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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

predictability on the subseasonal time scale (e.g., Waliser 2011 ). With the advances in models and initialization techniques (e.g., Vitart 2014 ), marked improvements in the dynamical MJO predictions have been reported and now exceed the skill of empirical predictions ( Kim et al. 2018 ). For example, at the National Centers for Environmental Prediction (NCEP), Wang et al. (2014) found that the Climate Forecast System, version 2 (CFSv2), had useful MJO prediction skill out to 20 days and was

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

.5°–32.5°N, 110°–140°E). Forecast anomalies of the multimodels are calculated relative to each model’s hindcast climatology (1990–2012) at each lead time. Two statistical techniques are applied to verify deterministic prediction skill, the simple anomaly correlation coefficient (ACC) and the root-mean-square error (RMSE) score. Projection method and composite analyses are also conducted in this study. All the calculations are performed based on the time series of the observed and forecast anomalies

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Jieshun Zhu, Wanqiu Wang, and Arun Kumar

, based on the NCEP Climate Forecast System, version 1 (CFSv1), Seo and Wang (2010) performed a series of experiments to explore the impacts of various factors on the simulation of the MJO. They found that the simulation strongly depended on the convection parameterization, and the use of the relaxed Arakawa–Schubert (RAS) cumulus parameterization of Moorthi and Suarez (1999) produced a significantly better representation of the MJO with more realistic periodicity, spectral power, and eastward

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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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Ching-Shu Hung and Chung-Hsiung Sui

may help to generate the next MJO ( Matthews 2008 ; Zhao et al. 2013 ; D. Kim et al. 2014 ) and the model prediction skill of the MJO becomes better when the forecasts are initialized with a strong MJO compared to those initialized with a weak or nonexistent MJO ( H.-M. Kim et al. 2014 ). Therefore, the identification of individual attribution from current and previous events is crucial to advance understanding for the initiation and propagation mechanism of the MJO and to improve model

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

and December 2013 was chosen to compute OLR climatology. The OLR for December 2016 is obtained from NOAA Climate Data Record (CDR) of OLR version 1.2 ( Lee and NOAA CDR Program 2011 ), which is estimated from High-Resolution Infrared Radiation Sounder (HIRS) radiance observations with a 2-day lag. It is given daily with 1° × 1° horizontal resolution. The European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim, hereinafter ERA-Int; Dee et al. 2011 ) is utilized for zonal

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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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Giuseppe Torri, David K. Adams, Huiqun Wang, and Zhiming Kuang

; Higgins and Shi 2001 ; Bond and Vecchi 2003 ; Jones et al. 2004 ; Becker et al. 2011 ; Schreck et al. 2013 ; Thompson and Roundy 2013 ; Matsueda and Takaya 2015 ; Klotzbach et al. 2016 ; Zhou et al. 2016 ; Zheng et al. 2018 ; Tippett 2018 ; Barrett 2019 ), it is important to forecast the MJO accurately. Upon reaching the Maritime Continent, some MJO events weaken and do not propagate farther (e.g., Rui and Wang 1990 ; Salby and Hendon 1994 ; Zhang and Hendon 1997 ; Hsu and Lee 2005

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

sensitivities of the diurnally phase-locked gravity waves in the MC through semi-idealized cloud-permitting numerical model experiments. Realistic geography is imposed in these experiments, while the lateral boundary conditions are semi-idealized by repeating a diurnal composite calculated from a regime of interest, following the technique of several past studies ( Trier et al. 2010 ; Sun and Zhang 2012 ; Chen et al. 2016 ). We conduct a series of sensitivity tests modifying the islands or their existence

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Casey R. Densmore, Elizabeth R. Sanabia, and Bradford S. Barrett

-time Multivariate MJO (RMM) index phase space (RMM phases 4 and 5) from 1980 to 2017 for all months. b. Identifying the QBO The QBO was identified from 1980 to 2017 using an EOF analysis of daily stratospheric zonal wind anomalies, averaged meridionally from 10°S to 10°N and bounded vertically by 100 and 10 hPa. Zonal wind data from the ECMWF interim reanalysis (ERA-Interim; Dee et al. 2011 ; www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ) were smoothed temporally using a 151-day running

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