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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

surface temperature, which are of importance in the context of decision making. Empirical forecast tools have been developed that exploit this link and utilize MJO information for predictions ( Zhou et al. 2012 ; Riddle et al. 2013 ; Johnson et al. 2014 ). In the last decade, advances have been made in the prediction of MJO using dynamical models (e.g., Vitart 2017 ). These are due to improvements in the observations and data assimilation systems, improvements in the physical parameterization

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

seesaw. Seasonal prediction skill of ENSO has been significantly improved over the past 2–3 decades through the development of coupled ocean–atmosphere general circulation models (OAGCMs) (e.g., Cane et al. 1986 ; Barnston et al. 1999 ; Jin et al. 2008 ; Luo et al. 2008 , 2015 ; Graham et al. 2011 ; Cottrill et al. 2013 ; MacLachlan et al. 2015 ). While the forecast skill of ENSO varies with target seasons, ENSO phases, and ENSO strength (e.g., Jin et al. 2008 ), ENSO can be generally

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Joshua Chun Kwang Lee, Anurag Dipankar, and Xiang-Yu Huang

data assimilation configurations to gain insights on their weaknesses. Faccani et al. (2009) and Sun et al. (2020) assessed the impact of assimilating additional radiosonde data on forecasts over western Africa and Antarctica, respectively. However, despite the availability of data from recent intensive observation projects over the western Maritime Continent, such case studies in this region are still absent. Here, we perform a case study over western the Maritime Continent, building on

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Daehyun Kang, Daehyun Kim, Min-Seop Ahn, and Soon-Il An

method a. Dataset We use the National Oceanic and Atmospheric Administration (NOAA) daily interpolated outgoing longwave radiation (OLR) product ( Liebmann and Smith 1996 ) as a proxy for tropical convection. Various atmospheric state variables are obtained from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) product ( Hersbach et al. 2019 ). The OLR and ERA5 data are obtained for the period 1979–2018 and interpolated onto a 2.5° longitude × 2

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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

convection makes weather forecasting in this area a challenging task ( Love et al. 2011 ; Birch et al. 2016 ; Johnson et al. 2016 ; Baranowski et al. 2019 ) and limits the predictability of heavy rain events. The climate of southwest Sulawesi (Gowa, Makassar, Maros, Jeneponto, and Takalar districts) is monsoonal with two main seasons: a wet season between November and April, with prevailing westerly winds, and a dry season throughout the rest of the year. Precipitation over the MC region has a

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Yuntao Wei and Zhaoxia Pu

Forecasting (WRF) Model to analyze the complex interactions among moisture, cloud, radiation, and convection. The rest of the paper is arranged as follows. Section 2 reviews the global evolution of this BSISO event. Section 3 introduces the numerical model, simulations, and diagnostic strategy. Section 4 validates the model simulation. The moisture variation and related cloud–radiation effects are examined in sections 5 and 6 , respectively. A discussion of the results and a summary of the main

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

-Pacific summer climate . Meteor. Atmos. Phys. , 113 , 171 – 180 , doi: 10.1007/s00703-011-0146-8 . 10.1007/s00703-011-0146-8 Jiang , X. , S. Yang , J. Li , Y. Li , H. Hu , and Y. Lian , 2013 : Variability of the Indian Ocean SST and its possible impact on summer western North Pacific anticyclone in the NCEP Climate Forecast System . Climate Dyn. , 41 , 2199 – 2212 , doi: 10.1007/s00382-013-1934-2 . 10.1007/s00382-013-1934-2 Jiang , X. , Y. Li , S. Yang , J. Shu , and G. He , 2015

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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

1. Introduction There has been growing interest in forecasts at subseasonal time scales (i.e., 3–4 weeks; National Research Council 2010 ; National Academies of Sciences, Engineering, and Medicine 2016 ), which fills the gap between medium-range weather forecast and seasonal prediction. The Madden–Julian oscillation (MJO; Madden and Julian 1971 ), the primary mode of tropical intraseasonal climate variability in the boreal winter and spring, is considered to be a major source of global

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