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

database for Q 1 . Day 2 forecasts follow similarly. To compare model-generated heating profiles and to construct superensemble forecasts, we have interpolated observed profiles of heating rates to the model grid (∼0.7°) using bilinear interpolation technique. Figure 1 shows that because of the narrow swath of the TMI instrument there is a wide gap between two successive passes. This gap is greater near the equator and decreases toward the poles. Because of the precession of the TMI orbit, the full

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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

also been detected (e.g., chlorophyll: Waliser et al. 2005 ; ozone: Tian et al. 2007 ; aerosols: Tian et al. 2008 ). The quasi-periodic occurrence of the MJO provides a primary source for the predictability of tropical atmosphere on subseasonal time scales, which may bridge the forecasting gap between medium- to long-range weather forecast and short-term climate prediction (e.g., Waliser et al. 2006 ; Jiang et al. 2008 ). Therefore, the improved understanding of the fundamental features of 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

et al. 1999 ) for February of 1998. Based on the results of the spectral precipitation statistics of Takayabu (2002) , Shige et al. (2004 , 2007 , 2008) also developed the spectral latent heating (SLH) algorithm and data. Grecu et al. (2009) developed a similar technique to utilize TMI data to remedy the undersampling associated with the narrow swath of the PR using a training algorithm (TRAIN). Yet another estimate from TRMM is based on the precipitation radar heating (PRH) algorithm by

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

treating the entire atmospheric column as a single layer, scaling representative convective and stratiform heating profiles from cloud-resolving model simulations by the net convective and stratiform precipitation fluxes, respectively, at the surface. Shige et al. (2004 ; hereafter SH04) expanded on this technique, extracting precipitation column depth as well as convective–stratiform proportion information from spaceborne radar observations to further categorize a given vertical profile and then

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

techniques, including current remote sensing or in situ instruments, which explains why nearly all retrieval schemes depend heavily on some type of cloud-resolving model (CRM). However, apparent heating or Q 1 , of which LH is an important component, can be derived indirectly by measuring vertical profiles of temperature and the associated 3D wind fields from extensive rawinsonde networks through a residual method (known as a diagnostic heating budget; Yanai et al. 1973 ). The launch of the Tropical

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

Research Organisation (CSIRO) Southern Surveyor ship stationed in the Timor Sea. The sounding array covers an area with a radius of approximately 150 km. The variational analysis technique described above was applied to the sounding IOP over the domain enclosed by the five boundary sounding stations ( Fig. 1 ). In addition, two subanalysis domains were defined to better describe the convective systems of continental/coastal and island origin observed during the break period (respectively the mainland

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