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Wansuo Duan and Zhenhua Huo

mean of the forecasting members is often regarded as the result of a deterministic forecast. The ensemble mean may filter the unpredictable parts and leave the common parts of the forecasting members, ultimately decreasing the uncertainties of single forecast results ( Leith 1974 ; Leutbecher and Palmer 2008 ). With its benefit of producing probabilistic distribution information of forecast results, ensemble forecasting has become a major technique in numerical weather and climate forecasting

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Jie Feng, Ruiqiang Ding, Deqiang Liu, and Jianping Li

1998 ; Zhu et al. 2002 ). With this additional information, the quality of forecasts can be significantly enhanced. Initially, the use of ensemble techniques focused on random samples (Monte Carlo forecasting) as a description of the probability distribution of initial states ( Epstein 1969 ; Leith 1974 ). However, the atmosphere is an extremely complex system, which has a very high phase-space dimension; that is, the number of random samples must be sufficiently large. Consequently, the cost of

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Richard Kleeman

systems, then apparently many random variables of interest are effectively large sums of other random variables and so by the central limit theorem approximately Gaussian. 3. Initial conditions probability distribution Errors arise in the initial conditions of weather predictions for two principal reasons—first because the observational networks that determine the current state of the atmosphere are less than perfect, and second because the models utilized to make forecasts have misrepresentations of

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William Miller, Hua Chen, and Da-Lin Zhang

, during, and after RI have been successfully reproduced with a 72-h (0000 UTC 18 October–0000 UTC 21 October 2005) prediction using the Weather Research and Forecasting (WRF) Model with a quadruply nested (27, 9, 3, and 1 km) grid and initial and lateral boundary conditions that are identical to the Geophysical Fluid Dynamics Laboratory’s then-operational data. Then, Zhang and Chen (2012 , hereafter ZC12 ) used the hydrostatic equation to demonstrate how the warming above the 380-K isentrope in

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Shao-Fan Chang, Yu-Chieng Liou, Juanzhen Sun, and Sheng-Lun Tai

.g., Snyder and Zhang 2003 ; Tong and Xue 2005 ; Jung et al. 2008a , b ; Zhang et al. 2009 ; Tsai et al. 2014 ). A distinguishing feature of the EnKF is its capability to provide flow-dependent background error covariance using ensemble forecast statistics. Recently, active efforts have been made to develop hybrid techniques by combining the EnKF with the variational approaches for the assimilation of radar data (e.g., Li et al. 2012 ; Pan et al. 2012 ; Gao and Stensrud 2014 ). Compared to 3DVar

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Timothy DelSole and Michael K. Tippett

structure. Such filters would allow predictable structures to be diagnosed even if they are not persistent and even if they vary on time scales shorter than typical averaging windows. Filters based on optimal projection vectors are a common tool in signal processing theory and are used extensively in climate and seasonal predictability under the names optimal fingerprinting and signal-to-noise empirical orthogonal functions (EOFs). These techniques are now recognized to be generalizations of linear

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Daniel P. Stern and Fuqing Zhang

approximately 1200 UTC on 29 August to 0000 UTC on 3 September. For each flight, the P-3 flew two to four radial legs through the storm center, and for each leg, NOAA’s Hurricane Research Division (HRD) performed a three-dimensional analysis of the Cartesian horizontal ( u and υ ) and vertical ( w ) velocities, using the automated technique of Gamache et al. (2004) . We acquired these analyses, averaged all legs from each flight to maximize azimuthal coverage, and calculated the tangential winds ( υ t

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Xiouhua Fu, Bin Wang, Duane E. Waliser, and Li Tao

–western Pacific region is also one of the most vulnerable areas around the world to the impacts of climate-related natural disasters. About 80% of these natural disasters are caused by extreme hydrometeorological events (e.g., flood, drought, etc.; IFRC 2000 ). These extreme events are also modulated by intraseasonal variability ( Goswami and Ajayamohan 2001 ; Goswami et al. 2003 ; Jones et al. 2004 ). If we could provide useful forecast of this intraseasonal variability with lead times of several weeks

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Rebecca E. Morss, Chris Snyder, and Richard Rotunno

1. Introduction Despite decades of research, the predictability behavior of the atmosphere at meso- and synoptic scales is not well understood. Understanding atmospheric predictability is important from theoretical, practical, and societal perspectives. As a fundamental question, atmospheric predictability is related to key aspects of atmospheric dynamics and underlies many elements of modern weather prediction. From a practical perspective of improving weather forecasts, understanding error

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E. Tyrlis and B. J. Hoskins

1. Introduction A comprehensive climatology of Northern Hemisphere (NH) blocking according to the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data has been presented in Tyrlis and Hoskins (2008 , hereafter TH ). This study was based on the use of an index, B , of the meridional contrast about a central blocking latitude (CBL) of the potential temperature ( θ ) on a potential vorticity (PV) surface considered to be the dynamical tropopause, the 2-PVU

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