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Beata Latos, Thierry Lefort, Maria K. Flatau, Piotr J. Flatau, Donaldi S. Permana, Dariusz B. Baranowski, Jaka A. I. Paski, Erwin Makmur, Eko Sulystyo, Philippe Peyrillé, Zhe Feng, Adrian J. Matthews, and Jerome M. Schmidt

dataset (TRMM 3B42 v7) were utilized at their native spatial resolution of 0.25° for the same period as the OLR data. Additionally, Integrated Multisatellite Retrievals for Global Precipitation Mission (IMERG) Final Precipitation L3 V06 products ( Huffman et al. 2019 ) on a 0.1° × 0.1° latitude–longitude grid were used, daily and half-hourly for the 19–24 January 2019 period and half-hourly for 1 October 2013–30 June 2019. b. Reanalysis data European Centre for Medium-Range Weather Forecasts (ECMWF

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Marvin Xiang Ce Seow, Yushi Morioka, and Tomoki Tozuka

Temperature (OISST), version 2, dataset. It is based on the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) infrared satellite data from 1982 to 2005 and operational AVHRR data from 2006 onward ( Reynolds et al. 2002 ). The atmospheric data of 850-hPa zonal and meridional winds and surface heat fluxes are from the ERA-Interim reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF). They have a T255 spectral resolution and 60 vertical levels from the surface to 0

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Lei Zhou, Ruomei Ruan, and Raghu Murtugudde

. Data and methods Atmospheric variables, such as wind velocities and specific humidity, are obtained from ERA5 ( Copernicus Climate Change Service 2017 ) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The outgoing longwave radiation (OLR) is from the NOAA satellite data ( Liebmann and Smith 1996 ). All data are from 1982 to 2019 and the intraseasonal variabilities are obtained with a 20–100-day bandpass Butterworth filter. Two other reanalysis products from ERA-40

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Ya Yang, Xiang Li, Jing Wang, and Dongliang Yuan

European Centre for Medium-Range Weather Forecasts (ECMWF). A hindcast run was forced with the ECMWF wind stress and heat flux between 1990 and 2001. More detailed description of model configuration can be found in Wang and Yuan (2015) . We extracted equatorial waves by decomposing the results of the hindcast experiment. The wave decomposition method has been explained in Yuan et al. (2004) . The three-dimensional pressure and zonal velocity of the OGCM were projected onto the baroclinic vertical

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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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Yuntao Wei and Zhaoxia Pu

findings are presented in section 7 . 2. Characterization of the BSISO event We first use satellite and reanalysis data products to characterize a BSISO event during July 2017. The satellite precipitation product from the Climate Prediction Center morphing technique (CMORPH) ( Joyce et al. 2004 ) data is available at a horizontal resolution of 8 km and a time step of 30 min. Hourly, 0.25° horizontal resolution analysis data at pressure levels from the fifth generation of the European Centre for Medium

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Lei Song and Renguang Wu

1. Introduction East Asia is subject to frequent cold events during boreal winter. The long-lasting cold event in January and early February 2008 caused large economic and life losses in eastern China ( Zhou et al. 2009 ; Wen et al. 2009 ). In December 2009, several cold events occurred in the United States, Europe, and East Asia, bringing grave damage to these regions ( Wang and Chen 2010 ). A strong cold event struck East Asia in January 2016, causing snowfall and frigid weather in many

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Claire L. Vincent and Todd P. Lane

of 10%–30% over continental Europe relative to TRMM, and in particular less precipitation over the European Alps than TRMM. Vincent and Lane (2016a) compared TRMM and CMORPH estimates to rain gauges in New Guinea during a 1-month period and found a systematic overprediction in most locations for CMORPH, with many sites exceeding 10 mm day −1 and smaller positive and negative biases of less than about 5 mm day −1 for TRMM. That these results are inconsistent with those of Skok et al. (2016

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

the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ) for the period 1982 to August 2002, and to the global analyses from BoM’s numerical weather prediction system thereafter (e.g., Hudson et al. 2011 ). Initial conditions for the ocean are obtained from the POAMA Ensemble Ocean Data Assimilation System (PEODAS), which assimilates available ocean observations using an ensemble Kalman filter (e.g., Yin et al. 2011 ). 2) The SINTEX-F model

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Kevin E. Trenberth and Yongxin Zhang

Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Edition 4.0 radiation on 1° × 1° grids, from Langley Atmospheric Science Data Center ( http://ceres.larc.nasa.gov/order_data.php ) ( Loeb et al. 2009 ). Observations from CERES begin in March 2000 and have been extended back in time ( Allan et al. 2014 ) using model results and other constraints, so that we use results from January 2000 on. The atmospheric computations here all utilize only the European Centre for Medium

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