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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

this paper our focus is on the illustration of the approach that can be used to quantify predictive skill associated with the MJO. 2. Data and analysis procedure The observational data analyzed in this study include OLR from the NOAA Advanced Very High Resolution Radiometer ( Liebmann and Smith 1996 ), U850 and U200, and 2-m temperature (T2m) from the Climate Forecast System Reanalysis ( Saha et al. 2010 ), and rainfall estimate from the Climate Prediction Center morphing technique (CMORPH) based

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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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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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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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