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Caren Marzban and Scott Sandgathe

1. Introduction It has become evident that the performance of numerical weather prediction (NWP) models must be assessed within a framework that acknowledges the existence of “objects” in the spatial field of observations and forecasts. Standard verification techniques ignore the spatial structure of forecast and observation fields and treat errors inappropriately. For example, a misplaced forecast’s contribution to the mean squared error is independent of the magnitude of the displacement. Or

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Daniel Hodyss

1. Introduction Numerical prediction through state estimation in the geophysical sciences strives for high resolution and longer cycling intervals and/or assimilation windows. High resolution, which presently refers to resolving the mesoscale, provides the ability to resolve those details of the circulation that are important to making a useful forecast, such as convection along frontal boundaries and the internal dynamics within a tropical cyclone. The cycling interval refers to the time

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Javier Amezcua, Eugenia Kalnay, and Paul D. Williams

model representing inertial oscillations of the simple harmonic type ( Williams 2009 ). In the present paper, the filter will be implemented and tested in the Simplified Parameterizations, Primitive Equation Dynamics (SPEEDY) model ( Molteni 2003 ), a relatively simple nonlinear atmospheric general circulation model (AGCM). Our objectives are to examine whether the use of the RAW filter changes either the climatology or the skill of weather forecasts, or both. For the first objective, we will

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Hui-Ling Chang, Huiling Yuan, and Pay-Liam Lin

system was developed by the Central Weather Bureau (CWB) in partnership with the National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Systems Division (NOAA/ESRL/GSD), for the purpose of improving the capability of short-range QPFs for severe weather systems. At present, major forecast centers pay more and more attention to advanced data assimilation schemes. The three-dimensional variational data assimilation (3DVAR) technique has been widely used in operational

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Paul J. Roebber

and Vallée 2002 ), N -day running mean bias removal (e.g., Cheng and Steenburgh 2007 ), and weighted MOS (e.g., Baars and Mass 2005 ). Other bias removal methods include incorporating observations made after the time of forecast issuance but prior to verification (e.g., Nipen et al. 2011 ; Huang et al. 2012 ), and the use of analogs ( Delle Monache et al. 2011 ). Evolutionary programs [EP; see Fogel (1999) for a historical overview of the technique] have been shown in recent studies to

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Luca Delle Monache, F. Anthony Eckel, Daran L. Rife, Badrinath Nagarajan, and Keith Searight

as AnEn requires only a single model forecast, as opposed to the multiple model runs of an NWP ensemble. Another advantage is that forecast uncertainty is based solely upon past observations, thereby eliminating the need to simulate all sources of NWP forecast uncertainty via sophisticated and computationally intensive techniques, and perhaps also avoiding the need for postprocessing calibration. The AnEn attempts to capture flow-dependent error growth by assigning the observed errors from

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Huw C. Davies

heat in a resting ambient atmosphere yields a net descent in the inner core of the initially warmed region with ascent above and below ( Fanelli and Bannon 2005 ). This contrast in the heating–ascent relationship is relevant in an NWP context. Longstanding challenges in NWP are to deliver reliable quantitative forecasting of precipitation, and to develop robust and effective techniques to assimilate/adjust model fields to account for misrepresented or unrepresented cloud diabatic heating. The

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Yanfeng Zhao, Donghai Wang, and Jianjun Xu

. 2015 ). Consequently, starting from the perspective of the better predictability of the large-scale circulation, an effort for extending the forecast lead time for precipitation, especially for heavy rains and PSR events, is worthwhile and reasonable to enhance disaster prevention and mitigation capabilities. For the large-scale circulation, spectral nudging (SN) is a scale-selective interior constraint technique ( von Storch et al. 2000 ) in a regional model. When the large-scale systems develop

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Yukiko Imada, Shinjiro Kanae, Masahide Kimoto, Masahiro Watanabe, and Masayoshi Ishii

advance notice of the tendency of the continuous seasonal rainfall at the beginning of August 2011, the flood damage could have been reduced. We based the statistical function on SVDA following the case of Taiwan in Chu et al. (2008) , but chose the predictor after taking full account of the climatic properties of Thailand and the skill of the CGCM forecast. The novelty of our forecasting system is that it is based on a fully coupled model including an ocean assimilation system (one-tier forecasting

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George H. Bryan, Jason C. Knievel, and Matthew D. Parker

’s relevance to squall-line properties that is based on a model other than that used by RKW88 and WR04 . Therefore, the main goal of our study is to assess whether RKW theory’s relevance to squall lines is supported by other numerical models. We base our new simulations on the recent study by WR04 , using the same simulation details and analysis techniques. We use three additional numerical models, all of which have been developed more recently than the model used by WR04 . Our findings provide

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