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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

significantly better than its previous version (CFSv1) with skillful predictions of 10–15 days ( Seo et al. 2009 ). Similar skills were also reported in the dynamical MJO predictions at other operational centers such as the Predictive Ocean Atmosphere Model for Australia (POAMA; Rashid et al. 2011 ), the European Centre for Medium-Range Weather Forecasts (ECMWF; Vitart et al. 2010 ; Vitart 2014 ), and Beijing Climate Center, China ( Liu et al. 2017 ). Vitart et al. (2017) and Lim et al. (2018

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Chen Li, Jing-Jia Luo, Shuanglin Li, Harry Hendon, Oscar Alves, and Craig MacLachlan

.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2 . 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2 Alves , O. , and D. Hudson , 2015 : Seasonal prediction: Towards ACCESS-S. Coupled modelling and prediction: From weather to climate—Abstracts of the Ninth CAWCR Workshop, CAWCR Tech. Rep. 080, p. 147. Alves , O. , and Coauthors , 2003 : POAMA: Bureau of Meteorology operational coupled model seasonal forecast system. Proc. National Drought Forum , Brisbane, Queensland, Australia, Bureau of Meteorology, 49–56. Barnston , A. G

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Jieshun Zhu, Wanqiu Wang, and Arun Kumar

, as well as SSTs, every 30 (60) minutes in CFSv2 (CFSv2L). Different from the standard configuration of CFSv2 ( Saha et al. 2014 ), which uses the 2007 version of the NCEP operational Global Forecast System (GFS), the atmospheric component in the two coupled systems used in this study (CFSv2 and CFSv2L) is the 2011 version of the NCEP GFS, but the model physics are configured as in Saha et al. (2014) . In this study, the following two convection schemes built into the model are used for

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Ming Feng, Yongliang Duan, Susan Wijffels, Je-Yuan Hsu, Chao Li, Huiwu Wang, Yang Yang, Hong Shen, Jianjun Liu, Chunlin Ning, and Weidong Yu

-10-745-2014 . 10.5194/os-10-745-2014 Kim , H. M. , P. J. Webster , V. E. Toma , and D. Kim , 2014 : Predictability and prediction skill of the MJO in two operational forecasting systems . J. Climate , 27 , 5364 – 5378 , https://doi.org/10.1175/JCLI-D-13-00480.1 . 10.1175/JCLI-D-13-00480.1 Lee , J. Y. , B. Wang , M. C. Wheeler , X. Fu , D. E. Waliser , and I. S. Kang , 2013 : Real-time multivariate indices for the boreal summer intraseasonal oscillation over the

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See Yee Lim, Charline Marzin, Prince Xavier, Chih-Pei Chang, and Bertrand Timbal

to affect the region around late October or early November ( Wongsaming and Exell 2011 ; Moten et al. 2014 ). Also, November is the second wettest month over the region after December (see section 2 and Fig. 2 ) and is a month when operational forecasters begin to monitor cold surges. The daily mean precipitation data are obtained from the TRMM 3B42 dataset with a resolution of 0.25°× 0.25°. The daily MSLP and 850-hPa winds are obtained from ERA-Interim (ERA-I) at 1° × 1° and 0.5° × 0

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Wan-Ling Tseng, Huang-Hsiung Hsu, Noel Keenlyside, Chiung-Wen June Chang, Ben-Jei Tsuang, Chia-Ying Tu, and Li-Chiang Jiang

. Kim , and M. I. Lee , 2017 : Why does the MJO detour the Maritime Continent during austral summer? Geophys. Res. Lett. , 44 , 2579 – 2587 , doi: 10.1002/2017GL072643 . 10.1002/2017GL072643 Kim , H.-M. , P. J. Webster , V. E. Toma , and D. Kim , 2014 : Predictability and prediction skill of the MJO in two operational forecasting systems . J. Climate , 27 , 5364 – 5378 , doi: 10.1175/JCLI-D-13-00480.1 . 10.1175/JCLI-D-13-00480.1 Klingaman , N. , and S. Woolnough , 2013

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Jian Ling, Yuqing Zhao, and Guiwan Chen

assimilation system . Quart. J. Roy. Meteor. Soc. , 137 , 553 – 597 , https://doi.org/10.1002/qj.828 . 10.1002/qj.828 Feng , J. , T. Li , and W. Zhu , 2015 : Propagating and nonpropagating MJO events over Maritime Continent . J. Climate , 28 , 8430 – 8449 , https://doi.org/10.1175/JCLI-D-15-0085.1 . 10.1175/JCLI-D-15-0085.1 Fu , X. , J. Y. Lee , B. Wang , W. Wang , and F. Vitart , 2013 : Intraseasonal forecasting of the Asian summer monsoon in four operational and research

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Chidong Zhang and Jian Ling

numerical models ( Inness and Slingo 2003 ; Kim et al. 2009 ; Seo et al. 2009 ), creating an MJO “prediction barrier” ( Weaver et al. 2011 ; Fu et al. 2013 ). For example, the fraction of MJO events that fail to propagate through the MC is 30% in a global reanalysis product but 50% in the ECMWF forecast system ( Vitart and Molteni 2010 ). The MJO prediction barrier would inevitably undermine the model capability of forecasting global influences of the MJO ( Hendon et al. 2000 ) and hinder the overall

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Xingwen Jiang, Jianchuan Shu, Xin Wang, Xiaomei Huang, and Qing Wu

National Natural Science Foundation of China (Grants 41661144019, 91337107, and 41375081), the operational prediction development program of CMA (CMAHX20160504), key project of basic applied research plan of Sichuan Province (2016JY0046), and the Basic Research and Operation Program of the CMA Institute of Plateau Meteorology (BROP 201514). REFERENCES Adler , R. F. , and Coauthors , 2003 : The version 2 Global Rainfall Climatology Project (GPCP) Monthly Precipitation Analysis (1979–present) . J

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

0400 and 0700 LST. Propagation behavior was explained in terms of the land–sea breeze. Hassim et al. (2016) and Vincent and Lane (2016a) examined the diurnal cycle of precipitation around New Guinea using the Weather Research and Forecasting (WRF) Model and satellite precipitation radar data. They found that precipitation associated with convective clouds propagated offshore at two distinct speeds. Within 100–200 km of the coast, precipitation propagated at with density currents associated

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