Search Results

You are looking at 1 - 10 of 19 items for :

  • Years of the Maritime Continent x
  • All content x
Clear All
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

Restricted access
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

Restricted access
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

Restricted access
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

Restricted access
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

Full access
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

Full access
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

Full access
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

Open access
D. Argüeso, R. Romero, and V. Homar

available for convection is provided. These two aspects are interesting directions to continue identifying the dominant factors that may help simulate realistic tropical convective precipitation. Acknowledgments This work was supported by the REHIPRE project. REHIPRE is funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Actions Individual Fellowship Grant Agreement 743547. This study was also supported by the COASTEPS project (CGL2017-82868-R

Open access
Wei-Ting Chen, Shih-Pei Hsu, Yuan-Huai Tsai, and Chung-Hsiung Sui

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

Full access