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

algorithms designed to use satellite-estimated surface rain rates and precipitation profiles have been developed, intercompared, validated, and applied in the past decade (see Tao et al. 2006 , 2007 ). They are the Goddard convective–stratiform heating (CSH) algorithm, the hydrometeor heating (HH) algorithm, the Goddard profiling heating (GPROF heating) algorithm, 1 the spectral latent heating (SLH) algorithm, and the precipitation radar heating (PRH) algorithm. The CSH algorithm only requires

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

attempts to “train” a passive microwave heating algorithm using only cloud-resolving model simulations resulted in high biases of estimated upper-tropospheric precipitation and heating because of high biases in the precipitation simulations and synthesized microwave signatures (see Lang et al. 2007 ). In GO06 , the high biases in estimated precipitation and heating profiles were overcome using globally distributed spaceborne radar profiles of precipitation–heating instead of a limited number of cloud

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

profile and precipitation profile. The spectral latent heating (SLH) algorithm has been developed for the TRMM PR using a CRM ( Shige et al. 2004 , 2007 ; hereafter, Part I and Part II ). The previous methods considers only rain types (convective or stratiform), based on the assumption that the shape of the overall MCS heating profile is determined by the relative amounts of convective and stratiform heating ( Tao et al. 1993a ; Schumacher et al. 2004 ). In these bulk methods, the variation of

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

. 1998 ; Tao et al. 2006 ). In particular, the TMI and the TRMM Precipitation Radar (PR), which have been providing the distribution of rainfall characteristics throughout the tropics since 1997, are the basis for two different LH datasets: the TRMM CSH and TMI 2A12. These two datasets arise from two independent algorithms: the convective and stratiform heating (CSH; Tao et al. 1993 ) and the Goddard profiling (GPROF) heating ( Olson et al. 1999 , 2006 ), respectively. The relationships between LH

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Yukari N. Takayabu, Shoichi Shige, Wei-Kuo Tao, and Nagio Hirota

dataset of spaceborne precipitation radar, which has a 13.8-GHz Ku-band frequency. In this study, using the TRMM PR data and conversion tables between precipitation and latent heating, we utilized Q 1 − Q R [Eq. (1) ] as a large-scale diabatic heating rate obtained from the SLH algorithm ( Shige et al. 2004 , 2007 , 2008 ): Here, Q 1 is the apparent heat source invented by Yanai et al. (1973) ; Q R is the radiative heating; and s is the dry static energy, s = C p T + gz . The SLH

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

contribution of LH to the large-scale distribution of atmospheric heat sources (e.g., Yanai et al. 1973 ; Luo and Yanai 1984 ; Lin and Johnson 1996 ; Schumacher et al. 2007 ). Information on the large-scale distribution of LH profiles is also expected from TRMM PR observations ( Simpson et al. 1988 ) because the precipitation profiles observed by PR are closely related to LH profiles, which cannot be remotely sensed by satellite instruments in a straightforward way. Several algorithms have been

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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

-dimensional structure of diabatic heating associated with precipitation and its temporal variability over the entire tropics became possible. Several ways of utilizing the TRMM data to calculate diabatic heating profiles have been proposed and implemented. Tao et al. (2001) presented an intercomparison of profiles from three diabatic heating algorithms, the hydrometeor heating (HH; Yang and Smith 1999a , b ), convective–stratiform heating (CSH; Tao et al. 1993 ), and the Goddard Profiler heating (GPROF; Olson

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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

availability of latent heating (LH) estimates based on the Tropical Rainfall Measuring Mission (TRMM; Tao et al. 2006 ) provide an unprecedented opportunity to investigate heating structures associated with the MJO convection. By using LH estimates based on an earlier version of a spectral latent heating algorithm ( Shige et al. 2004 ), Morita et al. (2006) examined vertical heating structures of the MJO based on a composite analysis. In their study, two centers of maximum heating at about 3 and 7 km

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

model, which carried an RMS error of roughly 8.8 km. While looking at the geographical distributions of these heating errors we noted that over nonprecipitating areas large errors in heating Q 1 arise from the radiative transfer algorithms. The nature of these errors were very persistent, that is, systematic, and the multimodel superensemble was able to reduce these errors quite drastically. The strength of this study reconfirms the great strength of the multimodel superensemble. In a series of

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