heating estimates on larger scales have, until now, primarily relied on model reanalyses. Given recent emphasis on evaluating model performance using observations, a strictly observation-based estimate of large-scale diabatic heating is highly desirable. As a result

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Yasu-Masa Kodama, Masaki Katsumata, Shuichi Mori, Sinsuke Satoh, Yuki Hirose, and Hiroaki Ueda

convective processes. Although processes of positive LH (i.e., moisture condensation on cloud droplets) cannot be observed by PR, two algorithms using TRMM PR data have been proposed to bypass this problem: one is a PRH algorithm ( Satoh and Noda 2001 ; Satoh 2004 ), and the other is a spectral latent heating (SLH) algorithm ( Shige et al. 2004 , 2007 ). The SLH algorithm evaluates LH profiles using lookup tables, prepared by simulations of a cloud-resolving model (CRM), whereas the PRH algorithm is a

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Manuel D. Zuluaga, Carlos D. Hoyos, and Peter J. Webster

vertical and horizontal distribution of LH (e.g., Hartmann et al. 1984 ; DeMaria 1985 ; Lau and Peng 1987 ; Nakazawa 1988 ; Sui and Lau 1989 ; Emanuel et al. 1994 ; Yanai et al. 2000 ; Chiang et al. 2001 ). These studies show that improved vertical distributions of LH allow a better representation of tropical east–west circulations (e.g., the Walker circulation) by models other than those using simple heating functions, such as Webster (1972) and Gill (1980) . However, even the more advanced

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