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Yan Zhu, Tim Li, Ming Zhao, and Tomoe Nasuno

humidity ( q ) from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) ( Dee et al. 2011 ). All data above are daily data with horizontal resolution of 2.5° latitude × 2.5° longitude for 1984–2005. In this study, we focus on the boreal winter season (November–April). b. Methodology For the data analysis, we first removed the climatological annual cycle. Then a Lanczos bandpass filter ( Duchon 1979 ) was used to derive the HFW component ( A ′; <20 days), MJO

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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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Anurag Dipankar, Stuart Webster, Xiang-Yu Huang, and Van Quang Doan

region running regional models for weather prediction using input conditions from the big centers like the European Centre for Medium-Range Weather Forecasts (ECMWF,) the Met Office (United Kingdom), and the National Oceanic and Atmospheric Administration (United States). A novelty of the current study is that it utilizes results from a convection-permitting state-of-the-art NWP model to highlight the biases in the input conditions from the high-resolution (9 km) deterministic forecast from ECMWF

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Chen Li, Jing-Jia Luo, and Shuanglin Li

multidecadal fluctuation of the CEF seesaw, and the possible role of the Indian Ocean dipole (IOD) in the CEF seesaw are discussed in section 5 . A summary is given in section 6 . 2. Observational data and model experiments a. Observational data and methods Monthly mean horizontal and vertical winds at multiple pressure levels, sea level pressure (SLP), precipitation, and SST data for the period 1900–2010 are derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) twentieth

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James H. Ruppert Jr., Xingchao Chen, and Fuqing Zhang

is applied to all meteorological input variables. The European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) is invoked for these boundary conditions ( Dee et al. 2011 ). All of the simulations of the study are summarized in Table 1 . A control simulation (CTL) is conducted on the domain shown in Fig. 1 using the easterly regime diurnal composite repeated in time for 30 days to assess its evolution. The simulation reaches a statistically steady state in <5

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Jian Ling, Yuqing Zhao, and Guiwan Chen

resolution of 0.25° × 0.25° is used to identify individual MJO events in the observation. Other variables covering the same period are three-dimensional wind fields, air temperature, specific humidity (0.75° × 0.75°) provided by European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-I) ( Dee et al. 2011 ) and NOAA Optimum Interpolation (OI) High Resolution Sea Surface Temperature, version 2 (OISSTv2; Reynolds et al. 2007 ), with a horizontal resolution of 0.25° × 0.25° provided by

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Ching-Shu Hung and Chung-Hsiung Sui

section 4 . 2. Data and methods a. Data The datasets used in this study include 1) the interpolated daily outgoing longwave radiation (OLR) obtained from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites ( Liebmann and Smith 1996 ) and 2) the ERA-Interim data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The abovementioned data are available from the website of the NOAA/OAR/ESRL PSD (Boulder, Colorado; https

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James H. Ruppert Jr. and Fuqing Zhang

( Gottschalck et al. 2013 ; Johnson and Ciesielski 2013 ). This study invokes the large-scale convection-permitting regional model simulation described by Wang et al. (2015) , which successfully reproduced the October and November DYNAMO MJO events. The simulation was conducted using the Weather Research and Forecasting (WRF) Model, version 3.4.1 ( Skamarock et al. 2008 ), with the domain shown in Fig. 1 , using the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA

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

Observation Portal , 2018b : MetOp (Meteorological Operational Satellite Program of Europe). Accessed 2 November 2018, https://directory.eoportal.org/web/eoportal/satellite-missions/m/metop . Gille , S. T. , S. G. Llewellyn Smith , and S. M. Lee , 2003 : Measuring the sea breeze from QuikSCAT scatterometry . Geophys. Res. Lett. , 30 , 1114 , https://doi.org/10.1029/2002GL016230 . 10.1029/2002GL016230 Gille , S. T. , S. G. Llewellyn Smith , and N. Statom , 2005 : Global observations

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