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method (VAM) to combine wind measurements from moored buoys and several microwave radiometers and scatterometers with reanalysis data from the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) database. From this, global 0.25° gridded 6-hourly wind vector estimates are produced. By combining rainfall information from TRMM and GPM with wind estimates from CCMP, a detailed picture of variability between BSISO phases can be established. An understanding of intraseasonal
method (VAM) to combine wind measurements from moored buoys and several microwave radiometers and scatterometers with reanalysis data from the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) database. From this, global 0.25° gridded 6-hourly wind vector estimates are produced. By combining rainfall information from TRMM and GPM with wind estimates from CCMP, a detailed picture of variability between BSISO phases can be established. An understanding of intraseasonal
). RMM1 and RMM2 are used to trace MJO propagation in the Indian Ocean and are appropriate for use in this study. Evaporation and 10-m winds are provided from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim global atmospheric reanalysis, available daily at 1° spatial resolution from 1979 to present ( http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ ; Berrisford et al. 2011 ). ERA-Interim is an improvement of the previous reanalysis ERA-40 as it better
). RMM1 and RMM2 are used to trace MJO propagation in the Indian Ocean and are appropriate for use in this study. Evaporation and 10-m winds are provided from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim global atmospheric reanalysis, available daily at 1° spatial resolution from 1979 to present ( http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ ; Berrisford et al. 2011 ). ERA-Interim is an improvement of the previous reanalysis ERA-40 as it better
influence weather and climate over remote places including East Asia ( Chang and Lau 1982 ; Lau and Chang 1987 ), North America ( Yanai and Tomita 1998 ; Yang et al. 2002 ; Chan and Li 2004 ), and Europe ( Neale and Slingo 2003 ). Convection over the SCS–MC region exhibits significant multiscale variability, which remains a great challenge to the global atmospheric models, owing to the difficulties in representing convective processes in the tropical environment with contrasting land–ocean difference
influence weather and climate over remote places including East Asia ( Chang and Lau 1982 ; Lau and Chang 1987 ), North America ( Yanai and Tomita 1998 ; Yang et al. 2002 ; Chan and Li 2004 ), and Europe ( Neale and Slingo 2003 ). Convection over the SCS–MC region exhibits significant multiscale variability, which remains a great challenge to the global atmospheric models, owing to the difficulties in representing convective processes in the tropical environment with contrasting land–ocean difference
numerics of the MIT GCM. Proc. ECMWF Seminar Series on Numerical Methods: Recent Developments in Numerical Methods for Atmosphere and Ocean Modelling , Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 139–149 , https://www.ecmwf.int/node/7642 . Bellenger , H. , and J.-P. Duvel , 2009 : An analysis of tropical ocean diurnal warm layers . J. Climate , 22 , 3629 – 3646 , https://doi.org/10.1175/2008JCLI2598.1 . 10.1175/2008JCLI2598.1 Bogdanoff , A. S. , 2017
numerics of the MIT GCM. Proc. ECMWF Seminar Series on Numerical Methods: Recent Developments in Numerical Methods for Atmosphere and Ocean Modelling , Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 139–149 , https://www.ecmwf.int/node/7642 . Bellenger , H. , and J.-P. Duvel , 2009 : An analysis of tropical ocean diurnal warm layers . J. Climate , 22 , 3629 – 3646 , https://doi.org/10.1175/2008JCLI2598.1 . 10.1175/2008JCLI2598.1 Bogdanoff , A. S. , 2017
such as CMORPH. For this study, CMORPH precipitation estimates are considered for boreal summers (May–October) from 1998 to 2018. Since the analysis period includes several strong El Niño events, the analysis of this paper was repeated by excluding the summers in which a strong El Niño event was decaying (1998, 2010, and 2016), and the conclusions were found to be unchanged. The fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) [ Dee et al. 2011
such as CMORPH. For this study, CMORPH precipitation estimates are considered for boreal summers (May–October) from 1998 to 2018. Since the analysis period includes several strong El Niño events, the analysis of this paper was repeated by excluding the summers in which a strong El Niño event was decaying (1998, 2010, and 2016), and the conclusions were found to be unchanged. The fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) [ Dee et al. 2011
every day for the years 1998–2012, with prescribed NOAA Optimum Interpolation (OI) weekly SSTs and sea ice ( Reynolds et al. 2002 ). Initial atmospheric state variables (horizontal velocities, temperature, specific humidity, and surface pressure) are from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, hereinafter ERA-Int; Dee et al. 2011 ). The initialization procedure is described in Ma et al. (2015) . Based on the results in Ma et al. (2013
every day for the years 1998–2012, with prescribed NOAA Optimum Interpolation (OI) weekly SSTs and sea ice ( Reynolds et al. 2002 ). Initial atmospheric state variables (horizontal velocities, temperature, specific humidity, and surface pressure) are from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, hereinafter ERA-Int; Dee et al. 2011 ). The initialization procedure is described in Ma et al. (2015) . Based on the results in Ma et al. (2013
terrain height to study the effects of topography on precipitation (e.g., Barthlott and Kirshbaum 2013 ; Rasmussen and Houze 2016 ; Zhu et al. 2017 ). All simulations used observed weekly averaged NOAA optimum interpolation sea surface temperatures (OI SSTs; Reynolds et al. 2002 ). Each simulation is initiated with horizontal winds, temperature, pressure (Exner function), water vapor mixing ratio, and soil moisture and temperature from the fifth generation of the European Centre for Medium
terrain height to study the effects of topography on precipitation (e.g., Barthlott and Kirshbaum 2013 ; Rasmussen and Houze 2016 ; Zhu et al. 2017 ). All simulations used observed weekly averaged NOAA optimum interpolation sea surface temperatures (OI SSTs; Reynolds et al. 2002 ). Each simulation is initiated with horizontal winds, temperature, pressure (Exner function), water vapor mixing ratio, and soil moisture and temperature from the fifth generation of the European Centre for Medium
