Search Results

You are looking at 1 - 10 of 33 items for :

  • Operational forecasting x
  • DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation x
  • All content x
Clear All
Jian Ling, Peter Bauer, Peter Bechtold, Anton Beljaars, Richard Forbes, Frederic Vitart, Marcela Ulate, and Chidong Zhang

. MJO initiation dates are marked above the horizontal lines to the left. Tracked amplitudes (amp) and propagation speed (spd) are given for each event (see section 2 ). (b) Corresponding time series of RMM index amplitude (curve) and phases (colors). The global measure is the all-season Real-time Multivariate MJO (RMM) index of Wheeler and Hendon (2004) . It has been used to assess the statistics of MJO forecast skill of operational and research models ( Lin et al. 2008 ; Gottschalck et al. 2010

Full access
Paul E. Ciesielski and Richard H. Johnson

flow blocking caused by the elevated terrain to its east and the diurnal heating of this large island. Because of the large spacing between the sounding sites in the core DYNAMO array, local island effects were aliased onto larger scales impacting analyzed fields and atmospheric budgets. To mitigate these local effects on the large-scale analyses, low-level European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses (OA) were used in a 2.5° radius around Sri Lanka to estimate

Restricted access
Jun-Ichi Yano and Joseph J. Tribbia

that the same could be true for the whole tropical planetary-scale circulations. Such a new perspective may have an immediate impact on global model initialization strategy over the tropics: the current basic strategy is an initialization based on a linear equatorial wave decomposition (cf. Žagar et al. 2005 ). The proposed MJO–modon theory suggests a nonlinear balance initialization ( Baer and Tribbia 1977 ; Kasahara 1982 ; Tribbia 1984b ) as the key for a successful MJO forecast. From a

Open access
Xiouhua Fu, Wanqiu Wang, June-Yi Lee, Bin Wang, Kazuyoshi Kikuchi, Jingwei Xu, Juan Li, and Scott Weaver

.1175/2011MWR3584.1 . Fu , X. , J.-Y. Lee , B. Wang , W. Q. Wang , and F. Vitart , 2013a : Intra-seasonal forecasting of the Asian summer monsoon in four operational and research models . J. Climate , 26 , 4186 – 4203 , doi: 10.1175/JCLI-D-12-00252.1 . Fu , X. , J.-Y. Lee , P.-C. Hsu , H. Taniguchi , B. Wang , W. Q. Wang , and S. Weaver , 2013b : Multi-model MJO forecasting during DYNAMO/CINDY period . Climate Dyn. , 41 , 1067 – 1081 , doi: 10.1007/s00382

Full access
Yue Ying and Fuqing Zhang

predictability of the tropical weather systems and CCEWs. The first ensemble simulation designed to examine the practical predictability limits starts from 18 October to 2 November (corresponding to the MJO phases 1–3). The IC and LBC ensemble perturbations are sampled from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) global ensemble forecasts archived in The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE). 1 The TIGGE

Full access
Tomoe Nasuno, Tim Li, and Kazuyoshi Kikuchi

, representation of the MJO in numerical models (even state-of-the-art operational models) is not satisfactory ( Hung et al. 2013 ; Zhang et al. 2013 ). In particular, the accurate simulation and forecast of convective initiation of the MJO is a difficult task ( Seo et al. 2009 ; Gottschalck et al. 2010 ). A number of studies have addressed the idea that moisture accumulation is the key to initiation and control of the MJO ( Bladé and Hartmann 1993 ; Kemball-Cook and Weare 2001 ; Maloney et al. 2010

Full access
Kai-Chih Tseng, Chung-Hsiung Sui, and Tim Li

in this study. In this study, we calculate a diagnostic moisture budget in the DYNAMO/CINDY sounding array in the Indian Ocean (IO). Instead of using reanalysis data, we use operational data from the European Centre for Medium-Range Weather Forecasts, which are assimilated with field observations and satellite data during the DYNAMO/CINDY IOP. In section 2 , we describe the data and method utilized in this study. In section 3 , the MJO evolutions from October to December 2011 are discussed

Full access
Hungjui Yu, Paul E. Ciesielski, Junhong Wang, Hung-Chi Kuo, Holger Vömel, and Ruud Dirksen

station metadata regarding if and when sites were upgraded to this new software, its impact on radiosonde climate records remains unknown, and moreover, it cannot be adapted and improved by users for their data ( Wang et al. 2013 ). Thus, an important first step is a thorough evaluation of the data produced by this new algorithm. To address the shortcomings of the global operational networks for climate studies and to ensure that future climate records are more useful than the records to date, the

Full access
Hyodae Seo, Aneesh C. Subramanian, Arthur J. Miller, and Nicholas R. Cavanaugh

, P.-C. Hsu , H. Taniguchi , B. Wang , W. Wang , and S. Weaver , 2013 : Multi-model MJO forecasting during DYNAMO/CINDY period . Climate Dyn. , 41 , 1067 – 1081 , doi: 10.1007/s00382-013-1859-9 . Gottschalck , J. , and Coauthors , 2010 : A framework for assessing operational Madden–Julian oscillation forecasts: A CLIVAR MJO working group project . Bull. Amer. Meteor. Soc. , 91 , 1247 – 1258 , doi: 10.1175/2010BAMS2816.1 . Gottschalck , J. , P. E. Roundy , C. J

Full access
Kunio Yoneyama, Chidong Zhang, and Charles N. Long

were provided by the DYNAMO Project Office at the NCAR Earth Observing Laboratory (EOL). EOL maintained the field catalog on the Internet. It includes all necessary information for the field operation and in-field data analysis, such as field reports from all observation sites that summarized instrument conditions, status of data collection and transmission, operational products such as satellite images and numerical forecasts, preliminary data analysis, and update of the operational schedule

Full access