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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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D. Argüeso, R. Romero, and V. Homar

precipitating systems and associated circulations that better resemble the observations. 2. Data and experiments a. Model description and experimental design We use the Weather Research and Forecasting (WRF) modeling system, version 3.9.1, to investigate the influence of spatial resolution and convective scheme on the realism of simulated precipitation in the MC ( Fig. 1 ). The model was forced with the latest generation reanalysis ERA5 ( Copernicus Climate Change Service 2017 ), which operates at a spatial

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