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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

surface temperature, which are of importance in the context of decision making. Empirical forecast tools have been developed that exploit this link and utilize MJO information for predictions ( Zhou et al. 2012 ; Riddle et al. 2013 ; Johnson et al. 2014 ). In the last decade, advances have been made in the prediction of MJO using dynamical models (e.g., Vitart 2017 ). These are due to improvements in the observations and data assimilation systems, improvements in the physical parameterization

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Andung Bayu Sekaranom and Hirohiko Masunaga

–Julian oscillation (MJO) is a factor that generates the extreme events. The MJO influence was also identified by Tangang et al. (2008) in peninsular Malaysia during flood events in December 2006 to January 2007, where an active phase of the Indian Ocean dipole was also viewed as another factor responsible for the extremes. However, because of a lack of observations, the general mechanism for the extreme precipitation dynamics over the MC is still not fully understood. Substantial amounts of precipitation data

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D. Argüeso, R. Romero, and V. Homar

formulations (i.e., physical parameterizations, dynamical core). For example, simulated precipitation usually peaks too early in the day compared to observations, especially at lower resolutions, and regional models tend to produce too much precipitation over land and too little over the ocean ( Gianotti et al. 2012 ; Kwan et al. 2013 ; Birch et al. 2016 ; Hassim et al. 2016 ; Vincent and Lane 2017 ; Im and Elthair 2018 ). Previous studies ( Love et al. 2011 ; Birch et al. 2015 ; Bhatt et al. 2016

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Jieshun Zhu, Wanqiu Wang, and Arun Kumar

degree of air–sea coupling. For question (ii), we investigate physical processes contributing to SST evolutions in observations and two CFSv2 simulations with contrasting levels of fidelity in simulating the MJO eastward propagation, which results from different convection schemes. The paper is organized as follows: The model, the experimental design, and the datasets are described in the section 2 . The results are presented in section 3 , where factors influencing MJO propagation simulations are

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Claire L. Vincent and Todd P. Lane

the complex topography and the absence of in situ measurements over the sea. Satellite precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM) 3B42 V7 ( Huffman et al. 2007 ; Goddard Space Flight Center 1998 ) and the Climate Prediction Center morphing technique (CMORPH; Joyce et al. 2004 ; Climate Prediction Center 2011 ) were used in the study. TRMM estimates are derived from passive microwave sensor observations from polar-orbiting satellites, together with brightness

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Chidong Zhang and Jian Ling

from that over the open water of the Indian and Pacific Oceans. In observations, when the MJO propagates over the MC, it often weakens, its propagation speed becomes uneven, and it may completely break down and fail to reemerge on the Pacific side ( Rui and Wang 1990 ; Hendon and Salby 1994 ; Hsu and Lee 2005 ; Kim et al. 2014 ). The weakening and blocking of the MJO by the MC is known as a “barrier effect” on MJO propagation. This barrier effect of the MC in nature is often exaggerated in

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Wei-Ting Chen, Shih-Pei Hsu, Yuan-Huai Tsai, and Chung-Hsiung Sui

2015. We apply the Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates (version 1.0 CRT; Joyce et al. 2004 ; Xie et al. 2017 ) as rainfall data. It is derived by combining satellite infrared and microwave sounders, with calibration against surface gauge observations. The temporal and spatial resolution of CMORPH data used in the present study is 3 hourly and 0.25° × 0.25° in the latitude–longitude grid. The period for computing rainfall climatology is

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Ewan Short, Claire L. Vincent, and Todd P. Lane

offshore behavior of the Maritime Continent’s diurnal wind cycles using satellite scatterometer observations. So far, detailed studies of the Maritime Continent’s diurnal wind cycles have relied on mesoscale models like WRF, as widespread in situ observations are generally lacking over the region’s seas. However, the growing record of satellite scatterometer data provides a new observational dataset that can be used for this purpose. This study presents a new method of utilizing these datasets by

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